Whole-flow quality monitoring method and device in optical lens production process

Abstract

The invention relates to the technical field of quality control, and discloses a whole-flow quality monitoring method and device in an optical lens production process, and the method comprises the steps: firstly obtaining a first deformation parameter set and a second deformation parameter set, obtaining a flow forward feature vector and a flow vector matrix through processing, and building a flow field grading rule according to the flow forward feature vector and the flow vector matrix; a historical production data set is called to construct a flow-process mapping model to obtain a process control strategy, meanwhile, melt related parameters are obtained, three-dimensional flow field distribution is obtained, and multi-physics field coupling simulation is carried out; and finally, different process optimization strategies are triggered according to simulation results and grading conditions. According to the method, accurate monitoring of the whole production process of the optical lens is achieved, and the product quality and the production efficiency are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quality control, and more specifically, to a full-process quality monitoring method and device in the production process of optical lenses. Background Art

[0002] In the production process of optical lenses, quality control is a crucial link. With the continuous development of optical technology, the performance requirements for optical lenses are getting higher and higher, such as high refractive index, aspherical shape, etc., which pose higher challenges to quality monitoring in the production process.

[0003] Chinese Patent with the authorization announcement number CN117974719B discloses a processing tracking and detection method for optical lenses, which can track the processing process by integrating machine vision and deep neural network, and can detect processing anomalies in a timely manner. However, this method mainly focuses on the tracking and anomaly detection of the processing process, and pays less attention to the reconstruction of the flow field of the molten material in the mold cavity during the injection molding stage. In the injection molding of optical lenses, the flow state of the material directly affects the product quality, such as uneven refractive index distribution. This method cannot solve the problem of real-time reconstruction of the flow field of the molten material in the mold cavity during the injection molding process, and cannot provide an effective basis for optimizing the injection molding process.

[0004] Chinese Patent with the authorization announcement number CN119047695B provides a lens production quality management system based on optical characteristics, which can obtain various data in the lens production process and perform steady-state evaluation and index output. However, this system has deficiencies in real-time monitoring and controlling the flow state of the molten material in the mold cavity. For the injection molding process of high refractive index aspherical lenses, traditional pressure sensors can only obtain single-point data and cannot reconstruct the three-dimensional flow field, resulting in a problem that the uneven refractive index distribution rate is as high as 3.5%. This system cannot effectively solve this problem.

[0005] The prior art cannot obtain comprehensive flow field information in the quality monitoring of the optical lens production process, it is difficult to accurately judge the flow state of the material, lacks fine classification of different flow anomaly patterns and personalized parameter optimization control, and cannot achieve precise control of the molding process. Summary of the Invention

[0006] To overcome the above-mentioned defects of the prior art, the present invention provides a full-process quality monitoring method and device in the production process of optical lenses, aiming to solve the problem of real-time reconstruction of the flow field of molten materials in the mold cavity during the injection molding stage, and improve the production quality and efficiency of optical lenses. By embedding a distributed fiber Bragg grating array and an ultrasonic guided wave emitter on the mold surface, combined with a multi-physics field coupling model, it is possible to obtain and analyze the physical field information in the mold cavity in real time, accurately reconstruct the three-dimensional flow field, effectively solve the problem of uneven refractive index distribution, and improve product quality; accelerate the detection speed of abnormal working conditions, adjust process parameters in a timely manner; reduce material waste, lower production costs; shorten the molding cycle, and improve production efficiency.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A full-process quality monitoring method in the production process of optical lenses, including:

[0009] Obtain the first deformation parameter set and the second excitation parameter set, perform wavelet packet decomposition on the first deformation parameter set, and extract the high-frequency components of the n1 layer as the flow front feature vector F f ; Generate a flow vector matrix M according to the second excitation parameter set v ; According to the flow front feature vector F f and the flow vector matrix M v , establish a flow field classification rule to obtain the classification result of the flow anomaly mode; the classification result of the flow anomaly mode includes a first-level flow mode, a second-level flow mode, and a third-level flow mode;

[0010] Retrieve the historical production data sets of n2 batches. Based on the historical production data sets, use a generative adversarial network to construct a flow-process mapping model; according to the flow front feature vector F f , the flow vector matrix M v and the trained flow-process mapping model, obtain a process control strategy;

[0011] Obtain the melt viscosity η in the mold cavity 0 , and the heat transfer coefficient h between the melt and the mold 0 ; Based on η 0 and h 0 , obtain the three-dimensional flow field distribution v of the melt in the mold cavity 0 (x, y, z, t); According to the three-dimensional flow field distribution v 0 (x, y, z, t), obtain the accurate three-dimensional transient flow field distribution v(x, y, z, t); According to the classification result of the flow anomaly mode, perform multi-physics field coupling simulation on the accurate three-dimensional transient flow field distribution v(x, y, z, t) to obtain the multi-physics field coupling simulation result;

[0012] According to the multi - physical - field coupling simulation results and the process control strategy, different process optimization strategies are triggered for the classification results of different flow anomaly patterns.

[0013] Further, the obtaining of the first deformation parameter set and the second excitation parameter set includes:

[0014] Within 0.5 s after the mold is closed, surface deformation time - series data is collected through a distributed fiber Bragg grating array to generate the first deformation parameter set, and the first deformation parameter set includes a pressure fluctuation value sequence P d (t) and a temperature gradient sequence T d (t);

[0015] Synchronously start the ultrasonic guided - wave transmitter, apply a directional excitation wave along the melt flow direction, detect the reflected - wave phase difference through the Doppler effect, and generate the second excitation parameter set. The second excitation parameter set includes the x - axis flow velocity distribution V e (x) and the y - axis shear stress distribution τ e (y).

[0016] Further, the wavelet packet decomposition of the first deformation parameter set and the extraction of the high - frequency components of the n1 layer as the flow front feature vector F f include:

[0017] Using the Daubechies8 wavelet basis function, perform 8 - layer wavelet packet decomposition on the pressure fluctuation value sequence P d (t); in the high - frequency sub - band coefficients of the n1 layer, extract the wavelet energy spectrum peak value and the corresponding scale index; form a 16 - dimensional flow front feature vector F with the extracted wavelet energy spectrum peak value and the corresponding scale index f ; the n1 is a positive integer, and its value range is [5, 8].

[0018] Further, the generation of the flow vector matrix M v includes: using the x - axis flow velocity distribution V e (x) and the y - axis shear stress distribution τ e (y) as the row vector and column vector of the flow vector matrix M v respectively.

[0019] Further, the establishment of the flow - field classification rule and obtaining the classification result of the flow anomaly pattern includes:

[0020] When the flow front feature vector F f ≥0.7 and the included angle θ of the main components of the flow vector matrix M v <15°, it is determined as a first - level flow pattern, and the included angle θ of the main components is the direction of the first principal component of M v ;

[0021] When 0.4 ≤ F f <0.7 and 15° ≤ θ < 30°, it is determined as the secondary flow pattern;

[0022] The remaining cases are determined as the tertiary flow pattern.

[0023] Furthermore, the historical production data set includes historical flow field characteristic quantities and corresponding process parameter optimization quantities; the historical flow field characteristic quantities include the historical flow front characteristic vector F f , hist and the historical flow vector matrix M v,hist ; the process parameter optimization quantities include the historical pressure optimization quantity ΔP hist and the historical temperature optimization quantity ΔT hist ;

[0024] The construction of the flow-process mapping model using the generative adversarial network includes:

[0025] Taking the historical flow front characteristic vector F f , hist and the historical flow vector matrix M v,hist as the input X, and taking the corresponding historical pressure optimization quantity ΔP hist and the historical temperature optimization quantity ΔT hist as the output Y to form the training data pair (X, Y); building the flow-process mapping model and training the flow-process mapping model according to the training data pair (X, Y).

[0026] Furthermore, the obtaining of the process control strategy includes:

[0027] If it is the secondary flow pattern or the tertiary flow pattern, then input the flow front characteristic vector F f and the flow vector matrix M v into the trained flow-process mapping model to obtain the pressure adjustment quantity ΔP pred and the temperature adjustment quantity ΔT pred ;

[0028] If it is the primary flow pattern, then ΔP pred = 0, ΔT pred = 0;

[0029] Combining ΔP pred and ΔT pred to form the process control strategy (ΔP pred , ΔT pred ).

[0030] Furthermore, the heat transfer coefficient h 0 is obtained by performing inverse calculation from the temperature gradient sequence T d (t) and the pre-constructed three-dimensional transient heat transfer equation;

[0031] The obtained three-dimensional flow field distribution v 0 (x, y, z, t) of the melt in the mold cavity includes: Based on η 0 and h 0 , by using the pre-established constitutive equation of generalized Newtonian fluid and the three-dimensional transient flow control equation, and numerically solving by the finite volume method, the three-dimensional flow field distribution v 0 (x, y, z, t) of the melt in the mold cavity is obtained.

[0032] Furthermore, the obtaining of the accurate three-dimensional transient flow field distribution v 0 (x, y, z, t) from the three-dimensional flow field distribution v

[0033] includes: Matching the x-axis velocity distribution V e (x) with v 0 (x, y, z, t), and correcting η 0 and h 0 to obtain the optimized melt viscosity η opt and the optimized heat transfer coefficient h opt ;

[0034] Substituting the optimized melt viscosity η opt and the optimized heat transfer coefficient h opt into the constitutive equation of generalized Newtonian fluid and the three-dimensional transient flow control equation, and numerically solving again to obtain the accurate three-dimensional transient flow field distribution v(x, y, z, t) that matches the actual flow state.

[0035] Furthermore, the matching of the x-axis velocity distribution V e (x) with v 0 (x, y, z, t) and correcting η 0 and h 0 includes:

[0036] For the x-axis velocity distribution V e (x) and v 0 (x, y, z, t), N matching points are extracted on the cross-section y e (x) of V 0 -z 0 to establish the least squares objective function of the velocity deviation;

[0037] Based on the gradient descent method, the least squares objective function is iteratively optimized to obtain the optimized melt viscosity η opt and the optimized heat transfer coefficient h opt .

[0038] Furthermore, the grading results for different flow anomaly patterns trigger different process optimization strategies, including:

[0039] Input the multi-physical field coupling simulation results into a pre-trained refractive index prediction network to obtain the real-time refractive index distribution n(x, y, z, t);

[0040] Determine the target refractive index distribution n tgt (x, y, z), calculate the deviation Δn(x, y, z, t) between n(x, y, z, t) and the target refractive index distribution n tgt (x, y, z), perform double integration of Δn(x, y, z, t) in space and time to obtain the cumulative deviation amount Δn total ;

[0041] Set the deviation threshold θn, and according to Δn total and θn, trigger different process optimization strategies based on the grading results of different flow anomaly patterns.

[0042] Furthermore, the triggering of different process optimization strategies according to Δn total and θn based on the grading results of different flow anomaly patterns includes:

[0043] For the first-level flow pattern, if Δn total < θn, then maintain the current process control strategy (ΔP pred , ΔT pred ) unchanged;

[0044] For the second-level flow pattern or the first-level flow pattern, if Δn total ≥ θn, then trigger the optimization of process parameters based on global sensitivity analysis;

[0045] For the third-level flow pattern, trigger the adaptive parameter regulation strategy.

[0046] The full-process quality monitoring device in the optical lens production process is used to implement the full-process quality monitoring method in the optical lens production process described above. The device includes:

[0047] Flow anomaly detection module: used to obtain the first deformation parameter set and the second excitation parameter set, perform wavelet packet decomposition on the first deformation parameter set, and extract the n1-layer high-frequency component as the flow front feature vector F f ; generate the flow vector matrix M according to the second excitation parameter set v ; establish the flow field grading rule according to the flow front feature vector F f and the flow vector matrix M v , and obtain the grading results of the flow anomaly pattern; the grading results of the flow anomaly pattern include the first-level flow pattern, the second-level flow pattern, and the third-level flow pattern;

[0048] Process control strategy generation module: used to retrieve the historical production data set of batch n2, and based on the historical production data set, an adversarial generative network is adopted to construct a flow-process mapping model; according to the flow front feature vector F f , flow vector matrix M v and the trained flow-process mapping model, to obtain a process control strategy;

[0049] Simulation module: used to obtain the melt viscosity η 0 in the mold cavity, as well as the heat transfer coefficient h 0 between the melt and the mold; based on η 0 and h 0 , to obtain the three-dimensional flow field distribution v 0 (x, y, z, t) of the melt in the mold cavity; according to the three-dimensional flow field distribution v 0 (x, y, z, t), to obtain the accurate three-dimensional transient flow field distribution v(x, y, z, t); according to the classification results of the flow anomaly patterns, perform multi-physics field coupling simulation on the accurate three-dimensional transient flow field distribution v(x, y, z, t) to obtain the multi-physics field coupling simulation results;

[0050] Process optimization module: according to the multi-physics field coupling simulation results and the process control strategy, for different classification results of the flow anomaly patterns, trigger different process optimization strategies.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] The present invention realizes multi-dimensional precise control of the injection molding process. In the data acquisition link, the distributed fiber Bragg grating array and the ultrasonic guided wave transmitter work together to obtain rich and complementary data, providing a reliable basis for subsequent analysis. Through wavelet packet decomposition, constructing a flow vector matrix and establishing a flow field classification rule, different flow anomaly patterns can be finely distinguished, and potential problems can be discovered in advance. The flow-process mapping model constructed based on historical data can give accurate process control strategies according to the real-time flow state, improving the pertinence and effectiveness of process adjustment. The accurate measurement of melt parameters and flow field simulation, combined with multi-physics field coupling simulation, deeply reveals the physical change law of the material in the mold cavity. Finally, the process optimization strategy triggered according to the simulation results and classification effectively reduces the refractive index distribution unevenness rate, reduces quality fluctuations and defect rates, shortens the molding cycle, and reduces material waste. Overall, this method breaks through traditional limitations, improves the real-time performance of quality prediction, accelerates the optimization speed, promotes the injection molding process towards intelligence and digitization, significantly improves the quality and efficiency of optical lens production, and enhances the competitiveness of enterprises in the industry. Description of the Drawings

[0053] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0054] Figure 1 It is a principle flowchart of the full-process quality monitoring method in the production process of the optical lens in the present invention;

[0055] Figure 2 It is a method flowchart for obtaining the first deformation parameter set and the second excitation parameter set in the full-process quality monitoring method in the production process of the optical lens in the present invention;

[0056] Figure 3 It is the method flowchart for performing wavelet packet decomposition on the first deformation parameter set and extracting the high-frequency components of the n1 layer as the flow front feature vector F in the full-process quality monitoring method in the production process of the optical lens in the present invention f ;

[0057] Figure 4 It is to match the x-axis velocity distribution V e (x) with v 0 (x, y, z, t) and correct η 0 and h 0 in the full-process quality monitoring method in the production process of the optical lens in the present invention;

[0058] Figure 5 It is a method flowchart for triggering different process optimization strategies in the full-process quality monitoring method in the production process of the optical lens in the present invention;

[0059] Figure 6 It is a method flowchart for constructing a quadratic response surface model in the full-process quality monitoring method in the production process of the optical lens in the present invention;

[0060] Figure 7 It is a functional module diagram of the full-process quality monitoring device in the production process of the optical lens in the present invention. Specific embodiments

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0062] Embodiment 1

[0063] Please refer to Figure 1 as shown, this embodiment provides a full-process quality monitoring method in the production process of optical lenses, including:

[0064] Step S1000, obtain the first deformation parameter set and the second excitation parameter set, and based on the first deformation parameter set and the second excitation parameter set, establish a flow field classification rule to obtain the classification result of the flow anomaly mode;

[0065] Further, step S1000 includes:

[0066] Step S1100, obtain the first deformation parameter set and the second excitation parameter set;

[0067] Further, as Figure 2 shown, step S1100 includes:

[0068] Step S1110, within 0.5 s after the mold is closed, collect the surface deformation time series data through a distributed fiber Bragg grating array to generate the first deformation parameter set, and the first deformation parameter set includes the pressure fluctuation value sequence P d (t) and the temperature gradient sequence T d (t);

[0069] Step S1120, synchronously start the ultrasonic guided wave transmitter, apply a directional excitation wave along the melt flow direction, and detect the reflected wave phase difference through the Doppler effect to generate the second excitation parameter set, and the second excitation parameter set includes the x-axis flow velocity distribution V e (x) and the y-axis shear stress distribution τ e (y).

[0070] Specifically, the main purpose of step S1100 is to obtain the key data for constructing the initial flow field classification rule, that is, the first deformation parameter set and the second excitation parameter set, which lays a data foundation for the subsequent accurate analysis of the material flow state during the injection molding process. This step collects the physical quantity data related to the material flow from different angles through specific detection techniques and means, and these data complement each other and can comprehensively reflect the dynamic characteristics of the material flow during the injection molding process.

[0071] The distributed fiber Bragg grating array is a monitoring device based on fiber optic sensing technology, and its layout density is 300 per m². This high-density layout method can achieve high-resolution perception of the mold surface deformation field. The working principle of the fiber Bragg grating is based on its sensitivity to external physical quantities (such as strain, temperature). When the external environment changes, the reflection spectrum of the grating will change accordingly. By demodulating and analyzing the grating reflection spectrum, the strain and temperature data of each point on the mold surface can be accurately obtained.

[0072] In actual operation, by filtering the strain data continuously collected in time series, the noise interference in the data can be removed, and the changing trend of the pressure fluctuation can be restored. For example, during the injection molding process, when the molten material is injected into the mold cavity, it will generate pressure on the mold surface. The change of this pressure will cause the change of the strain on the mold surface. The strain data is collected by the fiber Bragg grating, and after filtering, the pressure fluctuation value sequence P reflecting the pressure change can be obtained. d (t). Similarly, by calculating the spatial gradient of the collected temperature data, the temperature gradient distribution T can be obtained. d (t). The temperature gradient refers to the rate of change of temperature with position in space, which can reflect the direction and intensity of heat transfer in the mold. By obtaining the temperature gradient sequence T d (t), the temperature difference at different positions in the mold during the injection molding process can be understood. The beneficial effect of this step is significant. In the initial stage of molding, it can capture the dynamic deformation characteristics of the mold surface caused by the material flow in time, and obtain the pressure fluctuation value sequence P d (t) and the temperature gradient sequence T d (t). The spatio-temporal distribution data of these two key physical quantities has many important significances. From the perspective of process optimization, the pressure fluctuation and temperature gradient data can reflect the flow uniformity and filling state of the material in the cavity. If the pressure fluctuation is too large, it may mean that there is a turbulent flow phenomenon in the material flow, which is likely to cause defects in the product. By monitoring these data, the injection molding process parameters, such as injection speed, temperature, etc., can be adjusted in time to ensure the product quality. From the perspective of production process monitoring, these data provide a basis for real-time monitoring of the injection molding process, which is convenient for operators to detect potential problems in time, prevent production failures, and improve production efficiency.

[0073] The ultrasonic guided wave is an elastic wave that propagates in solid or fluid media, and has characteristics such as long propagation distance and sensitivity to medium characteristics. In this step, a directional ultrasonic guided wave transmitter is installed at the mold gate, and a Doppler effect detector array is arranged on the side wall perpendicular to the gate direction. When the transmitter emits ultrasonic guided waves, the guided waves propagate in the melt. Due to the flow of the melt, the Doppler frequency shift effect will occur.

[0074] The Doppler effect refers to the phenomenon that when there is relative motion between the wave source and the observer, the frequency of the wave received by the observer will change. In this scenario, when the ultrasonic guided wave propagates in the flowing melt, the flow of the melt causes the frequency of the reflected wave to change relative to the transmitted wave. By detecting the frequency change of the reflected wave signal and combining the spatial distribution characteristics of the guided wave field, the velocity vector field of the material flow can be inversely calculated, and thus the x-axis velocity distribution V can be obtained. e(x). Meanwhile, during the flow of the material, it is subjected to shear forces from the cavity wall surface, and these shear forces cause slight changes in the phase of the reflected wave. By demodulating and analyzing the phase difference distribution of the reflected wave and combining with the geometric dimension parameters of the cavity, the shear stress distribution τ can be calculated. e (y).

[0075] For example, during the actual injection molding production of a certain plastic part, assume that the frequency change and phase difference of the reflected wave are measured by an ultrasonic guided wave detection system. After calculation, at a certain moment, the flow velocity distribution V in the x-axis direction e (x) shows that the melt has a relatively fast flow velocity in a certain area of the cavity, while the shear stress distribution τ in the y-axis direction e (y) indicates that a relatively large shear force acts on this area. This may mean that the melt flow is not very stable in this area and defects are likely to occur. The second set of excitation parameters V e (x), τ e (y) characterize the dynamic characteristics of the material flow from a rheological perspective and complement the first set of deformation parameters. The first set of deformation parameters mainly reflects the deformation and temperature changes on the mold surface due to material flow, while the second set of excitation parameters delves into the melt interior to obtain the dynamic distributions of key parameters such as flow velocity and shear stress. The fusion of such multi-source data jointly constitutes the multi-source heterogeneous data basis for comprehensively perceiving the injection molding process. The active ultrasonic guided wave detection method is adopted to overcome the defect of insufficient sensitivity of traditional passive sensors. Traditional passive sensors can usually only detect some macroscopic physical quantity changes and it is difficult to accurately measure the microscopic flow parameters inside the melt. However, the ultrasonic guided wave detection method can penetrate into the complex structure interior to detect the microscopic motion parameters of the fluid. By optimizing the frequency selection and emission direction of the ultrasonic guided wave, the signal-to-noise ratio of the phase difference signal can be maximized, the measurement sensitivity can be improved, making the obtained data more accurate and reliable, providing strong support for subsequent accurate analysis of the material flow state and optimization of the injection molding process, and playing an important role in improving product quality stability, reducing the scrap rate, and enhancing production efficiency.

[0076] Step S1200, based on the first set of deformation parameters and the second set of excitation parameters, establish a flow field classification rule to obtain the classification result of the flow anomaly pattern;

[0077] Furthermore, step S1200 includes:

[0078] Step S1210, perform wavelet packet decomposition on the first set of deformation parameters, and extract the high-frequency components of the n1 layer as the flow front feature vector F f ; n1 is a positive integer, and the value range is [5, 8];

[0079] Furthermore, as Figure 3 shown, step S1210 includes:

[0080] Step S1211: Using the Daubechies8 wavelet basis function, perform 8-layer wavelet packet decomposition on the pressure fluctuation value sequence P d (t).

[0081] Step S1212: In the high-frequency subband coefficients of the n1-th layer, extract the peak value of the wavelet energy spectrum and the corresponding scale index.

[0082] Step S1213: Construct a 16-dimensional flow front feature vector F from the extracted peak value of the wavelet energy spectrum and the corresponding scale index f .

[0083] Specifically, in the optical lens injection molding production process, performing wavelet packet decomposition on the first deformation parameter set to extract the flow front feature vector F f is a key link for monitoring the flow state. The first deformation parameter set includes the pressure fluctuation value sequence P d (t) and the temperature gradient sequence T d (t), where the pressure fluctuation value sequence P d (t) reflects the dynamic change of the pressure on the mold surface due to the material flow during injection molding, and the temperature gradient sequence T d (t) reflects the temperature difference distribution on the mold surface due to material flow and heat transfer.

[0084] Wavelet packet decomposition is a time-frequency analysis method. In this step, the Daubechies8 wavelet basis function is used to perform 8-layer wavelet packet decomposition on the pressure fluctuation value sequence P d (t). The Daubechies8 wavelet basis function has good compact support and regularity, and can well characterize the signal features in the time domain and frequency domain. When performing wavelet packet decomposition, the signal will be continuously decomposed into subbands of different frequencies. The low-frequency subbands contain the main trend information of the signal, and the high-frequency subbands contain the details and local change information of the signal. From the perspective of signal feature extraction, this value range can accurately capture the key features of the flow front. During the injection molding process, it can sensitively perceive the changes related to flow instability in the high-frequency components of the pressure fluctuation value sequence P d (t), and construct a 16-dimensional flow front feature vector F that accurately characterizes the flow front state through the extracted peak value of the wavelet energy spectrum and the scale index f . In terms of computing resources and processing efficiency, the value range of [5, 8] achieves a balance, avoiding insufficient feature mining due to too small n1 affecting anomaly recognition, or excessive consumption of computing resources and extension of processing time due to too large n1, which reduces real-time performance. In terms of the stability and reliability of the flow field classification rules, this value range ensures the flow front feature vector F fStability is achieved, grading errors are reduced, and in production of different batches, even if there are fluctuations in process parameters, the grading model can operate stably, providing a reliable basis for process adjustment and quality control, and ensuring product quality consistency.

[0085] After completing the 8-layer wavelet packet decomposition, the peak value of the wavelet energy spectrum and the corresponding scale index are extracted from the high-frequency sub-band coefficients at the n1-th layer (where the value range of n1 is [5, 8]). The peak value of the wavelet energy spectrum represents the degree of energy concentration of the signal in that frequency sub-band, while the scale index reflects the variation characteristics of the signal at different time scales. For example, when the material flow becomes unstable, such as in the case of turbulence, the energy in the high-frequency band of the pressure fluctuation value sequence P d (t) will increase significantly, the peak value of the wavelet energy spectrum of the corresponding frequency sub-band will increase significantly, and the scale index will also change accordingly. The extracted peak values of the wavelet energy spectrum and the corresponding scale indices are used to form a 16-dimensional flow front feature vector F f .

[0086] The beneficial effects of this process are significant. From the monitoring perspective, the flow front feature vector F f can sensitively reflect the flow instability during the filling process of the molten material in the mold cavity. Traditional injection molding process monitoring relies on empirical models or simple statistical rules and is difficult to accurately capture complex three-dimensional flow changes. However, this method uses wavelet packet decomposition to analyze the pressure fluctuation signal from both the time domain and the frequency domain, can extract high-dimensional features, and enhance the ability to identify abnormal flow patterns. For example, in actual production, if a certain dimension value of the flow front feature vector F f is abnormally high, combined with process experience and historical data, it can quickly determine that there may be flow stratification or curling phenomena, thus giving an early warning and avoiding the production of defective optical lens products. From the perspective of optimizing production, accurate monitoring of flow instability provides an accurate basis for subsequent process parameter adjustment, helps to optimize the injection molding process, improve product quality and production efficiency, and reduce the scrap rate. In terms of cost control, timely discovery and solution of flow anomalies reduce the waste of raw materials and rework costs caused by product defects, and improve the economic benefits of the enterprise.

[0087] Step S1220, generate a flow vector matrix M according to the second set of excitation parameters v ;

[0088] Using the x-axis velocity distribution V e and the y-axis shear stress distribution τ e as the row vector and column vector of the flow vector matrix M v respectively.

[0089] Specifically, the second set of excitation parameters includes the x-axis velocity distribution V e(x) and y-axis shear stress distribution τ e (y), x-axis flow velocity distribution V e (x) reflects the change in the flow velocity of the molten material in the mold cavity along the x direction, and the y-axis shear stress distribution τ e (y) reflects the stress distribution state generated by the shear force of the cavity wall on the material in the y direction during material flow.

[0090] Taking the x-axis flow velocity distribution V e (x) and the y-axis shear stress distribution τ e (y) as the row vector and column vector of the flow vector matrix M v respectively. Compared with simply superimposing or splicing the two, this method can explore the spatial correlation characteristics of the flow velocity field and the shear stress field. During the injection molding process, the flow velocity of the material and the shear stress it receives affect each other. For example, near the cavity wall, the material flow velocity will decrease due to wall friction, and at the same time, a large shear stress will be generated. Constructing them into the flow vector matrix M v can more comprehensively describe the flow state of the material in the two-dimensional plane.

[0091] Using a convolutional encoder to process these data, the convolutional encoder can automatically extract the spatial features and internal connections in the data. The convolution operation slides the convolution kernel over the data to extract features from local regions, and can effectively capture the change rules of the flow velocity field and the shear stress field in space. For example, at a certain moment, when the flow velocity of the material suddenly increases in a certain area of the cavity, the convolutional encoder can identify the correlation between the flow velocity change in this area and the surrounding shear stress distribution, and explore the physical mechanism contained in the distribution of the second excitation parameter set. The flow vector matrix M v generated in this way is a more abstract and robust representation of the material flow state, and can better reflect the actual flow situation of the material in the cavity.

[0092] Step S1230, according to the flow front feature vector F f and the flow vector matrix M v , establish a flow field classification rule to obtain the classification result of the flow anomaly mode, and the classification result of the flow anomaly mode includes the first-level flow mode, the second-level flow mode and the third-level flow mode;

[0093] Furthermore, step S1230 includes:

[0094] Step S1231, when the flow front feature vector F f ≥0.7 and the included angle θ between the main components of the flow vector matrix M v <15°, it is determined as the first-level flow mode, and the included angle θ between the main components is the direction of the first principal component of M v ;

[0095] Step S1232, when 0.4 ≤ F f < 0.7 and 15° ≤ θ < 30°, it is determined as the secondary flow pattern;

[0096] Step S1233 determines the remaining cases as the tertiary flow pattern.

[0097] Specifically, Step S1230 aims to establish a set of scientific and reasonable flow field classification rules based on the flow front characteristic vector F f and the flow vector matrix M v , so as to obtain the classification results of the flow anomaly pattern, providing a key basis for subsequent process adjustment and quality control. This step effectively evaluates and classifies the flow stability of the material during the injection molding process by comprehensively considering multiple key parameters reflecting the material flow state.

[0098] In Step S1231, when the flow front characteristic vector F f ≥ 0.7 and the included angle θ between the main components of the flow vector matrix M v < 15°, it is determined as the primary flow pattern. The flow front characteristic vector F f here is a key index extracted by performing wavelet packet decomposition on the pressure fluctuation value sequence P d (t) in the first deformation parameter set, which can sensitively reflect the flow instability of the molten material during the filling process in the mold cavity. The larger the value of F f , the more significant the characteristics of flow instability. The included angle θ between the main components of the flow vector matrix M v refers to the direction of the first principal component of M v , and this included angle reflects the deviation degree between the material flow direction and the main axis of the mold cavity. When θ < 15°, it means that the deviation between the material flow direction and the main axis of the mold cavity is small, and the material flow is relatively smooth and close to the ideal laminar flow state. In actual injection molding production, assuming the production of an optical lens, during the injection molding process, it is monitored that the flow front characteristic vector F f reaches 0.8, and at the same time, the included angle θ between the main components of the flow vector matrix M v is 10°. According to the rules, it can be determined as the primary flow pattern. This indicates that the flow state of the material in the mold cavity is good at this time, and basically no adjustment of the process parameters is required, and the probability of product defects is relatively low. This precise determination method can help producers timely understand the production status, avoid unnecessary process adjustments, save production time and costs, and improve production efficiency. At the same time, accurately identifying the stable production state helps to accumulate high-quality production data, providing strong support for the subsequent consistency control of product quality and ensuring the stability of product quality.

[0099] Step S1232 stipulates that when 0.4 ≤ Ff When 0.7 < F < 1 and 15° ≤ θ < 30°, it is determined as the secondary flow pattern. In this interval, the flow front characteristic vector F f indicates that the material flow has begun to show a certain degree of instability, but has not reached a severe level; while the range of the principal component angle θ indicates that the deviation of the material flow direction from the main axis of the mold cavity has increased, and the flow begins to deviate from the ideal state. For example, when producing another optical lens with a special shape, if it is detected that F f is 0.5 and θ is 20°, it belongs to the secondary flow pattern. In this case, although the material flow has not gotten out of control, there are already potential risks, and it is necessary to fine-tune the process parameters, such as appropriately adjusting the injection pressure or temperature, to ensure that the material can fill the cavity more evenly and avoid quality problems such as uneven local thickness and surface defects of the product. Through timely fine-tuning, the generation of defects can be effectively prevented, the qualified rate of products can be improved, the production cost can be reduced, and the competitiveness of products in the market can be enhanced. At the same time, the accurate judgment of the secondary flow pattern also helps enterprises optimize the production process, continuously improve the production technology level, and accumulate experience in dealing with different flow states.

[0100] Step S1233 determines the remaining cases as the tertiary flow pattern. This means that in these cases, the material flow state reflected by the flow front characteristic vector F f and the principal component angle θ is poor, and the degree of flow instability is relatively high. For example, when producing an optical lens with a complex structure, if F f is less than 0.4 and θ is greater than 30°, it will be determined as the tertiary flow pattern. At this time, the flow of the material in the cavity may exhibit severe instability phenomena such as turbulence and stratification, which has a great impact on the product quality. In response to this situation, it is necessary to actively intervene in the process conditions and take more radical adjustment measures, such as significantly adjusting the injection speed and optimizing the mold temperature distribution, to suppress the violent fluctuations of the flow and ensure the product quality. This accurate definition and timely handling of the severely unstable flow state can effectively reduce the generation of waste products and reduce the economic losses of enterprises. At the same time, through the research and treatment of the tertiary flow pattern, it helps to promote the innovation and development of the injection molding process, develop more advanced process control methods, and improve the production level of the entire industry.

[0101] Overall, step S1230 and its sub-steps achieve a detailed classification of the material flow state during the injection molding process through clear classification rules. This classification method has many beneficial effects. From the perspective of quality control, it can promptly detect abnormal situations in material flow, take corresponding measures for different degrees of abnormalities, effectively prevent and reduce product defects, and improve product quality. From the perspective of production management, it helps to reasonably arrange production resources. For stable first-level flow patterns, unnecessary monitoring and adjustments can be reduced, and resources can be concentrated on dealing with unstable flow states; for second-level and third-level flow patterns, process parameters can be accurately adjusted according to specific situations, avoiding resource waste caused by blind adjustments and improving production efficiency. From the perspective of process optimization, the large amount of data and processing experience accumulated under different flow patterns provide a basis for optimizing the injection molding process, helping enterprises to continuously improve production processes, enhance their own technical strength, and gain an advantageous position in the market competition. In addition, this classification model also has good scalability and generality. It is not only applicable to the current production of optical lenses, but after appropriate adjustment, it can also be applied to the production process monitoring and quality control of other injection molded products, providing strong technical support for the development of the entire injection molding industry.

[0102] Step S2000, retrieve the historical production data set of n2 batches. Based on the historical production data set, use a generative adversarial network to construct a flow-process mapping model; according to the flow front feature vector F f , the flow vector matrix M v and the trained flow-process mapping model, obtain the process control strategy;

[0103] Furthermore, step S2000 includes:

[0104] Step S2100, retrieve the historical production data set of n2 batches. The historical production data set includes historical flow field feature quantities and corresponding process parameter optimization quantities; the historical flow field feature quantities include the historical flow front feature vector F f , hist and the historical flow vector matrix M v,hist ; the process parameter optimization quantities include the historical pressure optimization quantity ΔP hist and the historical temperature optimization quantity ΔT hist ;

[0105] Specifically, the purpose of step S2100 is to obtain historical production data for constructing a flow-process mapping model, providing a basis for subsequent accurate prediction and adjustment of process parameters. The historical production data set of n2 batches covers rich production process information. Among them, the historical flow field feature quantities include the historical flow front feature vector F f,hist and the historical flow vector matrix M v,hist . The historical flow front feature vector F f,histRecords the unstable characteristics of the material flow front in past production, such as whether abnormal conditions such as turbulence and stratification occur at the material flow front during different injection molding stages. This information is obtained through the analysis of the sequence of pressure fluctuation values at that time. The historical flow vector matrix M v,hist Characterizes the velocity field distribution of historical samples during the flow filling stage, specifically manifested as the velocity distribution in the x-axis direction and the shear stress distribution in the y-axis direction, etc., reflecting the flow velocity of the material at different positions in the cavity and the shear force received.

[0106] The process parameter optimization amounts include the historical pressure optimization amount ΔP hist and the historical temperature optimization amount ΔT hist , which are quantitative indicators for optimizing and adjusting the two key process parameters of pressure and temperature for different flow anomalies. For example, when it is monitored that the material flow appears abnormal, resulting in possible defects in the product, the injection molding pressure and temperature will be adjusted, and the specific values of these adjustments are recorded as the historical pressure optimization amount ΔP hist and the historical temperature optimization amount ΔT hist .

[0107] Obtaining a large number of (such as n2 taking 100) historical production data sets has many important significances. From the perspective of model training, rich data can more comprehensively cover various possible production conditions, making the subsequent constructed flow-process mapping model more generalizable and capable of adapting to the process parameter prediction requirements under different production conditions. Through the analysis of these data, the potential relationship between the flow field characteristics and the process parameter optimization can be explored. For example, it is found that when the historical flow front feature vector F f,hist exceeds a certain threshold, and the x-axis direction velocity distribution in the historical flow vector matrix M v,hist appears abnormal, it is often necessary to make adjustments to the pressure and temperature in a specific direction and amplitude. This provides accurate samples for subsequent model training and helps improve the accuracy of model prediction. From the perspective of production optimization, these historical data provide a strong reference for the process adjustment in the current production process. When similar flow anomalies to the historical ones occur, the historical optimization experience can be quickly borrowed, reducing the trial-and-error cost, improving production efficiency, reducing the scrap rate, and enhancing the stability of product quality.

[0108] Step S2200, based on the historical production data set, construct a flow-process mapping model using a generative adversarial network;

[0109] Furthermore, step S2200 includes:

[0110] Step S2210, the historical flow front feature vector F f , hist and the historical flow vector matrix M v,histTake the corresponding historical pressure optimization amount ΔP as the input X hist and the historical temperature optimization amount ΔT hist as the output Y to form a training data pair (X, Y);

[0111] Step S2220: Build a flow-process mapping model and train the flow-process mapping model according to the training data pair (X, Y);

[0112] The flow-process mapping model includes a generator G and a discriminator D. Construct a generator loss function L G and a discriminator loss function L D , and by minimizing the generator loss function L G and the discriminator loss function L D , alternately train the generator G and the discriminator D until reaching the Nash equilibrium.

[0113] The generator loss function L G includes:

[0114]

[0115] Where:

[0116] : Represents the mathematical expectation, used to calculate the average value of a random variable. Here, it is used to calculate the expectation of variables under different distributions.

[0117] : Represents the distribution of real data (i.e., historical flow field characteristic quantities, such as and ). Obtained from the historical production dataset, representing the true distribution of flow field characteristics in the actual production process.

[0118] : Samples drawn from the real data distribution , that is, the specific values of historical flow field characteristic quantities.

[0119] : The discriminator, whose input is the process parameter combination generated by the generator or the real process parameter combination , and the output is a probability value indicating that the input is a real sample.

[0120] : The generator, whose input is the historical flow field characteristic quantity , and the output is the generated process parameter combination , aiming to mimic the real process parameters as much as possible.

[0121] : The actual process parameter optimization amount (such as and ), which is obtained from the historical production dataset, represents the optimized values of the process parameters actually carried out for specific flow field characteristics.

[0122] : The weight coefficient is used to balance the importance of the two terms in the generator loss function. It can be adjusted within a certain range (such as ) through methods such as cross-validation to achieve the best training effect. A larger value will pay more attention to the closeness between the generated process parameters and the actual process parameters, while a smaller value will pay more attention to the ability to deceive the discriminator.

[0123] The first term : Measures the probability that the discriminator judges the process parameter combination generated by the generator as a real sample. The larger this probability is, the better, that is, it makes it difficult for the discriminator to distinguish the generated parameters from the real parameters. This term prompts the generator to generate combinations closer to the actual process parameter distribution.

[0124] The second term : Represents the mean square error between the generated process parameter combination and the actual process parameters. This term ensures that the parameters generated by the generator can not only deceive the discriminator but also be numerically close to the actual process parameter optimization amount, thus ensuring the practicality of the generated parameters.

[0125] As the process parameters generated by the generator get closer and closer to the actual process parameters , the value of the second term will gradually decrease. At the same time, if the generated makes it more difficult for the discriminator to distinguish it from the real sample, the value of the first term will also decrease. Generally speaking, when the process parameters generated by the generator meet the requirements more, the value will be smaller.

[0126] The discriminator loss function L D includes:

[0127]

[0128] The first term : Measures the probability that the discriminator correctly identifies the actual process parameter sample as real. The discriminator hopes that this probability is as large as possible, that is, the judgment of the real sample is more accurate.

[0129] The second term : Measures the probability that the discriminator correctly identifies the sample generated by the generator as fake. The discriminator also hopes that this probability is as large as possible, that is, the ability to identify the generated sample is stronger.

[0130] The entire loss function prompts the discriminator to continuously improve its ability to distinguish between real samples and generated samples, thereby driving the generator to generate process parameters closer to the real ones.

[0131] When the discriminator's recognition probability for real process parameter samples is larger, the value of the first term is smaller; when the probability that the discriminator recognizes the generated samples as fake is larger, the value of the second term is smaller. Therefore, when the discriminator's discrimination ability is stronger, the value of is smaller. During the training process, the discriminator continuously optimizes itself, making gradually decrease, and at the same time also prompting the generator to improve to cope with the improvement of the discriminator, ultimately reaching the Nash equilibrium.

[0132] The essence of sub-step S2210 is to establish the correlation between the flow field characteristics and the corresponding process parameter optimization strategies. For example, in a set of training data pairs, if the historical flow front feature vector F f,hist shows that there are obvious instabilities in the material flow front, and the historical flow vector matrix M v,hist indicates abnormal flow velocity and shear stress distributions, the corresponding historical pressure optimization amount ΔP hist and the historical temperature optimization amount ΔT hist record the adjustment values of the injection pressure and temperature made at that time to solve the flow problem. Through a large number of such data pairs, the optimization of process parameters required under different flow states can be fully presented, providing sufficient information for the model to learn.

[0133] The goal of the generator G is to generate a process parameter combination G(u) that is as close as possible to the real process parameter optimization amount v based on the input historical flow field characteristic quantity u. The first term G in its loss function L measures the probability that the generated process parameter combination is judged as a real sample by the discriminator. The generator hopes that this probability is as large as possible, that is, making it difficult for the discriminator to distinguish the generated parameters from the real ones, and prompting the generator to generate a process parameter combination that more conforms to the real distribution; the second term represents the mean square error between the generated process parameter combination and the real process parameters, ensuring that the generated parameters can not only deceive the discriminator but also be numerically close to the real optimization amount, guaranteeing the practicality of the generated parameters. For example, if the pressure and temperature adjustment parameters generated by the generator differ too much from the real optimization amount, the mean square error value of the second term will be very large, prompting the generator to adjust the generated parameters.

[0134] The goal of the discriminator D is to maximize its ability to distinguish between real samples and generated samples. During the training process, the discriminator continuously optimizes itself to improve its ability to distinguish between real samples and generated samples, thereby driving the improvement of the generator. For example, when the discriminator can accurately identify the unreasonable process parameters generated by the generator, the generator will adjust its generation strategy according to the feedback of the discriminator to generate parameters closer to the real situation. As the training progresses, the generator and the discriminator continuously play a game until they finally reach a Nash equilibrium. At this time, the generator G can directly generate optimized process parameters from the given flow characteristics.

[0135] The flow-process mapping model obtained by training through the adversarial generation network can accurately predict a suitable process parameter adjustment plan based on the flow field characteristics monitored in real time, effectively avoiding product defects caused by improper process parameters and improving product quality. In terms of improving production efficiency, it reduces the time and resource consumption required for traditional trial-and-error process adjustment, quickly provides accurate process parameter suggestions for production, speeds up the production process, and improves production efficiency. From the perspective of cost control, it reduces the scrap rate, reduces raw material waste and rework costs, and improves the economic benefits of the enterprise. At the same time, this model also provides a powerful tool for the optimization research of the optical lens production process, helps to deeply explore the complex relationship between the flow field and process parameters, and promotes the technological progress of the industry.

[0136] Step S2300, according to the flow front feature vector F f , the flow vector matrix M v and the trained flow-process mapping model, obtain the pressure adjustment amount ΔP pred and the temperature adjustment amount ΔT pred , and combine ΔP pred and ΔT pred to form a process control strategy (ΔP pred , ΔT pred ).

[0137] If it is a first-level flow mode, then ΔP pred = 0, ΔT pred = 0;

[0138] If it is a second-level flow mode or a third-level flow mode, then input the flow front feature vector F f and the flow vector matrix M v into the trained flow-process mapping model to obtain the pressure adjustment amount ΔP pred and the temperature adjustment amount ΔT pred .

[0139] Specifically, the flow front feature vector F f reflects the flow instability of the molten material during filling in the mold cavity. For example, when F fThe numerical value is abnormally high, which may indicate problems such as turbulence and delamination in the material flow; the flow vector matrix M v characterizes the flow state of the material in terms of flow velocity and shear stress, such as the x-axis flow velocity distribution V e (x) and the y-axis shear stress distribution τ e (y). Their changes can reflect the flow characteristics of the material at different positions in the cavity.

[0140] When it is judged as the first-level flow mode, it means that the material flow is in the most stable quasi-laminar flow state. At this time, ΔP pred = 0, ΔT pred = 0, that is, there is no need to adjust the current injection pressure and temperature parameters. This is because in this stable state, the existing process parameters can ensure that the material fills the cavity evenly and stably. Continuing to maintain the current parameters can ensure the stability of product quality and avoid introducing new unstable factors due to unnecessary parameter adjustments. For example, in an optical lens production workshop, when the monitored flow front characteristic vector F f and the flow vector matrix M v meet the determination conditions of the first-level flow mode, by maintaining the current injection pressure and temperature parameters, the quality of the produced lens products is stable and the yield is high.

[0141] If it is the second-level flow mode or the third-level flow mode, it indicates that the material flow has deviated from the ideal state to varying degrees or has a high degree of instability. At this time, input the flow front characteristic vector F f and the flow vector matrix M v into the trained flow-process mapping model. The model will output the pressure adjustment amount ΔP pred and the temperature adjustment amount ΔT pred according to the relationship between the flow field characteristics learned previously and the process parameter optimization. These adjustment amounts are obtained based on the learning and analysis of a large amount of historical production data and can specifically solve the current flow anomaly problem. For example, in the second-level flow mode, if the flow front characteristic vector F f shows a slight instability trend in the material flow and there is a local anomaly in the x-axis flow velocity distribution in the flow vector matrix M v , the model may output a small pressure adjustment amount ΔP pred and an appropriate temperature adjustment amount ΔT pred . By finely adjusting the injection pressure and temperature, the material flow can be restored to stability and the possibility of product defects can be reduced. In the third-level flow mode, the model will output more significant pressure and temperature adjustment amounts according to more complex flow anomalies to suppress the violent fluctuations of the flow and ensure product quality.

[0142] The beneficial effects of this step are reflected in multiple aspects. From the perspective of product quality assurance, by adjusting process parameters in a timely and accurate manner, product defects caused by abnormal flow, such as bubbles, uneven thickness, internal stress concentration, etc., are effectively avoided, improving the optical performance and overall quality of the product. In terms of production efficiency improvement, based on the rapid response of the model, an adjustment plan can be quickly given after detecting abnormal flow, reducing production interruptions and trial-and-error time, and accelerating the production process. From the perspective of cost control, the scrap rate is reduced, raw material waste and rework costs are decreased, and the production efficiency of the enterprise is improved. At the same time, this data-driven process control strategy provides strong support for the automation and intelligent control of the optical lens production process, helping to improve the production technology level of the entire industry.

[0143] Step S3000, obtain the melt viscosity η in the mold cavity 0 , and the heat transfer coefficient h between the melt and the mold 0 ; Based on η 0 and h 0 , obtain the three-dimensional flow field distribution v 0 (x, y, z, t) of the melt in the mold cavity; Match the x-axis velocity distribution V e (x) with v 0 (x, y, z, t), and correct η 0 and h 0 , to obtain the optimized melt viscosity η opt and the optimized heat transfer coefficient h opt ; According to η opt and h opt , obtain the accurate three-dimensional transient flow field distribution v(x, y, z, t); According to the classification results of the abnormal flow pattern, perform multi-physics field coupling simulation on the accurate three-dimensional transient flow field distribution v(x, y, z, t) to obtain the multi-physics field coupling simulation results;

[0144] Furthermore, step S3000 includes:

[0145] Step S3100, obtain the melt viscosity η in the mold cavity 0 , and the heat transfer coefficient h between the melt and the mold 0 ; The heat transfer coefficient h 0 is obtained by inverse calculation from the temperature gradient sequence T d (t) and the pre-constructed three-dimensional transient heat transfer equation;

[0146] Specifically, the melt viscosity η 0It reflects the ability of the melt to resist flow deformation inside, which directly affects the flow speed and uniformity of the material in the mold cavity. For example, when the melt viscosity is high, the material flow is relatively difficult, which may lead to insufficient filling or uneven flow; while when the melt viscosity is low, although the flow is relatively easy, it may cause problems such as flash. In this embodiment, a high-pressure capillary rheometer is used to measure the melt viscosity η in real time 0 , compared with the traditional method of looking up empirical values in a table, this in-situ measurement method can more accurately reflect the rheological properties of the melt under actual injection molding process conditions. Because during the injection molding process, the viscosity of the melt is dynamically affected by various factors such as temperature, pressure, and shear rate. The traditional table lookup method is difficult to take into account these real-time changing factors, while in-situ measurement can track the change of melt viscosity in real time

[0147] Heat transfer coefficient h 0 is used to characterize the efficiency of heat transfer between the melt and the mold, which is obtained by inverse calculation from the temperature gradient sequence T d (t) and the pre-constructed three-dimensional transient heat transfer equation. The temperature gradient sequence T d (t) is obtained by collecting using a distributed fiber Bragg grating array within 0.5 s after the mold is closed in the early stage. The three-dimensional transient heat transfer equation is established based on the basic principles of heat transfer, comprehensively considering the heat transfer process in time and space. Through this calculation process, the heat transfer coefficient h between the melt and the mold at different times and positions can be obtained 0 . Accurately obtaining the heat transfer coefficient h 0 is of great significance for analyzing the heat distribution and transfer law during the injection molding process. During the injection molding process, the heat exchange between the melt and the mold will affect the temperature distribution of the melt, and thus affect its viscosity and flow performance. If the heat transfer coefficient is inaccurate, the temperature change of the melt in the cavity cannot be accurately simulated, resulting in deviation in the analysis of the melt flow state. Therefore, accurately obtaining the melt viscosity η 0 and the heat transfer coefficient h 0 lays a solid data foundation for accurately simulating the flow and heat transfer process of the melt in the mold cavity subsequently, helps to improve the understanding and control ability of the injection molding process, and thus effectively guarantees the production quality of optical lenses

[0148] Step S3200, based on η 0 and h 0 , using the pre-constructed generalized Newtonian fluid constitutive equation and three-dimensional transient flow control equation, numerically solve by the finite volume method to obtain the three-dimensional flow field distribution v 0 (x, y, z, t);

[0149] Specifically, polymer melts exhibit non-Newtonian rheological properties during the injection molding process, that is, their viscosity changes with changes in factors such as shear rate. The generalized Newtonian fluid constitutive equation is a mathematical model used to describe this property, which can accurately characterize the rheological behavior of polymer melts under different shear conditions. For example, during the injection molding process, the melt is subjected to a higher shear rate near the gate. At this time, the generalized Newtonian fluid constitutive equation can reflect the changes in the melt viscosity, which in turn affects the flow velocity and pressure distribution of the melt.

[0150] The three-dimensional transient flow control equations include mass, momentum and energy conservation equations, which describe the flow behavior of polymer melt in the cavity from the physical essence. The mass conservation equation ensures that the mass of the melt will not be created or disappeared in the injection molding process, ensuring the physical rationality of the calculation; the momentum conservation equation describes the momentum change of the melt during the flow process, which is closely related to the speed and force of the melt; the energy conservation equation takes into account the energy conversion and transfer of the melt during the flow process, such as the energy change caused by frictional heat generation.

[0151] The finite volume method is a commonly used numerical calculation method, and its core idea is to discretize the cavity space and time. Specifically, the cavity is divided into a series of tiny control volumes, and the partial differential form of the control equation is integrated in each control volume to convert it into a set of algebraic equations. Then, an iterative algorithm is used to solve these algebraic equations with high precision. In each iteration, the value of the variable is continuously updated according to the result of the previous iteration until certain convergence conditions are met. In this way, the distribution of physical quantities such as velocity, pressure, and temperature of the melt in the cavity at different times and positions can be obtained, that is, the three-dimensional flow field distribution v 0 (x,y,z,t).

[0152] This step is achieved by accurate numerical simulation of the three-dimensional flow field distribution v 0 (x, y, z, t), provides detailed information for in-depth understanding of the flow law of the melt during the injection molding process. For example, the flow path of the melt in the cavity and whether there are dead corners can be analyzed, thereby providing a basis for optimizing mold design. Secondly, accurate flow field distribution helps to predict possible defects in the product, such as weld marks, bubbles, etc. By discovering potential problems in advance, process parameters can be adjusted in time to reduce scrap rates and improve product quality. In addition, this method based on physical equations and numerical calculations is more scientific and reliable than traditional empirical methods, and can more accurately guide actual production, improve production efficiency, reduce production costs, and promote technological progress in the injection molding production process of optical lenses.

[0153] Step S3300: Distribute the velocity V in the x-axis direction. e(x) and v 0 (x, y, z, t) is matched to correct η 0 and h 0 to obtain the optimized melt viscosity η opt and the optimized heat transfer coefficient h opt ;

[0154] Furthermore, as Figure 4 shown, step S3300 includes:

[0155] Step S3310, for the x - axis velocity distribution V e (x) and v 0 (x, y, z, t), on the cross - section y e -z 0 of V 0 (x), extract N matching points and establish the least - squares objective function of the velocity deviation;

[0156] The least - squares objective function includes:

[0157]

[0158] where:

[0159] is the number of matching points;

[0160] is the th matching point's coordinate;

[0161] and are corresponding to the and coordinates;

[0162] is the current time;

[0163] is the measured flow velocity value at the th matching point;

[0164] is the simulated flow velocity value at the th matching point;

[0165] is the melt viscosity to be corrected;

[0166] is the heat transfer coefficient to be corrected.

[0167] Step S3320: Iteratively optimize and solve the least - squares objective function based on the gradient - descent method to obtain the optimized melt viscosity η opt and the optimized heat - transfer coefficient h opt .

[0168] Specifically, first extract N matching points on the cross - section y e (x) of V 0 -z 0 . These matching points are the key positions for comparing the measured flow velocity and the simulated flow velocity. For example, in a specific optical lens injection mold, according to the structural characteristics of the mold and the main path of the melt flow, select the cross - section y 0 -z 0 in the more critical area of the melt flow, and uniformly or non - uniformly select N points on this cross - section as matching points according to actual needs. By comparing the flow velocity values (i.e., the measured flow velocity values e ) in the x - axis flow velocity distribution V (x) obtained by actual measurement with the flow velocity values (i.e., the simulated flow velocity values 0 ) of the corresponding points calculated from the three - dimensional flow - field distribution v (x,y,z,t), the difference between the flow field simulated by the current melt viscosity η 0 and heat - transfer coefficient h 0 and the actual situation can be evaluated.

[0169] Based on these matching points, establish the least - squares objective function of the velocity deviation. The significance of the least - squares objective function is to find the optimal melt viscosity η and heat - transfer coefficient h by minimizing the sum of the squares of the deviation between the measured flow velocity and the simulated flow velocity. Since the melt viscosity η and heat - transfer coefficient h affect the calculation result of the three - dimensional flow - field distribution v 0 (x,y,z,t), and thus affect the simulated flow velocity value, adjusting these two parameters to make the objective function J(η,h) reach the minimum value can make the simulated flow velocity closer to the measured flow velocity, so that the simulated flow field is more in line with the actual situation.

[0170] Subsequently, iteratively optimize and solve the least - squares objective function based on the gradient - descent method. The gradient - descent method is a commonly used optimization algorithm. It calculates the gradient of the objective function with respect to the variables (i.e., the melt viscosity η and heat - transfer coefficient h), and then gradually adjusts the values of the variables along the opposite direction of the gradient, so that the value of the objective function gradually decreases. In each iteration process, calculate the gradient of the objective function according to the current variable values, and then update the variables according to a certain step size. For example, in the first iteration, according to the initial melt viscosity η 0 and heat - transfer coefficient h 0Calculate the gradient of the objective function and then adjust the values of these two parameters; in the second iteration, calculate the gradient again based on the parameter values updated in the first iteration and further adjust the parameters, and repeat this iterative process until the value of the objective function converges to a smaller value, at which point the obtained melt viscosity η opt and heat transfer coefficient h opt are the optimized parameters.

[0171] Through this matching and optimization process, this step can significantly improve the simulation accuracy of the melt flow state in the mold cavity. Accurate simulation results help to more deeply understand the actual flow behavior of the melt during the injection molding process, providing a reliable basis for subsequent accurate prediction of product quality and optimization of process parameters. In terms of product quality assurance, more accurate simulation can detect potential quality problems in advance, such as internal stress concentration and appearance defects in the product caused by uneven melt flow. By adjusting the process parameters in a timely manner, these problems can be effectively avoided, improving the yield rate of the product. The optimized melt viscosity and heat transfer coefficient provide a more accurate reference for optimizing the injection molding process, helping engineers to formulate more reasonable process plans, improve production efficiency, reduce production costs, and enhance the competitiveness of products in the market.

[0172] Step S3400, substitute the optimized melt viscosity η opt and the optimized heat transfer coefficient h opt into the generalized Newtonian fluid constitutive equation and the three-dimensional transient flow control equation, and perform numerical solution again to obtain an accurate three-dimensional transient flow field distribution v(x, y, z, t) that matches the actual flow state;

[0173] Specifically, the generalized Newtonian fluid constitutive equation and the three-dimensional transient flow control equation are the core mathematical models describing the flow and heat transfer behavior of polymer melts during the injection molding process. In previous calculations, due to certain errors in the melt viscosity η 0 and heat transfer coefficient h 0 , the simulated three-dimensional flow field distribution v 0 (x, y, z, t) deviated from the actual situation. The optimized melt viscosity η opt and heat transfer coefficient h opt are obtained by correcting the original parameters based on the actually measured flow velocity data (i.e., the x-axis flow velocity distribution V e (x)), which are more in line with the physical characteristics in the actual injection molding process.

[0174] When substituting the optimized parameters into the equation for re - solving, the same finite - volume discretization method and numerical algorithm as in step S3200 are adopted to perform high - resolution solution of the cavity flow field in time and space. The finite - volume discretization method divides the cavity space into multiple tiny control volumes, and solves the physical quantities within each control volume, thereby realizing the discretization of the entire cavity flow field. The numerical algorithm is used to iteratively solve the discretized algebraic equations, and by continuously updating the values of variables, the calculation results gradually approach the real flow field state. During this process, due to the use of more accurate parameters, the boundary conditions in the constitutive equation and control equation can better adapt to the actual process state. For example, at the junction of the melt and the mold wall, the optimized heat transfer coefficient h opt can more accurately describe the heat transfer situation, making the setting of boundary conditions more in line with the actual heat transfer process, and thus improving the accuracy of numerical simulation.

[0175] The melt flow field distribution v(x, y, z, t) obtained through this step highly coincides with the actual injection - molding process in terms of time - space scale. This accurate flow field distribution provides a solid data basis for subsequent defect prediction and quality control. In terms of defect prediction, based on the accurate flow field distribution, it is possible to more accurately judge whether the flow of the melt in the cavity is uniform and whether defects such as weld lines and bubbles will form in certain areas. For example, by analyzing the convergence of the melt in the flow field, the possible positions of weld lines can be predicted; by observing the flow velocity and pressure distribution, it can be determined whether bubbles will be generated due to the inability to discharge gas. In terms of quality control, the accurate flow field distribution helps to optimize process parameters, such as adjusting the injection pressure, temperature, etc., to ensure uniform flow of the melt in the cavity, reduce the generation of defects, and improve product quality. In addition, this accurate flow field distribution can also provide a reference for mold design, helping engineers to improve the mold structure, further enhance the stability of the injection - molding process and product quality, and improve the competitiveness of enterprises in the field of optical lens production.

[0176] Step S3500: According to the classification results of the flow anomaly mode, perform multi - physical - field coupling simulation on the accurate three - dimensional transient flow field distribution v(x, y, z, t) to obtain the multi - physical - field coupling simulation results.

[0177] Furthermore, step S3500 includes:

[0178] Step S3510: Under the first - level flow mode, through thermal - fluid - solid coupling analysis, obtain the temperature field T(x, y, z, t), pressure field P(x, y, z, t), stress field σ(x, y, z, t) and product deformation displacement field δ(x, y, z, t), constituting the first - level flow simulation results;

[0179] Step S3520, in the secondary flow mode, through thermo-fluid-stress coupling analysis, obtain the temperature field T(x, y, z, t), pressure field P(x, y, z, t), secondary flow velocity field v 2 (x, y, z, t) and stress field σ(x, y, z, t), which constitute the secondary flow simulation results;

[0180] Step S3530, in the tertiary flow mode, through thermo-fluid-stress-electromagnetic multi-field coupling analysis, obtain the temperature field T(x, y, z, t), pressure field P(x, y, z, t), tertiary flow velocity field v 3 (x, y, z, t), stress field σ(x, y, z, t) and electromagnetic induction intensity field E(x, y, z, t), which constitute the tertiary flow simulation results;

[0181] Step S3540, combine the primary flow simulation results, secondary flow simulation results and tertiary flow simulation results to form the multi-physical field coupling simulation results.

[0182] Specifically, step S3500 aims to perform multi-physical field coupling simulation on the accurate three-dimensional transient flow field distribution v(x, y, z, t) according to the classification results of the flow anomaly mode, so as to obtain the multi-physical field coupling simulation results and provide a key basis for subsequent process optimization. By considering the interactions between multiple physical fields, this step deeply reveals the behavioral characteristics of polymer melts under different flow anomaly modes, which is of great significance for improving the production quality of optical lenses.

[0183] In step S3510, for the primary flow mode, a thermal-fluid-solid coupling analysis method is adopted to obtain the temperature field T(x, y, z, t), pressure field P(x, y, z, t), stress field σ(x, y, z, t), and product deformation displacement field δ(x, y, z, t), which constitute the primary flow simulation results. Thermal-fluid-solid coupling analysis is an analytical method that comprehensively considers the interactions of heat conduction, fluid flow, and solid mechanics. During the injection molding process of an optical lens, when in the primary flow mode, the flow of the melt in the mold cavity is relatively stable. For example, when producing an ordinary circular optical lens, the melt fills the cavity at a relatively uniform speed. At this time, through thermal-fluid-solid coupling analysis, it is possible to accurately simulate the heat transfer, pressure distribution during the filling process, and the impact on the mold wall and the final product shape. By obtaining the temperature field T(x, y, z, t), it is possible to understand the temperature changes of the melt at different positions and times in the cavity, which is crucial for controlling the curing process of the product. Excessive or too low temperature may cause defects in the product, such as internal stress concentration and poor surface quality. The analysis of the pressure field P(x, y, z, t) helps to master the pressure distribution during the filling process of the melt, ensuring that the melt can uniformly fill all corners of the cavity and avoiding underfilling or overfilling. The stress field σ(x, y, z, t) reflects the magnitude and distribution of the stress on the product during the molding process, while the product deformation displacement field δ(x, y, z, t) is directly related to the final shape accuracy of the product. Through the comprehensive analysis of these physical fields, it is possible to predict in advance the quality problems that may occur in the primary flow mode, such as minor deformations or internal stress concentration areas, providing strong support for subsequent process optimization, helping to improve the product qualification rate and reduce production costs.

[0184] In step S3520, for the secondary flow mode, thermal-fluid-stress coupling analysis is used to obtain the temperature field T(x, y, z, t), pressure field P(x, y, z, t), secondary flow velocity field v 2 (x, y, z, t), and stress field σ(x, y, z, t), which constitute the secondary flow simulation results. Compared with the primary flow mode, the flow of the melt in the secondary flow mode begins to show a certain degree of instability. Thermal-fluid-stress coupling analysis focuses on the mutual influence among heat, flow, and stress. Taking the production of an optical lens with a certain curvature as an example, in the secondary flow mode, the flow velocity and direction of the melt may show some fluctuations. Through thermal-fluid-stress coupling analysis, it is possible to more deeply study the impact of these fluctuations on product quality. The secondary flow velocity field v 2(x, y, z, t) reflects the secondary flow characteristics of the melt on the basis of primary flow. This secondary flow may be caused by factors such as the interaction between the melt and the die wall, temperature differences, etc. Analyzing the secondary flow velocity field helps to detect local flow anomalies of the melt in the cavity, such as possible eddy currents or regions with uneven flow velocities. Combining the analysis results of the temperature field, pressure field, and stress field can provide a more comprehensive understanding of the physical state changes of the product during the molding process. For example, when it is found that a certain area has a high temperature and stress concentration, and at the same time the secondary flow velocity is large, it can be judged that there may be potential quality risks in this area, such as defects like bubbles and sink marks. By detecting these problems in advance, process parameters can be adjusted specifically, such as optimizing the temperature distribution, adjusting the injection pressure, etc., so as to effectively avoid or reduce the generation of product defects and improve the stability and consistency of product quality.

[0185] In step S3530, under the three - level flow mode, a thermal - flow - stress - electromagnetic multi - field coupling analysis is carried out to obtain the temperature field T(x, y, z, t), pressure field P(x, y, z, t), three - level flow velocity field v 3 (x, y, z, t), stress field σ(x, y, z, t), and electromagnetic induction intensity field E(x, y, z, t), which constitute the three - level flow simulation results. The flow instability degree of the melt under the three - level flow mode is relatively high, involving more complex physical phenomena. The thermal - flow - stress - electromagnetic multi - field coupling analysis comprehensively considers the interaction of various physical processes such as heat conduction, fluid flow, stress distribution, and electromagnetic induction. For example, when producing lenses with special optical performance requirements, electromagnetic effects may be introduced during the injection molding process to improve product quality. Through this multi - field coupling analysis, the influence of the electromagnetic induction intensity field E(x, y, z, t) on the melt flow and product molding can be studied. The three - level flow velocity field v 3 (x, y, z, t) further describes the velocity change of the melt under complex flow conditions, which contains more flow details and unstable factors. Through a comprehensive analysis of these physical fields, the molding process of the product under a highly unstable flow state can be understood more deeply. For example, when it is found that the electromagnetic induction intensity field is strong in a certain area, and at the same time the stress field and temperature field in this area also show anomalies, and the three - level flow velocity field presents complex changes, it can be judged that serious quality problems may occur in this area, such as uneven internal structure of the product and inconsistent optical performance. Based on these analysis results, more targeted process improvement measures can be taken, such as adjusting electromagnetic parameters, optimizing the die structure, etc., to ensure that the product can still meet the quality requirements under complex flow conditions, improve the yield rate of the product, and expand the applicable range of the production process for optical lenses.

[0186] Step S3540 forms a multi-physics field coupling simulation result from the primary flow simulation result, secondary flow simulation result, and tertiary flow simulation result. The multi-physics field coupling simulation result integrates multiple physical field information under different flow anomaly modes. Starting from multiple physical mechanisms such as heat conduction, pressure, flow control, curing shrinkage, residual stress, and electromagnetic induction, it comprehensively reveals the defect generation law of polymer melts under different flow anomaly modes. This is the theoretical basis for achieving accurate quality prediction and optimal control. By comparing the simulation results under different levels of flow modes, it can be clearly seen that as the flow instability increases, the change trends of each physical field and the complexity of their interactions. For example, from the primary to the tertiary flow mode, the non-uniformity of the temperature field may gradually increase, the fluctuations of the pressure field become more intense, and the distribution of the stress field becomes more complex. These changes are closely related to the generation of product defects. Based on the multi-physics field coupling simulation result, production enterprises can formulate corresponding preventive measures and process optimization plans in advance. For possible defects such as bubbles, sink marks, and deformations, the process parameters can be adjusted before production to avoid a large number of defective products during the production process and reduce production costs. At the same time, these results also provide data support and theoretical basis for the improvement and innovation of the optical lens production process, helping to promote the development of industry technology and improve the quality control level and production efficiency of the entire optical lens production industry.

[0187] Step S4000, according to the multi-physics field coupling simulation result and the process control strategy, triggers different process optimization strategies for the classification results of different flow anomaly modes.

[0188] Furthermore, step S4000 includes:

[0189] Step S4100, according to the multi-physics field coupling simulation result, obtains the real-time refractive index distribution n(x, y, z, t); according to the real-time refractive index distribution n(x, y, z, t), triggers different process optimization strategies for the classification results of different flow anomaly modes;

[0190] Furthermore, as Figure 5 shown, step S4100 includes:

[0191] Step S4110, inputs the multi-physics field coupling simulation result into a pre-trained refractive index prediction network to obtain the real-time refractive index distribution n(x, y, z, t);

[0192] Step S4120, determines the target refractive index distribution n tgt (x, y, z), calculates the deviation Δn(x, y, z, t) between n(x, y, z, t) and the target refractive index distribution n tgt (x, y, z), and performs double integration of Δn(x, y, z, t) in space and time to obtain the cumulative deviation amount Δntotal ;

[0193] Step S4130, set the deviation threshold θn, and according to Δn total and θn, for the classification results of different flow anomaly patterns, trigger different process optimization strategies.

[0194] For the first-level flow pattern, if Δn total < θn, then maintain the current process control strategy (ΔP pred , ΔT pred ) unchanged;

[0195] For the second-level flow pattern or the first-level flow pattern, if Δn total ≥ θn, then enter step S4200 to trigger the process parameter optimization based on global sensitivity analysis;

[0196] For the third-level flow pattern, then enter step S4300 to trigger a strong robust adaptive parameter regulation strategy.

[0197] Specifically, step S4100 aims to obtain the real-time refractive index distribution n(x, y, z, t) according to the multi-physics field coupling simulation results, and based on this distribution and the classification results of the flow anomaly patterns, trigger different process optimization strategies, so as to achieve precise quality control of the optical lens production process. This step links the physical quantity information obtained from the multi-physics field coupling simulation with the optical performance indicators, providing a clear direction and basis for process optimization.

[0198] In step S4110, input the multi-physics field coupling simulation results into a pre-trained refractive index prediction network to obtain the real-time refractive index distribution n(x, y, z, t). The refractive index prediction network here adopts a three-dimensional CNN (Convolutional Neural Network) architecture. The three-dimensional CNN architecture is a deep learning model specifically used to process three-dimensional data. It can automatically extract deep features from the input three-dimensional data through a combination of multiple convolutional layers, pooling layers, and fully connected layers. In this embodiment, the network can learn and extract the key features affecting the refractive index from the spatio-temporal distributions of the flow field, temperature field, and stress field. The principle is that the convolutional kernels in the convolutional layer slide on the three-dimensional data, and local features are extracted through convolutional operations. Different convolutional kernels can capture feature information of different scales and directions; the pooling layer is used to downsample the data, reducing the data volume while retaining the main features, improving the computational efficiency and generalization ability of the model; the fully connected layer integrates the features extracted previously and outputs the final prediction result.

[0199] When training the network, a large number of process-performance datasets are used. These datasets contain the flow field, temperature field, stress field data corresponding to optical lenses produced under different process conditions (such as different injection pressures, temperatures, melt viscosities, etc.), as well as the refractive index data obtained from actual measurements. Using the flow field, temperature field, and stress field data in these datasets as inputs and the corresponding refractive index data as the true labels, the network is trained. During the training process, the network continuously adjusts its own parameters (such as the weights of the convolutional kernels, the connection weights of the fully connected layers, etc.) to make the predicted refractive index distribution as close as possible to the true labels. For example, during the training process, the network will learn the influence rules of factors such as melt flow velocity and direction on the refractive index based on the input flow field information; from the temperature field data, it will master the relationship between temperature changes and the refractive index; and by learning the stress field data, it can understand how the stress distribution changes the optical properties of the material and thus affects the refractive index. In this way, the network establishes a mapping relationship between the flow state and the optical performance, thereby realizing real-time quality prediction. When the new multi-physical field coupling simulation results are input into the trained network, the network can quickly and accurately predict the real-time refractive index distribution n(x, y, z, t). This deep learning-based prediction method can capture the non-linear relationship between complex physical fields and the refractive index more accurately compared with traditional prediction methods based on empirical formulas or simple models, improving the accuracy and reliability of the prediction and providing more accurate data support for subsequent process optimization.

[0200] In step S4120, the target refractive index distribution n tgt (x, y, z) is determined by the product design model, which reflects the spatial distribution characteristics of the ideal optical performance. For example, for a specific optical lens, its design requirements specify specific refractive indices at different positions to achieve specific optical functions such as focusing and imaging. These specific refractive index distributions are the target refractive index distribution n tgt (x, y, z). The difference Δn(x, y, z, t) between the real-time predicted n(x, y, z, t) and n tgt (x, y, z) quantitatively characterizes the instantaneous deviation degree of the optical performance. If the value of Δn(x, y, z, t) is large, it indicates that there is a large difference between the optical performance of the product in the current production process and the ideal state, which may affect the final quality of the product. The double integral of Δn(x, y, z, t) over space and time gives the cumulative deviation amount Δn total , because the quality of the optical lens is an index that comprehensively considers spatial and time factors. Spatially, the refractive index deviations at different positions will all affect the overall optical performance; temporally, the changes in the refractive index during the entire injection molding process will also accumulate and affect the product quality. The Δn obtained through double integration totalIt is possible to more comprehensively evaluate the overall quality level of the product. For example, at a certain moment, the refractive index deviation in a certain area of the product may be small, but if the deviation in this area persists throughout the production process, the Δn obtained through double integration total will be large, indicating that there are relatively serious quality problems in this area of the product and process adjustments are needed. This method of evaluating product quality by calculating the cumulative deviation amount can more accurately reflect the actual situation of product quality and provide a more reasonable basis for subsequent process optimization.

[0201] Step S4130 sets the deviation threshold θn. According to Δn total and θn, different process optimization strategies are triggered for the classification results of different flow anomaly patterns. The deviation threshold θn is a reference value set based on the quality requirements of the product and production experience. For the first-level flow pattern, if Δn total < θn, the current process parameters remain unchanged. This is because in the first-level flow pattern, the material flow is relatively stable. When the cumulative deviation amount is small, it indicates that the current process parameters can meet the product quality requirements and no adjustment is needed. This can avoid unnecessary process changes, ensure the stability and consistency of the production process, and reduce production costs. For example, when producing a certain batch of optical lenses, the calculated Δn total is less than the set θn. At this time, continuing to maintain the current process parameters such as injection pressure and temperature can ensure the stable quality of the subsequent produced lenses and reduce the quality fluctuations that may be caused by parameter adjustment.

[0202] For the second-level flow pattern or in the first-level flow pattern, if Δn total ≥ θn (for example, 0.05, where 0.05 is an example value and can be determined according to the specific product and process in practice), step S4200 is entered to trigger the optimization of process parameters based on global sensitivity analysis. This is because when the cumulative deviation amount reaches a certain level, it indicates that the current process parameters may need to be adjusted to improve product quality. Global sensitivity analysis can determine which process parameters have a greater impact on product quality (measured by refractive index deviation here), so as to targetedly adjust these parameters. For example, through global sensitivity analysis, it is found that temperature has a greater impact on refractive index deviation, then the process parameters related to temperature, such as mold temperature and melt temperature, can be focused on adjusting to reduce refractive index deviation and improve the optical performance of the product. This data- and analysis-based process optimization method can find the optimization direction more efficiently compared with the traditional trial-and-error method, reduce the blindness of process adjustment, and improve production efficiency and product quality.

[0203] For the three - level flow pattern, step S4300 is entered to trigger a strong - robustness adaptive parameter regulation strategy. The flow instability degree is relatively high in the three - level flow pattern, and product quality problems are more serious, thus requiring more powerful regulation means. The strong - robustness adaptive parameter regulation strategy can quickly and flexibly adjust process parameters according to real - time production data, suppress the violent fluctuations of the flow, enable the production process to quickly return to stability, and ensure product quality. For example, in the three - level flow pattern, the flow state of the melt is monitored in real time through sensors. When significant fluctuations in the flow are detected, this strategy can quickly adjust parameters such as injection pressure and speed, making the melt flow return to relative stability, thereby avoiding serious defects in the product and increasing the qualified rate of the product. This method of adopting different process optimization strategies for different flow patterns and quality deviation situations can perform precise regulation according to the actual situation of the production process, effectively improve the production quality and efficiency of optical lenses, reduce the scrap rate, and enhance the competitiveness of the enterprise in the market.

[0204] Step S4200, process parameter optimization based on global sensitivity analysis;

[0205] Furthermore, step S4200 includes:

[0206] Step S4210, construct a quadratic response surface model based on the deviation Δn(x, y, z, t) and the multi - physical - field coupling simulation results;

[0207] As Figure 6 shown, furthermore, step S4210 includes:

[0208] Step S4211, perform global sensitivity analysis using the Sobol method to obtain the temperature sensitivity coefficient ST of the deviation Δn(x, y, z, t) with respect to the temperature field T(x, y, z, t), and the pressure sensitivity coefficient SP of the deviation Δn(x, y, z, t) with respect to the pressure field P(x, y, z, t);

[0209] Step S4212, with ST and SP as independent variables and Δn total as the dependent variable, fit a quadratic response surface model Δn total = f(ST, SP) through least - squares regression.

[0210] Step S4220, input P d (t) and T d (t), as well as (ΔP pred , ΔT pred ) into the quadratic response surface model to obtain the quality deviation prediction function Δn total (ΔP, ΔT) of the process parameter adjustment amounts ΔP and ΔT, where ΔP is the pressure optimization adjustment amount and ΔT is the temperature optimization adjustment amount;

[0211] Calculate P d (t) and T d (t) corresponding current pressure sensitivity coefficient SP 1 and current temperature sensitivity coefficient ST 1 ;

[0212] Superimpose the process control strategy (ΔP pred , ΔT pred ) with the current pressure sensitivity coefficient SP 1 and the current temperature sensitivity coefficient ST 1 to obtain the adjusted temperature sensitivity index ST adj and the adjusted pressure sensitivity index SP adj ;

[0213] Substitute ST adj and SP adj into the quadratic response surface model to obtain the quality deviation prediction value Δn total,adj .

[0214] Step S4230, obtain the quality target, calculate the quality deviation Δm between the quality deviation prediction value Δn total,adj and the quality target, and evaluate the optimization effect of the process control strategy (ΔP pred , ΔT pred ). If the optimization effect does not meet the requirements, further fine-tune the process control strategy (ΔP pred , ΔT pred ).

[0215] Specifically, step S4200 focuses on the optimization of process parameters based on global sensitivity analysis. Its purpose is to achieve precise adjustment of process parameters by deeply analyzing the influence of process parameters on product quality, thereby improving product quality. Sub-step S4210 aims to construct a quadratic response surface model based on the deviation Δn(x, y, z, t) and the multi-physics coupling simulation results. The Sobol method is a global sensitivity analysis method used to evaluate the influence degree of model input variables on output results. By calculating the Sobol index of variables, the contribution of each input variable to the output is quantified. In this embodiment, the temperature sensitivity coefficient ST of the deviation Δn(x, y, z, t) to the temperature field T(x, y, z, t) and the pressure sensitivity coefficient SP of the deviation Δn(x, y, z, t) to the pressure field P(x, y, z, t) are obtained using the Sobol method. These two coefficients are key indicators for quantifying the influence of temperature and pressure factors on the refractive index deviation in the entire cavity, reflecting the sensitivity of optical performance to temperature and pressure changes during the molding process. For example, if the temperature sensitivity coefficient ST is large, it indicates that a small change in the temperature field T(x, y, z, t) will have a large impact on the refractive index deviation Δn(x, y, z, t), meaning that the adjustment of temperature parameters needs to be focused on during process optimization. Taking ST and SP as independent variables and Δn total as the dependent variable, a quadratic response surface model Δn total = f(ST, SP) is fitted by least squares regression. Least squares regression is a commonly used curve fitting method. By minimizing the sum of the squares of the errors between the observed values and the model predicted values, the functional relationship that best represents the data trend is found. This quadratic response surface model establishes a non-linear mapping relationship between the optical quality deviation and the process parameter sensitivity (i.e., product quality), and can intuitively show how the temperature and pressure sensitivity coefficients affect the overall quality level of the product. For example, through this model, it can be clearly seen how the product quality deviation Δn total will change when the temperature sensitivity coefficient ST increases within a certain range, providing a theoretical basis for subsequent process parameter adjustment.

[0216] Sub-step S4220 inputs the previously obtained pressure fluctuation value sequence P d (t), the temperature gradient sequence T d (t), and the process control strategy (ΔP pred , ΔT pred ) obtained according to the flow-process mapping model into the quadratic response surface model, thereby obtaining the quality deviation prediction function Δn total (ΔP, ΔT) of the process parameter adjustment amounts ΔP and ΔT. This step comprehensively considers the current process state (reflected by P d (t) and T d (t)) and the preliminary process adjustment strategy (ΔPpred , ΔT pred ), further refining the prediction of product quality deviation and providing a more accurate basis for evaluating the effectiveness of process control strategies.

[0217] In sub-step S4230, obtain the quality target of the product and calculate the predicted value of quality deviation Δn total,adj The quality deviation Δm between the quality target, and use this to evaluate the optimization effect of the process control strategy (ΔP pred , ΔT pred ). If the optimization effect does not meet the requirements, further fine-tune the process control strategy (ΔP pred , ΔT pred ). For example, when the quality deviation Δm exceeds the allowable range, it indicates that the current process control strategy fails to effectively improve product quality. According to the quadratic response surface model and sensitivity analysis results, the pressure adjustment amount ΔP pred and the temperature adjustment amount ΔT pred need to be fine-tuned, such as appropriately increasing or decreasing the pressure adjustment amount and fine-tuning the amplitude of temperature adjustment, until the quality deviation Δm meets the quality target requirements.

[0218] The beneficial effects of this step are reflected in multiple aspects. From the perspective of product quality improvement, by accurately analyzing the influence of temperature and pressure on product quality and optimizing process parameters accordingly, it is possible to effectively reduce the quality deviation of products and improve the consistency and stability of the optical performance of optical lenses. In terms of production efficiency, it avoids the time waste and resource consumption caused by blindly adjusting process parameters. Based on scientific sensitivity analysis and model prediction for adjustment, it is possible to quickly find a better combination of process parameters and shorten the production cycle. From the perspective of cost control, it reduces the scrap rate caused by unqualified product quality and lowers production costs. In addition, this data-driven process optimization method provides strong support for the continuous improvement of the optical lens production process and helps enterprises gain an advantage in the market competition.

[0219] Step S4300, a strong robust adaptive parameter regulation strategy.

[0220] Specifically, step S4300 implements a strong robust adaptive parameter regulation strategy for the three-level flow pattern, which is a key link to ensure the production quality of optical lenses under complex flow conditions. In the three-level flow pattern, the flow of materials in the cavity is highly unstable, and traditional process regulation methods are difficult to effectively solve the problem. Therefore, a more intelligent and adaptive regulation strategy is needed.

[0221] In actual operation, based on the electromagnetic induction intensity field E(x, y, z, t) in the three-level flow pattern obtained in step S3530, the power spatial distribution of the induction heating system is optimized. The electromagnetic induction intensity field E(x, y, z, t) reflects the magnetic field intensity at different positions in the cavity under the action of electromagnetic induction. By analyzing this distribution, it is possible to determine which regions require more or less energy input, and then make targeted adjustments to the power spatial distribution of the induction heating system. For example, if the electromagnetic induction intensity is weak in a certain region, resulting in unstable material flow, the power of the induction heating system in this region can be appropriately increased to raise the material temperature, reduce the melt viscosity, improve the flow performance, and promote uniform material flow.

[0222] Performing high-frequency dynamic voltage regulation on the key parts with abnormal three-level flow is also an important measure in this step. The key parts usually refer to the regions where turbulence, large velocity gradients, or abnormal pressures are likely to occur during the flow process, and these parts have a significant impact on product quality. High-frequency dynamic voltage regulation means quickly adjusting the voltage within a short period to adapt to the complex flow requirements of the material at the key parts. For example, when it is monitored that the material flow rate at a certain key part is too fast, which may lead to defects, the voltage at this part is reduced through high-frequency dynamic voltage regulation to reduce the induction heating power, lower the material temperature, increase the melt viscosity, thereby slowing down the flow rate and stabilizing the flow state.

[0223] Establishing a thermal-fluid-stress-electromagnetic multi-field coupling control loop is the core of achieving strong robust adaptive parameter regulation. The multi-field coupling control loop correlates multiple physical processes such as heat conduction, material flow, stress distribution, and electromagnetic induction to form a closed-loop control system. In this system, the changes in each physical field are monitored in real time, and the process parameters are dynamically adjusted according to the monitoring data. For example, when it is detected that the change in the thermal field affects the flow state of the material, which in turn leads to abnormal stress distribution, the control loop will automatically adjust parameters such as the power and voltage of the induction heating system according to the multi-physical field coupling simulation results and the preset control strategy, change the thermal field distribution, thereby affecting the viscosity and flow velocity of the material, and finally adjust the stress distribution to restore the entire system to a stable state.

[0224] The beneficial effects of this step are remarkable. In terms of product quality assurance, by precisely regulating the complex flow state, the violent fluctuations of the flow are effectively suppressed, and defects caused by flow instability, such as bubbles, weld lines, internal stress concentration, etc., are reduced, improving the quality and yield rate of optical lenses. In terms of process stability, the multi-field coupling control loop realizes real-time monitoring and dynamic adjustment of the process, enhancing the anti-interference ability of the process. Even when production conditions fluctuate, the stability of product quality can be guaranteed. From the perspective of improving production efficiency, production interruptions and rework caused by frequent quality problems are avoided, and production efficiency is increased. At the same time, this advanced regulation strategy helps to promote the development of the optical lens production process towards intelligence and refinement, enhances the core competitiveness of enterprises, and provides new ideas and methods for the technological progress of the industry.

[0225] Example 2

[0226] Based on Example 1, this example provides a full-process quality monitoring device in the production process of optical lenses, such as Figure 7 shown, including:

[0227] Flow anomaly detection module: used to obtain the first deformation parameter set and the second excitation parameter set, perform wavelet packet decomposition on the first deformation parameter set, and extract the high-frequency components of the n1 layer as the flow front feature vector F f ; generate a flow vector matrix M according to the second excitation parameter set v ; establish a flow field classification rule according to the flow front feature vector F f and the flow vector matrix M v , and obtain the classification result of the flow anomaly pattern; the classification result of the flow anomaly pattern includes the first-level flow pattern, the second-level flow pattern, and the third-level flow pattern;

[0228] Process control strategy generation module: used to retrieve the historical production data set of n2 batches, and based on the historical production data set, construct a flow-process mapping model using a generative adversarial network; according to the flow front feature vector F f , the flow vector matrix M v and the trained flow-process mapping model, obtain the process control strategy;

[0229] Simulation module: used to obtain the melt viscosity η 0 in the mold cavity, and the heat transfer coefficient h 0 between the melt and the mold; based on η 0 and h 0 , obtain the three-dimensional flow field distribution v 0 (x, y, z, t) of the melt in the mold cavity; according to the three-dimensional flow field distribution v 0(x, y, z, t) to obtain the precise three-dimensional transient flow field distribution v(x, y, z, t); according to the classification results of the flow anomaly patterns, perform multi-physical field coupling simulation on the precise three-dimensional transient flow field distribution v(x, y, z, t) to obtain the multi-physical field coupling simulation results;

[0230] Process optimization module: According to the multi-physical field coupling simulation results and the process control strategy, trigger different process optimization strategies for different classification results of the flow anomaly patterns.

[0231] In the flow anomaly detection module, the obtaining of the first deformation parameter set and the second excitation parameter set includes:

[0232] Step S1110, within 0.5 s after the mold is closed, collect the surface deformation time series data through a distributed fiber Bragg grating array to generate the first deformation parameter set, and the first deformation parameter set includes the pressure fluctuation value sequence P d (t) and the temperature gradient sequence T d (t);

[0233] Step S1120, synchronously start the ultrasonic guided wave transmitter, apply a directional excitation wave along the melt flow direction, detect the reflected wave phase difference through the Doppler effect to generate the second excitation parameter set, and the second excitation parameter set includes the x-axis flow velocity distribution V e (x) and the y-axis shear stress distribution τ e (y).

[0234] In the flow anomaly detection module, the wavelet packet decomposition of the first deformation parameter set and the extraction of the high-frequency components of the n1 layer as the flow front feature vector F f include:

[0235] Step S1211, use the Daubechies8 wavelet basis function to perform 8-layer wavelet packet decomposition on the pressure fluctuation value sequence P d (t);

[0236] Step S1212, in the high-frequency subband coefficients of the n1 layer, extract the wavelet energy spectrum peak value and the corresponding scale index;

[0237] Step S1213, construct a 16-dimensional flow front feature vector F from the extracted wavelet energy spectrum peak value and the corresponding scale index f .

[0238] In the process control strategy generation module, the construction of the flow-process mapping model using the generative adversarial network based on the historical production data set includes:

[0239] Step S2210, the historical flow front feature vector F f , histand the historical flow vector matrix M v,hist As the input X, use the corresponding historical pressure optimization amount ΔP hist and the historical temperature optimization amount ΔT hist As the output Y, form the training data pair (X, Y);

[0240] Step S2220, build a flow-process mapping model, and train the flow-process mapping model according to the training data pair (X, Y);

[0241] The flow-process mapping model includes a generator G and a discriminator D, construct a generator loss function L G and a discriminator loss function L D , by minimizing the generator loss function L G and the discriminator loss function L D , alternately train the generator G and the discriminator D until reaching the Nash equilibrium.

[0242] In the simulation module, obtaining the three-dimensional flow field distribution v 0 (x, y, z, t) of the melt in the mold cavity includes:

[0243] Obtain the melt viscosity η in the mold cavity 0 , and the heat transfer coefficient h between the melt and the mold 0 ; The heat transfer coefficient h 0 is obtained by inverse calculation from the temperature gradient sequence T d (t) and the pre-constructed three-dimensional transient heat transfer equation;

[0244] Based on η 0 and h 0 , adopt the pre-constructed generalized Newtonian fluid constitutive equation and three-dimensional transient flow control equation, and numerically solve by the finite volume method to obtain the three-dimensional flow field distribution v 0 (x, y, z, t) of the melt in the mold cavity.

[0245] In the process optimization module, the triggering of different process optimization strategies includes:

[0246] Step S4110, input the multi-physical field coupling simulation results into the pre-trained refractive index prediction network to obtain the real-time refractive index distribution n(x, y, z, t);

[0247] Step S4120, determine the target refractive index distribution n tgt (x, y, z), calculate the deviation Δn(x, y, z, t) between n(x, y, z, t) and the target refractive index distribution n tgt (x, y, z), and perform double integration of Δn(x, y, z, t) in space and time to obtain the cumulative deviation amount Δn total ;

[0248] Step S4130, set the deviation threshold θn, and according to Δn total and θn, for the classification results of different flow anomaly patterns, trigger different process optimization strategies.

[0249] For the first-level flow pattern, if Δn total < θn, then maintain the current process control strategy (ΔP pred , ΔT pred ) unchanged;

[0250] For the second-level flow pattern or the first-level flow pattern, if Δn total ≥ θn, then enter Step S4200 to trigger the optimization of process parameters based on global sensitivity analysis;

[0251] For the third-level flow pattern, enter Step S4300 to trigger a strong robust adaptive parameter regulation strategy.

[0252] The methods and apparatuses of the present application may be implemented in many ways. For example, the methods and apparatuses of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of the steps for the method is only for illustration, and the steps of the method of the present application are not limited to the specific order described above, unless otherwise specifically stated.

[0253] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive elaboration.

[0254] As described above in the specific embodiments, the purpose, technical solutions, and beneficial effects of the present invention are further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A full-process quality monitoring method in the optical lens production process, characterized in that: The method comprises: Obtain the first deformation parameter set and the second excitation parameter set, perform wavelet packet decomposition on the first deformation parameter set, and extract the n1-layer high-frequency component as the flow front feature vector F f ; According to the second excitation parameter set, generate the flow vector matrix M v ; According to the flow front eigenvector F f and the flow vector matrix M v , establishing a flow field classification rule to obtain a classification result of an abnormal flow pattern; the classification result of the abnormal flow pattern includes a primary flow pattern, a secondary flow pattern and a tertiary flow pattern; The historical production data set of batch n2 is retrieved. Based on the historical production data set, a flow-process mapping model is constructed using a generative adversarial network. According to the flow front feature vector F f , flow vector matrix M v and the trained flow-process mapping model to obtain the process control strategy; Obtain the melt viscosity η0 in the mold cavity and the heat transfer coefficient h0 between the melt and the mold; based on η0 and h0, obtain the three-dimensional flow field distribution v0(x, y, z, t) of the melt in the mold cavity; according to the three-dimensional flow field distribution v0(x, y, z, t), obtain the accurate three-dimensional transient flow field distribution v(x, y, z, t); according to the classification results of the flow anomaly mode, perform multi-physics field coupling simulation on the accurate three-dimensional transient flow field distribution v(x, y, z, t) to obtain the multi-physics field coupling simulation results; According to the multi-physics field coupling simulation results and process control strategies, different process optimization strategies are triggered according to the classification results of different flow abnormality patterns.

2. The full-process quality monitoring method in the optical lens production process according to claim 1, characterized in that: The obtaining of the first deformation parameter set and the second excitation parameter set comprises: Within 0.5s after the mold is closed, the surface deformation time series data is collected by a distributed fiber Bragg grating array to generate a first deformation parameter set, which includes a pressure fluctuation value sequence P d (t) and temperature gradient sequence T d (t); The ultrasonic guided wave transmitter is started synchronously to apply a directional excitation wave along the melt flow direction, and the phase difference of the reflected wave is detected by the Doppler effect to generate a second excitation parameter set, wherein the second excitation parameter set includes an x-axis flow velocity distribution V e (x) and y-axis shear stress distribution τ e (y).

3. The full-process quality monitoring method in the optical lens production process according to claim 2, characterized in that: The first deformation parameter set is decomposed by wavelet packet to extract n1 layers of high-frequency components as the flow front feature vector F f include: The Daubechies8 wavelet basis function is used to calculate the pressure fluctuation value sequence P d (t) Perform 8-layer wavelet packet decomposition; extract the wavelet energy spectrum peak and the corresponding scale index from the high-frequency subband coefficients of the n1th layer; and construct a 16-dimensional flow front feature vector F with the extracted wavelet energy spectrum peak and the corresponding scale index. f ; The n1 is a positive integer, and its value range is [5,8].

4. The full-process quality monitoring method in the optical lens production process according to claim 2, characterized in that: The generated flow vector matrix M v Includes: x-axis velocity distribution V e (x) and y-axis shear stress distribution τ e (y) are respectively used as flow vector matrices M v The row and column vectors of .

5. The full-process quality monitoring method in the optical lens production process according to claim 1, characterized in that: The step of establishing a flow field classification rule and obtaining a classification result of an abnormal flow pattern comprises: When the flow front eigenvector F f ≥0.7 and the flow vector matrix M v When the principal component angle θ is less than 15°, it is determined to be a primary flow mode, and the principal component angle θ is M v The first principal component direction of When 0.4≤F f <0.7 and 15°≤θ<30°, it is determined to be a secondary flow mode; The remaining cases were judged as the third-level flow mode.

6. The method for full-process quality monitoring in the optical lens production process according to claim 1, characterized in that: The historical production data set includes historical flow field characteristics and corresponding process parameter optimization quantities; the historical flow field characteristics include historical flow front characteristic vectors F f , hist and the historical flow vector matrix M v,hist The process parameter optimization amount includes the historical pressure optimization amount ΔP hist and historical temperature optimization ΔT hist ; The use of a generative adversarial network to construct a flow-process mapping model includes: The historical flow front feature vector F f , hist and the historical flow vector matrix M v,hist As input X, the corresponding historical pressure optimization amount ΔP hist and historical temperature optimization ΔT hist As output Y, a training data pair (X, Y) is formed; a flow-process mapping model is built, and the flow-process mapping model is trained according to the training data pair (X, Y).

7. The full-process quality monitoring method in the optical lens production process according to claim 1, characterized in that: The process control strategy includes: If it is a secondary flow mode or a tertiary flow mode, the flow front eigenvector F f and the flow vector matrix M v Input into the trained flow-process mapping model to obtain the pressure adjustment ΔP pred and temperature adjustment ΔT pred ; If it is a primary flow mode, then ΔP pred =0, ΔT pred =0; ΔP pred and ΔT pred Composition process control strategy (ΔP pred , ΔT pred ).

8. The method for monitoring the quality of the entire optical lens production process according to claim 2, characterized in that: The heat transfer coefficient h0 is given by the temperature gradient sequence T d (t) is obtained by inverse calculation with the pre-constructed three-dimensional transient heat transfer equation; The method of obtaining the three-dimensional flow field distribution v0 (x, y, z, t) of the melt in the mold cavity includes: based on η0 and h0, using pre-constructed generalized Newtonian fluid constitutive equations and three-dimensional transient flow control equations, and numerically solving them through the finite volume method to obtain the three-dimensional flow field distribution v0 (x, y, z, t) of the melt in the mold cavity.

9. The full-process quality monitoring method in the optical lens production process according to claim 8, characterized in that: The method of obtaining an accurate three-dimensional transient flow field distribution v(x, y, z, t) according to the three-dimensional flow field distribution v0(x, y, z, t) includes: The x-axis velocity distribution V e (x) is matched with v0(x,y,z,t), η0 and h0 are corrected, and the optimized melt viscosity η is obtained opt and the optimized heat transfer coefficient h opt ; The optimized melt viscosity η opt and the optimized heat transfer coefficient h opt , substitute into the generalized Newtonian fluid constitutive equation and the three-dimensional transient flow control equation, re-solve numerically, and obtain the accurate three-dimensional transient flow field distribution v(x, y, z, t) that matches the actual flow state.

10. The full-process quality monitoring method in the optical lens production process according to claim 9, characterized in that: The x-axis velocity distribution V e (x) is matched with v0(x,y,z,t), and the corrections of η0 and h0 include: For the x-axis velocity distribution V e (x) and v0(x,y,z,t), in V e N matching points are extracted on the cross section y0-z0 of (x) to establish the least squares objective function of velocity deviation; The least squares objective function is iteratively optimized based on the gradient descent method to obtain the optimized melt viscosity η opt and the optimized heat transfer coefficient h opt .

11. The method for monitoring the quality of the entire optical lens production process according to claim 1, characterized in that: The classification results of different flow abnormality modes trigger different process optimization strategies including: Input the multi-physics field coupling simulation results into the pre-trained refractive index prediction network to obtain the real-time refractive index distribution n(x, y, z, t); Determine the target refractive index distribution n tgt (x,y,z), calculate n(x,y,z,t) and target refractive index distribution n tgt The deviation of (x, y, z) is Δn(x, y, z, t), and Δn(x, y, z, t) is double integrated in space and time to obtain the cumulative deviation Δn total ; Set the deviation threshold θn according to Δn total and θn, the classification results of different flow abnormality patterns trigger different process optimization strategies.

12. The method for monitoring the quality of the entire optical lens production process according to claim 11, characterized in that: According to Δn total and θn, and the classification results of different flow abnormality patterns trigger different process optimization strategies including: For the primary flow mode, if Δn total <θn, then maintain the current process control strategy (ΔP pred , ΔT pred )constant; For the secondary flow mode or the primary flow mode, if Δn total ≥θn, the process parameter optimization based on global sensitivity analysis is triggered; For the third-level flow mode, the adaptive parameter control strategy is triggered.

13. A full-process quality monitoring device in an optical lens production process, which is used to implement the full-process quality monitoring method in an optical lens production process according to any one of claims 1 to 12, characterized in that: The device comprises: Flow anomaly detection module: used to obtain the first deformation parameter set and the second excitation parameter set, perform wavelet packet decomposition on the first deformation parameter set, and extract the n1 layer high-frequency component as the flow front feature vector F f ; According to the second excitation parameter set, generate the flow vector matrix M v ; According to the flow front eigenvector F f and the flow vector matrix M v , establishing a flow field classification rule to obtain a classification result of an abnormal flow pattern; the classification result of the abnormal flow pattern includes a primary flow pattern, a secondary flow pattern and a tertiary flow pattern; Process control strategy generation module: used to retrieve the historical production data set of batch n2, and build a flow-process mapping model based on the historical production data set using a generative adversarial network; according to the flow front feature vector F f , flow vector matrix M v and the trained flow-process mapping model to obtain the process control strategy; Simulation module: used to obtain the melt viscosity η0 in the mold cavity and the heat transfer coefficient h0 between the melt and the mold; based on η0 and h0, obtain the three-dimensional flow field distribution v0(x, y, z, t) of the melt in the mold cavity; according to the three-dimensional flow field distribution v0(x, y, z, t), obtain the accurate three-dimensional transient flow field distribution v(x, y, z, t); according to the classification results of the flow anomaly mode, perform multi-physics field coupling simulation on the accurate three-dimensional transient flow field distribution v(x, y, z, t) to obtain the multi-physics field coupling simulation results; Process optimization module: According to the multi-physics field coupling simulation results and process control strategies, different process optimization strategies are triggered according to the classification results of different flow abnormality patterns.

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