418results about "Registering/indicating quality control systems" patented technology

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention belongs to the technical field of film quality analysis and evaluation, and particularly relates to a flexible circuit board film pressing quality detection and analysis method and system. The method comprises the steps of generating a self-adaptive scanning path based on a design file and surface deformation data; performing multi-modal data acquisition and fusion along a path through optical surface scanning, ultrasonic tomography and an optical coherence tomography imaging assembly; potential defects are positioned, and size, shape, position and defect type probability values are quantized; judging the authenticity of the defect by adopting a three-stage mechanism of cross-modal probability comparison, physical characteristic logic verification and multi-frame time sequence verification; and performing weighted scoring on the real defect matching acceptance standard table, and performing disqualification marking or outputting a process optimization suggestion to the pressing equipment according to a scoring result. According to the invention, the imaging limitation of the transparent coating layer is broken through, the internal interface layering and the precise detection of the microbubbles are realized, and the closed-loop control from detection to production adjustment is formed.

The invention discloses a quality detection system and method for a glue injection laser contour, and belongs to the technical field of quality detection, and the method comprises the steps: obtaining a reflection point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color grey-scale map, and achieving the visual representation of the surface texture of a glue layer. A three-layer feature extractor is constructed, a quality primary judgment method is set, features of different scales are fused in a cross-layer mode, the problem of missing detection of complex defects in traditional single-feature detection is solved, basic defects are automatically recognized, and a quality primary judgment result is output; and performing defect judgment on the glue-injected finished product which is unqualified in primary judgment, setting a defect fine judgment method, performing voxel-level segmentation on the three-dimensional image, calculating defect characteristics, and judging whether the defects affect the use performance of the product or not so as to generate a fine judgment result, so that accurate positioning of complex defects is realized, and the defect type identification accuracy is improved.

The invention provides a copper wire stretching quality detection method and system for elongation analysis. The method comprises the following steps: obtaining production process record data of a copper wire sample meeting an elongation threshold; on the basis of a preset production process, traversing the production process record data for comparison to obtain a production process deviation parameter; according to the deviation parameters, extracting record data with the deviation parameters smaller than or equal to a threshold value for centralized value evaluation to obtain a production process reference space; when the production process monitoring data of the to-be-detected copper wire falls into the reference space, judging that the copper wire is qualified; when the monitoring data does not fall into the reference space, extracting a single-attribute deviation matrix; and retrieving an elongation threshold value of an abnormal sample meeting the deviation matrix, and executing targeted tensile quality actual measurement. By establishing a production process reference space, accurate prediction and targeted detection of the drawing quality of the copper wire are realized, and the omission ratio is effectively reduced.

The invention discloses a hard alloy cutter welding visual inspection data intelligent management method, and relates to the technical field of welding quality detection, and the method comprises the following steps: obtaining image data in a welding process through a visual inspection system; preprocessing the image data; analyzing defect characteristics of a welding joint area based on an image recognition algorithm, and recognizing a welding quality problem; generating a defect report based on the identified defect features; establishing a welding quality prediction model, and automatically generating a quality evaluation result; and storing the data to the cloud. Through high-resolution image capture and automatic defect analysis based on an image recognition algorithm, defects, such as cracks, air holes and weld irregularity, of a welding joint area can be accurately recognized in real time, the detection precision and efficiency are greatly improved, personal errors are reduced, a welding quality prediction model is established based on a machine learning algorithm, and the welding quality prediction accuracy is improved. Therefore, the quality of the current welding operation is predicted, and the reliability of the detection process is improved.

The invention relates to the technical field of surface defect detection, and discloses a gearbox gear defect detection method. The method comprises the steps of collecting a full-surface image of a gear, performing multi-layer feature extraction through image preprocessing in combination with a convolutional neural network, performing gridding segmentation and defect probability calculation through a Bayesian discrimination model, performing defect detection and classification on a defect-prone area and a defect-non-prone area by adopting a single-category model and a multi-category model respectively, and obtaining a defect-prone area and a defect-non-prone area. Generating a defect report; compared with the problems that in the prior art, manual detection is relied on, the speed is low, precision is insufficient, and particularly under the condition of a high-speed production line or uneven illumination, a traditional detection technology is difficult to effectively recognize small defects, real-time and high-precision gear surface defect detection is achieved by combining automatic detection based on deep learning, and the detection efficiency is improved. Therefore, the problems of missing detection and false detection in manual detection are avoided, the detection efficiency and accuracy are remarkably improved, and the method is suitable for a modern automatic production line.

The invention provides a glass processing dynamic monitoring method and system based on multi-source data fusion, and relates to the field of industrial artificial intelligence, the method comprises the following steps: continuously collecting sound signals generated during processing, and carrying out noise reduction processing on the obtained sound signals to obtain sound data after noise reduction; continuously monitoring the acoustic data after noise reduction, acquiring abnormal acoustic characteristics of the glass in the processing process, determining an accurate time point of the abnormal acoustic characteristics, and acquiring time information of the abnormal acoustic characteristics; acquiring a glass processing position at the time of the abnormal acoustic features according to the time information of the abnormal acoustic features, and acquiring image or video information of a glass processing area within the time range; according to the obtained abnormal acoustic characteristics and the image or video information of the glass processing area, whether defect information appears in the glass processing process or not is judged, so that real-time dynamic monitoring and defect accurate positioning can be realized in the glass processing process, and specific process parameter adjustment suggestions are generated.

The invention provides a filling process prediction method and device based on a discrete element method and data driving, and relates to the technical field of bulk material forming, and the method combines the physical modeling advantage of the discrete element method and the powerful prediction capability of a data driving method. The filling process of the granular material under different process parameters is simulated through a discrete element method, key quality indexes are obtained to serve as input of a data driving model, and the process parameters serve as output for training. The model not only can accurately predict the filling quality, but also can adjust the process parameters in real time according to the target quality index, so that the intelligent optimization of the filling process is realized. According to the method, the calculation efficiency is greatly improved, the generalization ability of the model is enhanced, the dependence on empirical data is reduced, a more efficient and accurate solution is provided for the filling process in the industries of building materials, pharmacy, powder metallurgy and the like, the product quality is improved, the production cost is reduced, and the research, development and application of novel materials and equipment are accelerated.

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.

The invention relates to the technical field of intelligent analysis of aluminum oxide, in particular to an intelligent data analysis system and method based on aluminum oxide production. Acquiring ball mill parameters, raw material parameters and a spherical alumina impurity threshold, and determining a standard threshold according to the ball mill parameters and the raw material parameters; sampling and detecting the processed spherical aluminum oxide to obtain a purity and morphology image set of the spherical aluminum oxide; constructing a morphology image segmentation model to segment the morphology image set to obtain a first image set and a second image set; constructing a first image recognition model to recognize the first image set to obtain a spherical alumina parameter set, and calculating to obtain a first coefficient; constructing a second image recognition model to recognize the second image set to obtain an impurity parameter set, and calculating to obtain a second coefficient; and calculating the quality coefficient according to the first coefficient, the second coefficient, the spherical alumina impurity threshold and the spherical alumina purity. The comprehensive evaluation of the spherical aluminum oxide is realized through the quality coefficient of the spherical aluminum oxide.

The invention relates to the technical field of double-sided adhesive tape production, and discloses a double-sided adhesive tape processing method, which comprises the following steps: obtaining base material raw paper and an adhesive raw material, processing to form a pretreatment material set, and outputting an initial process parameter configuration table; generating a processing flow update instruction set according to the dynamic adjustment signal, and determining target processing parameters through hierarchical disassembly classification; and outputting a targeted process execution scheme in combination with the equipment operation information set. According to the method, a multi-dimensional process vector matching historical database is constructed by extracting raw paper gram weight characteristics, adhesive viscosity characteristics and drying temperature characteristics, and accurate parameter configuration is realized. And potential defects are further identified based on a real-time monitoring data curve, the parameter priority is dynamically adjusted, and the optimization process is judged through statistics and time sequence stability. The method solves the problems of parameter curing, defect response lag and insufficient equipment collaboration in a traditional method, and improves the production efficiency and quality stability of the double-sided tape.

The invention relates to the technical field of injection molding production process monitoring, and discloses an injection molding production monitoring method, device and system.The injection molding production monitoring method comprises the steps that multi-source sensing data is collected, and pressure time sequence data and material spectrum data in the injection molding process are collected; extracting pressure waveform features to obtain a complete pressure waveform feature set; performing material characteristic analysis, and forming a material characteristic fingerprint in combination with the pressure waveform characteristics and the spectral data; material batch similarity calculation: constructing a material batch fingerprint database, and calculating the similarity between the new batch of materials and the historical batch of materials; process parameter optimization: continuously improving the accuracy of parameter optimization through multi-objective tradeoff optimization and parameter verification and iterative optimization; quality problem root causes are analyzed, and material problems and process parameter problems are accurately distinguished; through fusion of pressure waveform analysis and a material characteristic monitoring technology, comprehensive monitoring and intelligent regulation and control of an injection molding production process are realized.

The invention relates to the technical field of image processing, in particular to an intelligent detection method and system for support turning. The method comprises the steps that sensor signals of the support in the turning machining process are obtained in real time; constructing a sensor topological graph; extracting real-time time sequence process features reflecting the dynamic response of the tool-workpiece-machine tool system in the sensor topological graph; inputting the real-time time sequence process features into a trained multi-task prediction model, and outputting prediction deviation values of a plurality of geometric dimensions of the support and a prediction surface defect mask pattern; and if all the predicted deviation values are within the corresponding tolerance threshold values and the ratio of the defect area of the predicted surface defect mask pattern to the total area of the bracket is smaller than a set area threshold value, judging that the expected quality state of the bracket is qualified. According to the scheme, the expected quality state of the support product can be comprehensively judged in real time.

The invention belongs to the technical field of printing process optimization, and discloses a printing process optimization decision-making system based on big data. Comprising an image acquisition module used for acquiring a printed product image; the feature extraction module is used for analyzing the printed product image and extracting product feature data; the quality detection module is used for analyzing the product feature data and identifying quality defect problems; the reason tracing module traces the reason of the quality defect according to the quality defect problem; the process optimization module is used for dynamically optimizing printing process parameters based on quality defect reasons; according to the method, the advantages of advanced technologies such as computer vision, machine learning and intelligent optimization can be fully played, intelligent monitoring and dynamic optimization of the whole printing production process are achieved, the method can rapidly adapt to the changing production environment, the quality defect problem in printed products is reduced, and the printing quality and the production efficiency are improved.

The invention relates to the technical field of surface defect detection, and discloses an aluminum profile surface defect analysis method based on machine vision, and the method comprises the steps: obtaining equipment operation parameters, environment parameters, aluminum bar processing procedure parameters and aluminum profile surface snapshots of processing equipment of a target aluminum profile in real time; obtaining a corresponding equipment operation risk value according to the equipment operation parameter; according to the aluminum bar machining procedure parameters, the equipment operation risk values and the environment parameters, procedure score values of the corresponding aluminum bar machining key procedures are obtained; the aluminum profile surface snapshots, obtained from multiple angles, of the target aluminum profile are combined and then sent to the pre-trained surface recognition model for evaluation, the quality inspection defect value output by the surface recognition model is obtained, and the surface quality defects can be continuously, accurately and rapidly found in a non-contact detection mode. The aluminum profile production efficiency and quality are fully improved, and compared with manual detection, a machine vision system has lower cost and higher efficiency.

The present invention belongs to the technical field of aviation component mold production, and in particular, relates to a PIID coating detection method for aviation component molds, and the steps are as follows: full coverage scanning and detection of the surface of the aviation component mold; comprehensive detection of various defects according to different requirements of detection sensitivity and resolution for different coating defect types; training a large number of qualified and unqualified coating sample data to obtain a discriminant model for coating performance, inputting the coating data obtained by the detection into the discriminant model, and quickly and accurately identifying unqualified coatings. The present invention uses machine vision to obtain three-dimensional point cloud data and reconstruct the model, determine the optimal detection path, and achieve full coverage scanning; use machine learning to train the discriminant model to achieve rapid and accurate identification of coating quality, effectively solving key problems such as blind spot elimination, comprehensive defect detection and performance evaluation in mold coating detection, and significantly improving detection efficiency and accuracy.

The invention relates to a multi-dimensional aging test data analysis system and method, and aims to realize real-time acquisition and intelligent analysis of multiple performance indexes in the aging process of electronic products. The system comprises an information acquisition module, a test scheduling module, a data processing module, an analysis module, an abnormity judgment module, a report module and a communication module. Cooperative work among the modules is realized based on task driving and data circulation, accurate association of test data is realized through bar code binding, and unified analysis is performed in combination with multi-source information such as USB read-write performance, network transmission state and display resolution. The system realizes multi-task concurrent acquisition through a resource-aware scheduling strategy, and the analysis module identifies performance abnormity and outputs a structured report by combining preprocessing, feature extraction and time sequence prediction. According to the method, the test automation level and the anomaly recognition accuracy are improved.

The invention relates to the technical field of intelligent manufacturing and industrial digital twinning, in particular to a latex paint packaging full-process digital twinning method and system. The method comprises the following steps: collecting multi-source data in real time through a sensor network, and preprocessing to form a standard data stream; a coupled digital twinborn model fusing material characteristics and process parameters is constructed, and real-time prediction is realized by combining physical laws and data driving residual correction and online parameter self-adaption; constructing a multi-scale abnormal index by comparing model output with actual data, identifying an abnormal mode by adopting PCA, calculating local and overall health indexes, and predicting a trend; and generating a preliminary control strategy according to the abnormal mode and the health trend, forming closed-loop adaptive control in combination with health state weighted optimization and look-ahead adjustment, and issuing execution and feeding back an update strategy. According to the invention, the quality control qualification rate and the energy-saving efficiency of the whole process of emulsion paint packaging are improved.

The invention relates to the technical field of carbon fiber mold optimization, and discloses a carbon fiber box mold optimization method and system based on multiple targets. A multi-target definition library of the system stores a design constraint set composed of a geometric accuracy threshold value, a material strength threshold value and thermal deformation tolerance; the real-time sensing network collects data of layering tension distribution, temperature gradient and resin flow front edge; the topological dependency analyzer constructs a dependency relationship graph of the design parameters and the real-time sensing data; the material characteristic library stores carbon fiber layering parameters and resin curing kinetic parameters; the optimization calculation scheduler dynamically allocates multi-target optimization calculation task nodes; an iterative optimization engine generates a layering path sequence and curing temperature curve combination scheme; and the result verifier compares the combination scheme with the design constraint, and outputs a scheme deviation index. According to the system, the design standardization, the production monitoring comprehensiveness and the optimization verification accuracy of the carbon fiber box mold can be improved, and the product quality and the production stability are guaranteed.

The invention discloses a semiconductor product measurement method and device, a storage medium and electronic equipment, and relates to the technical field of semiconductor equipment, and the method comprises the steps: obtaining a processing strategy adopted by a target wafer batch at a process site if it is monitored that the target wafer batch enters a measurement site, the processing strategy comprises a combination of one or more seed devices and formula groups; judging whether an invalid measurement combination exists in the processing strategy or not; and if the invalid measurement combination exists, carrying out wafer drawing measurement on the target wafer batch according to an intelligent wafer drawing rule, the intelligent wafer extraction rule comprises preferentially extracting the wafers processed by adopting the invalid measurement combination, preferentially extracting the wafers processed by adopting the invalid measurement combination with shorter measurement validity period, and extracting the invalid measurement combination with the maximum coverage of the extracted wafers. According to the wafer drawing measurement method and device, wafer drawing measurement with higher efficiency and higher coverage can be realized, and the monitoring and positioning accuracy and efficiency of process abnormality in the wafer processing process are improved.

The invention discloses a dynamic optimization method and system for a boron diffusion process of a photovoltaic cell and electronic equipment. Comprising the following steps: collecting process parameters and sheet resistance data, and associating the process parameters and the sheet resistance data to form a process parameter set; optimizing a process parameter set, removing noise data and incomplete data, and extracting an available feature data set; dividing the feature data set into a training set and a verification set, and training to obtain a nonlinear regression model for predicting resistance data under different feature data; carrying out validity verification on the nonlinear regression model, and constructing an optimization function to reduce a difference value between a predicted value and an actual value; and deploying a nonlinear regression model in the diffusion process, adjusting the process parameters of the diffusion process in real time according to the predicted value, and optimizing the obtained sheet resistance data. The problem that the boron diffusion process is difficult to accurately adjust by a traditional process control method can be solved.

The invention discloses an optimization method and system for a 42CrMo steel heat treatment process based on digital twinning, and the method comprises the steps: obtaining historical data of 42CrMo steel in each stage of the heat treatment process, and constructing a corresponding digital twinning model based on each cooling rate, deploying all the digital twin models at specified positions in a preset virtual space; acquiring a real-time data packet of the 42CrMo steel in the heat treatment process, and dividing the real-time data packet into a plurality of sub-data packets according to different cooling rates; determining a target time sequence identifier corresponding to each sub-data packet according to the cooling rate, and transmitting the target time sequence identifier to a corresponding target digital twin model; the target digital twin model runs the sub-data packets and generates corresponding optimization information; and detecting the current visual field range of the user, and guiding the user to adjust the visual field until the target digital twinborn model can be seen. By means of the digital twinborn model, accurate control over the cooling rate can be achieved, and therefore the abrasion resistance and hardness of the 42CrMo steel are optimized.

The invention provides a glass production line cold end stress detection system and method. Belongs to the technical field of glass production detection. The method comprises the following steps: carrying out stress-related spectral data acquisition on a cold end area of the glass production line through a multispectral stress sensing array, and preprocessing the acquired spectral data to obtain a preprocessed spectral data set; spectral feature extraction is carried out on the preprocessed spectral data set, and a spectral feature vector set is constructed; based on the spectral feature vector set, utilizing a space-time correlation analysis algorithm to extract space-time features of glass cold end stress to obtain a stress space-time feature matrix; spectral data are collected through the multispectral stress sensing array, the influence of traditional contact type detection on glass products and a production line is avoided, and the detection precision and the real-time performance are improved.

The present invention discloses a new energy welding quality detection method and system, which relates to the field of new energy welding technology. The present invention includes acquiring a welded part image, performing grayscale processing, extracting welding edges, and using Hough linear transformation to extract welding lines and potential defect edges; gradient and sharpness calculation; skeleton extraction and defect feature calculation; defect manifestation degree evaluation; defect classification and quality judgment; comprehensive defect evaluation; unqualified joint processing and fault analysis; generating visual detection results including defect area marking and severity annotation. The present invention identifies and evaluates the surface defects of welded joints through multi-step image processing and feature extraction technology, thereby improving the accuracy and reliability of new energy welding quality detection; using multi-dimensional indicators such as defect manifestation degree, surface defect coefficient, defect area change rate and defect position concentration, comprehensively evaluate welding quality and effectively distinguish between minor, medium and severe defects.

The invention discloses a fruit juice beverage production monitoring and early warning method and device, and relates to the technical field of food production, and the method comprises the steps: carrying out the correlation division of production nodes on a fruit juice beverage production line, and obtaining a heat and oxidation abnormal correlation node set; configuring a temperature sensor array at each thermal association node to monitor real-time temperature-time data and generate a first early warning signal; and monitoring dissolved oxygen data, a material turbulence state and pipeline pressure at each oxidation associated node, establishing an oxidation intake map sequence, predicting the total oxidation intake and distribution uniformity, and analyzing the influence of oxidation on quality indexes to generate a second early warning signal. The technical problems that in the fruit juice beverage production process, heat imbalance can damage raw material nutrition and affect the sterilization effect, and oxidation can cause color and luster deterioration, taste decline and shelf life shortening are solved, and accurate monitoring, abnormity analysis and early warning of quality related key factors in the fruit juice beverage production process are achieved; and the stable product quality is effectively guaranteed.

The invention discloses an MES-based aluminum alloy casting production management method and system, and relates to the technical field of production management, and the method comprises the steps: firstly, synchronously collecting time sequence data streams of core process parameters such as pressure, speed, position and the like, and carrying out the multi-channel data alignment; and a deep learning auto-encoder model is introduced to carry out nonlinear dimension reduction and feature extraction on high-dimensional data, and reconstruction errors are utilized to realize real-time evaluation and anomaly detection of the quality of the casting process. And for the abnormal state exceeding the preset threshold value, key influence factors causing quality fluctuation are further traced through an intelligent diagnosis mechanism, autonomous decision is made based on a diagnosis result, an optimal parameter adjustment strategy is formed, and finally a closed-loop control instruction is generated to drive the production equipment to perform adaptive adjustment. In this way, the problem of unstable quality caused by parameter fluctuation in the casting process can be effectively solved, and the stability and the intelligent level of the aluminum alloy casting process are remarkably improved.

The invention discloses a coating quality prediction and optimization method and system based on multi-modal data fusion, and the method comprises the steps: S1, fusing coating parameters, environment data, equipment states and historical quality data, and generating a global feature matrix; s2, inputting the matrix into a GBDT model containing physical prior constraints, and outputting a quality risk probability and a key index; s3, target parameters are generated through a genetic algorithm, Q-Learningg and Pareto screening; s4, performing closed-loop feedback on updated data; s5, cross-line adaptation is achieved through transfer learning and domain adaptation; and S6, iteratively training the model in batches. The system comprises a data fusion module, a quality prediction module, a parameter optimization module, a closed-loop control module, an overline adaptation module and a self-evolution module. The method aims at solving the problems that traditional coating overline parameters are difficult to reuse, small sample modeling cost is high, prediction is separated from process logic, and parameter adjustment lags, so that overline adaptability and prediction stability are improved, small sample cost and energy consumption are reduced, and the method is suitable for high-end coating scenes such as automobiles and aviation.

The invention discloses a distribution transformer load loss prediction method and system based on pilot-scale test monitoring data. The method comprises the following steps: collecting pilot-scale test data of a distribution transformer load loss related variable; preprocessing the collected pilot plant test data, and performing correlation analysis on the preprocessed variable data to obtain an independent variable in a distribution transformer finished product load loss prediction function; performing multiple regression analysis on the selected independent variables to obtain a regression model, correcting the regression model to obtain a distribution transformer finished product load loss prediction function, and verifying the load loss prediction function; and performing load loss prediction on the distribution transformer finished product based on the verified load loss prediction function. According to the method, the predicted value of the load loss can be quickly obtained by analyzing pilot test data, the time and the cost of a traditional experiment are reduced, and the calculated load loss prediction precision can be effectively improved by analyzing variables and verifying functions.

The invention discloses an injection molding product quality online prediction method. The method comprises the following steps of S100, obtaining time sequence parameters and non-time sequence parameters which influence the final size of a product in the product injection molding process; s200, performing multi-stage division on the time sequence parameters according to injection molding process characteristics, and constructing a multi-stage fusion model to perform local-stage feature fusion extraction on the time sequence parameters to obtain global size features; s300, deeply mining non-time-sequence features in the non-time-sequence parameters by using the convolutional network; and S400, fusing the features obtained in the step S200 and the step S300, and constructing a multi-stage fusion regression model for predicting the final size of the product. The method has the beneficial effects that the whole technological process is divided into different stages, the independent local model is constructed for each specific stage, the characteristics of each stage can be more accurately extracted and explained through the staged processing mode, and then the accuracy of quality prediction of the injection molding product can be effectively improved.