Residual stress monitoring system for laser fuse 3D printing based on ultrasonic stress release

Abstract

The present invention relates to the field of stress monitoring technology, and discloses a laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release. The system includes: a signal processing module, which is used to couple the signal data of the ultrasonic sensor, the temperature data of the temperature sensor and the deformation data of the laser fuse 3D printing to obtain the residual stress characteristics; a stress analysis module, which is used to calculate the stress field of the residual stress characteristics to obtain local stress distribution data and global stress field distribution data; a multi-scale analysis module, which is used to couple the scales according to the local stress distribution data and the global stress field distribution data to obtain the scale analysis results; and an adaptive adjustment module, which is used to adaptively calculate the printing parameters according to the scale analysis results. The present invention can improve the structural stability of the existing 3D laser fuse printing on the printed part.

Description

Technical Field

[0001] The present invention relates to the technical field of stress monitoring, and in particular to a laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release. Background Art

[0002] In modern industrial manufacturing, 3D printing technology (Additive Manufacturing) has attracted widespread attention due to its high degree of freedom, high efficiency and material utilization. Especially in the fields of aerospace, medical equipment and automobile manufacturing, the production demand for complex parts has given rise to higher requirements for printing accuracy, quality and stability. However, due to the material properties and thermal coupling during the printing process, the generation of residual stress is inevitable, which has become an important challenge affecting the performance and reliability of printed parts.

[0003] In the traditional laser fuse 3D printing process, the uneven heat input and cooling speed between layers will produce various residual stresses such as thermal stress and mechanical stress. These residual stresses will cause deformation, cracks and even structural failure of the printed parts. In addition, the traditional printing process mainly focuses on printing accuracy and surface quality, and lacks accurate prediction and dynamic control of residual stress. In summary, the existing technology has the problem of low structural stability of 3D laser fuse printing on printed parts. Summary of the invention

[0004] The present invention provides a laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release, the main purpose of which is to solve the problem of low structural stability of 3D laser fuse printing on printed parts in the prior art.

[0005] To achieve the above-mentioned purpose, the laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release provided by the present invention includes: a signal processing module, a stress analysis module, a multi-scale analysis module, and an adaptive adjustment module, specifically:

[0006] A signal processing module is used to obtain signal data of an ultrasonic sensor, temperature data of a temperature sensor, and deformation data of laser fuse 3D printing, and couple the signal data, the temperature data, and the deformation data to obtain residual stress characteristics;

[0007] A stress analysis module is used to perform stress field calculation on the residual stress characteristics to obtain local stress distribution data and global stress field distribution data;

[0008] A multi-scale analysis module, used for performing scale coupling according to the local stress distribution data and the global stress field distribution data to obtain a scale analysis result;

[0009] The adaptive adjustment module is used to adaptively calculate printing parameters according to the scale analysis results, and generate control instructions according to the calculated printing parameters.

[0010] Optionally, when the signal processing module performs data coupling of the signal data, the temperature data and the deformation data to obtain the residual stress characteristics, it is specifically used to:

[0011] Performing ultrasonic signal processing on the signal data to obtain ultrasonic signal processing features;

[0012] Performing time series analysis on the temperature data to obtain temperature transformation data;

[0013] Performing strain characteristic calculation on the deformation data to obtain strain change data;

[0014] The ultrasonic signal processing feature, the temperature transformation data and the strain change data are feature-fused to obtain the residual stress feature.

[0015] Optionally, when the signal processing module performs the function of performing ultrasonic signal processing on the signal data to obtain ultrasonic signal processing characteristics, it is specifically used to:

[0016] Perform time-frequency analysis on the signal data to obtain time-frequency characteristics, and use the following formula to perform time-frequency analysis:

[0017]

[0018] in, For the Signal data corresponding to the time Shift by a preset amount in time and preset frequency The corresponding time-frequency characteristics are: For the The signal data corresponding to the moment, For the preset time window Panning on the timeline Units after the signal data Alignment, is the integral variable, which means that the integral process is in time The entire range of is the complex exponential function, is an imaginary unit, is a frequency variable;

[0019] Performing ultrasonic time delay calculation on the signal data based on the time-frequency characteristics to obtain ultrasonic propagation time variation data;

[0020] Calculating stress variation data based on the ultrasonic wave propagation time variation data;

[0021] Performing echo analysis on the signal data to obtain waveform characteristics;

[0022] The time-frequency features, the stress change data and the waveform features are feature spliced ​​to obtain ultrasonic signal processing features.

[0023] Optionally, when performing the function of performing stress field calculation on the residual stress feature to obtain local stress distribution data and global stress field distribution data, the stress analysis module is specifically used to:

[0024] Calculating the local stress state of each layer using the residual stress characteristics;

[0025] Using the residual stress characteristics to identify stress concentration areas of the local stress state, and obtain local stress distribution data;

[0026] Calculating multi-layer stress cumulative distribution data based on the local stress distribution data;

[0027] Constructing a global stress field according to the residual stress characteristics and the multi-layer stress cumulative distribution data;

[0028] A stress field simulation is performed according to the global stress field to obtain global stress field distribution data.

[0029] Optionally, when the stress analysis module performs the function of calculating the local stress state of each layer using the residual stress characteristics, it is specifically used to:

[0030] The residual stress characteristics are spatially layered to obtain a plurality of layer feature sets;

[0031] Performing feature clustering on the feature set one by one to obtain a number of local categories;

[0032] The residual stress characteristics are used to calculate stress values ​​for the local categories to obtain a local stress state.

[0033] Optionally, when the stress analysis module performs the function of identifying the stress concentration area of ​​the local stress state by using the residual stress characteristics to obtain local stress distribution data, it is specifically used to:

[0034] Performing significant analysis on ultrasonic wave propagation time variation data and waveform characteristics in the residual stress characteristics to obtain significant characteristics;

[0035] converting the local stress state into a stress distribution diagram;

[0036] The significant features are used to perform threshold screening on each data in the stress distribution map to obtain local stress distribution data.

[0037] Optionally, when the stress analysis module performs the function of calculating the multi-layer stress cumulative distribution data according to the local stress distribution data, it is specifically used to:

[0038] Initialize a three-dimensional array;

[0039] Accumulating the local stress distribution data into a three-dimensional array one by one to obtain an accumulated array;

[0040] The accumulated array is smoothed to obtain multi-layer stress cumulative distribution data.

[0041] Optionally, when executing the function of constructing a global stress field according to the residual stress characteristics and the multi-layer stress cumulative distribution data, the stress analysis module is specifically used to:

[0042] Construct a three-dimensional finite element model based on the preset printout;

[0043] Meshing the three-dimensional finite element model to obtain a mesh model;

[0044] The multi-layer stress cumulative distribution data is loaded into the grid model as an initial stress field to obtain an initial model;

[0045] The residual stress characteristics are used to adjust the stress value of the initial model to obtain a global stress field.

[0046] Optionally, when the multi-scale analysis module performs the function of performing scale coupling according to the local stress distribution data and the global stress field distribution data to obtain a scale analysis result, it is specifically used to:

[0047] Performing feature extraction on the local stress distribution data and the global stress field distribution data to obtain a similar feature set;

[0048] Establishing an association relationship between the local stress distribution data and the global stress field distribution data using the similar feature set;

[0049] Based on the association relationship, the influence range of the local stress distribution data on the global stress field distribution data is evaluated to obtain a scale analysis result.

[0050] Optionally, the adaptive adjustment module, when executing the function of adaptively calculating the printing parameters according to the scale analysis result, is specifically used to:

[0051] generating an objective function according to the scale analysis result;

[0052] Obtaining the correlation between the adjustable parameters in the printing process and the preset adjustable parameters and the objective function components, and calculating the performance index of the current adjustable parameters by using the objective function and the correlation;

[0053] The performance index and the objective function are used to calculate the optimal solution of the adjustable parameters to obtain the printing parameters, and the optimal solution is calculated using the following formula:

[0054]

[0055] in, is the final printing parameter, To dynamically adjust adjustable parameters, is the objective function, The constraints for dynamically adjusting adjustable parameters are preset. is the preset penalty factor, It is a mathematical symbol, which means to find The value of , so that the objective function in the brackets reaches the minimum value.

[0056] The present invention couples the signal data of the ultrasonic sensor, the temperature data corresponding to the temperature sensor, and the deformation data to obtain the residual stress characteristics, which can obtain more comprehensive and accurate material state information from multiple different places, and improve the accuracy of subsequent analysis; based on the coupled data analysis, the influence of different printing parameters (such as laser power, scanning speed, etc.) on the residual stress can be better understood, which is helpful to formulate a better printing strategy and reduce cracks or other defects caused by stress concentration. Therefore, the laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release proposed by the present invention can solve the problem of low structural stability of 3D laser fuse printing on printed parts in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A functional module diagram of a laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release provided by an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of a stress field calculation process provided by an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a process for generating printing parameters provided by an embodiment of the present invention;

[0060] Figure 4 A schematic flow chart of a method for monitoring residual stress in laser fuse 3D printing based on ultrasonic stress release provided in one embodiment of the present invention.

[0061] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0063] Reference Figure 1 As shown, it is a functional module diagram of a laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release provided by one embodiment of the present invention. In this embodiment, the laser fuse 3D printing residual stress monitoring system 100 based on ultrasonic stress release can be installed in an electronic device. According to the functions implemented, the laser fuse 3D printing residual stress monitoring system 100 based on ultrasonic stress release may include a signal processing module 101, a stress analysis module 102, a multi-scale analysis module 103 and an adaptive adjustment module 104. The module described in the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0064] In the embodiment of the present invention, the signal processing module 101 includes obtaining the signal data of the ultrasonic sensor of the laser fuse 3D printing, the temperature data of the temperature sensor and the deformation data, coupling the signal data, the temperature data and the deformation data to obtain the residual stress characteristics;

[0065] In the embodiment of the present invention, the stress analysis module 102 includes performing stress field calculation on the residual stress characteristics to obtain local stress distribution data and global stress field distribution data;

[0066] In the embodiment of the present invention, the multi-scale analysis module 103 includes performing scale coupling according to the local stress distribution data and the global stress field distribution data to obtain a scale analysis result;

[0067] In the embodiment of the present invention, the adaptive adjustment module 104 includes adaptively calculating printing parameters according to the scale analysis result, and generating control instructions according to the calculated printing parameters.

[0068] In detail, the modules described in the laser fuse 3D printing residual stress monitoring system 100 based on ultrasonic stress release described in the embodiment of the present invention adopt the same technical means as the laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0069] In combination with specific embodiments, the various components and specific workflows of the laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release are described below:

[0070] The signal processing module 101 is used to obtain the signal data of the ultrasonic sensor, the temperature data of the temperature sensor and the deformation data of the laser fuse 3D printing, and couple the signal data, the temperature data and the deformation data to obtain the residual stress characteristics.

[0071] In the embodiments of the present invention, by coupling multiple sensor data, more comprehensive and accurate material state information can be obtained. For example, signal data can reveal internal defects, temperature data can indicate thermal stress distribution, and deformation data can show changes in external structure. The combination of these information can more accurately identify and quantify residual stress.

[0072] In the embodiment of the present invention, the ultrasonic sensor is usually used to detect defects inside or on the surface of the material, such as cracks, pores, etc. In laser fuse 3D printing, it can be used to monitor the state of the molten pool, the quality of interlayer bonding, and the integrity of the internal structure. The signal data of the ultrasonic sensor of laser fuse 3D printing includes the propagation speed, reflection intensity, propagation time, etc. of the ultrasonic wave, which can reflect the physical properties of the material such as density changes and interface characteristics.

[0073] In the embodiment of the present invention, the temperature sensor is used to monitor the temperature changes during the printing process in real time, including the melting pool temperature, the ambient environment temperature, etc.

[0074] In the embodiment of the present invention, deformation data refers to the displacement distribution of each point of a structure or material under the action of an external force (such as load, temperature change).

[0075] In the embodiment of the present invention, when the signal processing module performs data coupling of the signal data, the temperature data and the deformation data to obtain the residual stress characteristics, it is specifically used to:

[0076] Performing ultrasonic signal processing on the signal data to obtain ultrasonic signal processing features;

[0077] Performing time series analysis on the temperature data to obtain temperature transformation data;

[0078] Performing strain characteristic calculation on the deformation data to obtain strain change data;

[0079] The ultrasonic signal processing feature, the temperature transformation data and the strain change data are feature-fused to obtain the residual stress feature.

[0080] In the embodiment of the present invention, when the signal processing module performs the function of performing ultrasonic signal processing on the signal data to obtain ultrasonic signal processing characteristics, it is specifically used to:

[0081] Performing time-frequency analysis on the signal data to obtain time-frequency features;

[0082] Performing ultrasonic time delay calculation on the signal data based on the time-frequency characteristics to obtain ultrasonic propagation time variation data;

[0083] Calculating stress variation data based on the ultrasonic wave propagation time variation data;

[0084] Performing echo analysis on the signal data to obtain waveform characteristics;

[0085] The time-frequency features, the stress change data and the waveform features are feature spliced ​​to obtain ultrasonic signal processing features.

[0086] In detail, the time-frequency analysis can be performed using the following formula:

[0087]

[0088] in, For the Signal data corresponding to the time Shift by a preset amount in time and preset frequency The corresponding time-frequency characteristics are: For the The signal data corresponding to the moment, For the preset time window Panning on the timeline Units after the signal data Alignment, is the integral variable, which means that the integral process is in time The entire range of is the complex exponential function, is an imaginary unit, is a frequency variable.

[0089] In detail, by calculating the cross-correlation function between the transmitted signal and the received signal, the time delay corresponding to the signal peak position is located, the propagation time at different time points is recorded, and time series data is generated. The time series data is the ultrasonic propagation time variation data.

[0090] In detail, the propagation time of ultrasonic waves is affected by the stress state. According to the acoustic elastic effect, the stress change data can be calculated by the following formula:

[0091]

[0092] In detail, is the stress change data, is the acoustic elastic constant of the preset material, It is the preset ultrasonic propagation time variation data.

[0093] In detail, the echo signal of the signal data is extracted, and the waveform characteristics of the echo signal are extracted, including the envelope, pulse width, peak position, main frequency, bandwidth, harmonic components, etc. of the echo signal.

[0094] In detail, the time-frequency features, stress change features, and waveform feature vectors are directly concatenated to form a high-dimensional feature vector, which is the ultrasonic signal processing feature.

[0095] In the embodiment of the present invention, the time series analysis refers to calculating the time temperature gradient and the spatial temperature gradient of the temperature data, and then extracting the preset material thermal expansion coefficient based on the time temperature gradient and the spatial temperature gradient to calculate the strain caused by the temperature change. The time temperature gradient of the temperature data refers to the rate of change of temperature over time, which is obtained by calculating the temperature data through the finite difference method; the spatial temperature gradient is the distribution change of temperature at different positions obtained by calculating the partial derivative of the temperature distribution in the spatial grid.

[0096] In an embodiment of the present invention, a relationship between the deformation and the displacement is constructed based on the deformation data, and the displacement gradient is calculated using the finite difference method based on the relationship. The gradient is used as strain data, and statistical characteristics of the strain data, such as the average strain, maximum strain, and standard deviation, are calculated. The strain values ​​of key areas (such as high strain areas) are extracted to obtain strain change data.

[0097] In the embodiment of the present invention, the feature fusion is performed by weighted calculation on the ultrasonic signal processing feature, the temperature transformation data and the strain change data using preset weights, and the weighted calculation results are recombined to obtain the residual stress feature.

[0098] In the embodiment of the present invention, by integrating ultrasonic signals, temperature and deformation data, more comprehensive material status information can be provided, thereby improving the diagnostic accuracy of internal defects, thermal stress and external deformation.

[0099] The stress analysis module 102 is used to perform stress field calculation on the residual stress characteristics to obtain local stress distribution data and global stress field distribution data.

[0100] In the embodiment of the present invention, stress field analysis is performed using accurate residual stress characteristics, which can quickly identify stress concentration points and discover potential damage or failure areas.

[0101] In the embodiments of the present invention, local stress distribution data refers to the stress distribution within a small range (usually a specific area or unit) of a material or structure, focusing on detail features, while global stress field distribution data describes the stress distribution of the entire material or structure under specific loads or conditions, emphasizing the overall stress change trend.

[0102] Ginseng Figure 2 As shown, when the stress analysis module performs the function of calculating the stress field of the residual stress feature to obtain local stress distribution data and global stress field distribution data, it is specifically used to:

[0103] S21, calculating the local stress state of each layer using the residual stress characteristics;

[0104] S22, using the residual stress characteristics to identify the stress concentration area of ​​the local stress state to obtain local stress distribution data;

[0105] S23, calculating the multi-layer stress cumulative distribution data according to the local stress distribution data;

[0106] S24, constructing a global stress field according to the residual stress characteristics and the multi-layer stress cumulative distribution data;

[0107] S25. Perform stress field simulation according to the global stress field to obtain global stress field distribution data.

[0108] In the embodiment of the present invention, when the stress analysis module performs the function of calculating the local stress state of each layer by using the residual stress characteristics, it is specifically used to:

[0109] The residual stress characteristics are spatially layered to obtain a plurality of layer feature sets;

[0110] Performing feature clustering on the feature set one by one to obtain a number of local categories;

[0111] The residual stress characteristics are used to calculate stress values ​​for the local categories to obtain a local stress state.

[0112] In detail, the residual stress features are grouped according to their spatial positions to form a feature set for each layer, and several layers are obtained by stratifying using the coordinate axes in the residual stress features. The residual stress features of the same layer are added to the same set to obtain several layers of feature sets.

[0113] In detail, in each layer of feature set, clustering is performed according to spatial position and feature values ​​(such as stress value, temperature, strain, etc.) to obtain several local categories. The residual stress features within the feature set are clustered by utilizing a clustering algorithm based on multidimensional features. Each residual stress feature is converted into a multidimensional vector, and multidimensional clustering is performed using K-Means or Gaussian mixture model (GMM). The feature set of each class is output, which can simultaneously consider spatial and feature similarities, making the classification more accurate.

[0114] In detail, the stress value calculation is performed on the residual stress characteristics in each local category by averaging or weighted averaging the characteristic values ​​within the local category to obtain the ultrasonic stress value at the category level; the temperature characteristic values ​​within the local category are statistically analyzed to calculate the average thermal stress value of the category, the deformation characteristics within the category are averaged or weighted averaged to obtain the mechanical stress value of the category, and the stress components of the local category are synthesized to obtain the local stress state of the category.

[0115] In the embodiment of the present invention, when the stress analysis module performs the function of identifying the stress concentration area of ​​the local stress state by using the residual stress characteristics to obtain the local stress distribution data, it is specifically used to:

[0116] Performing significant analysis on ultrasonic wave propagation time variation data and waveform characteristics in the residual stress characteristics to obtain significant characteristics;

[0117] converting the local stress state into a stress distribution diagram;

[0118] The significant features are used to perform threshold screening on each data in the stress distribution map to obtain local stress distribution data.

[0119] In detail, the stress values ​​of the local stress state are mapped to the geometric areas of the material (such as meshes or nodes) to generate stress distribution maps.

[0120] In detail, the extracted significant features are used to screen the stress values ​​or specific areas in the stress distribution map, and the areas above the significant features are screened out. The stress values ​​that meet the conditions are retained, and the other parts are set to zero or marked as non-significant areas, and finally the local stress distribution data are generated.

[0121] In an embodiment of the present invention, when the stress analysis module performs the function of calculating the multi-layer stress cumulative distribution data according to the local stress distribution data, it is specifically used to:

[0122] Initialize a three-dimensional array;

[0123] Accumulating the local stress distribution data into a three-dimensional array one by one to obtain an accumulated array;

[0124] The accumulated array is smoothed to obtain multi-layer stress cumulative distribution data.

[0125] In detail, the initializing the three-dimensional array refers to creating an empty three-dimensional array or matrix for storing multi-layer stress cumulative distribution data.

[0126] In detail, starting from the bottom layer, the local stress distribution data are accumulated into a three-dimensional array layer by layer. Layer, local stress distribution data Add to multi-layer stress cumulative distribution data middle: ,in, Indicates The depth position of the layer.

[0127] In detail, a smoothing filter (such as a Gaussian filter) is used to filter the cumulative stress distribution to eliminate local noise, and an interpolation method (such as bilinear interpolation or cubic spline interpolation) is used to fill in blank data for the filtered data to obtain multi-layer stress cumulative distribution data.

[0128] In an embodiment of the present invention, when executing the function of constructing a global stress field according to the residual stress characteristics and the multi-layer stress cumulative distribution data, the stress analysis module is specifically used to:

[0129] Construct a three-dimensional finite element model based on the preset printout;

[0130] Meshing the three-dimensional finite element model to obtain a mesh model;

[0131] The multi-layer stress cumulative distribution data is loaded into the grid model as an initial stress field to obtain an initial model;

[0132] The residual stress characteristics are used to adjust the stress value of the initial model to obtain a global stress field.

[0133] In detail, by obtaining a preset printing shape and size, a geometric model is established according to the printing shape and size, and the elastic modulus, Poisson's ratio, thermal expansion coefficient and other parameters of the preset material are attribute-associated with the geometric model to obtain a three-dimensional finite element model.

[0134] In detail, the surface of the three-dimensional finite element model is meshed with hexahedral units, and a higher mesh density is set in stress concentration areas (such as areas with significant deformation or residual stress) to capture details, and a coarser mesh is used in areas with smaller stress changes to reduce computational complexity.

[0135] In detail, the multi-layer stress cumulative distribution data is used to generate a continuous stress field in a three-dimensional finite element model using an interpolation algorithm (such as linear interpolation or cubic spline interpolation), and the interpolated stress field data is mapped to the grid points of the finite element model to ensure that the data corresponds to the grid. The loaded stress field data is used as an initial condition to represent the initial stress state of the system, and an initial model is obtained.

[0136] In detail, data related to stress adjustment are extracted from the residual stress characteristics, and the adjusted node stress values ​​are calculated using a material mechanics model or a data-driven model.

[0137] In the embodiment of the present invention, the global stress field distribution of the overall structure is calculated based on the global stress field to obtain global stress field distribution data.

[0138] In the embodiment of the present invention, the global stress field provides a perspective of the overall stress distribution, and the local stress distribution refines the stress characteristics of key areas, such as stress concentration points or defective areas. By combining the two, it is possible to optimize the structural design from a macro level and evaluate local details from a micro level, thereby improving the ability to optimize the structural design.

[0139] The multi-scale analysis module 103 is used to perform scale coupling according to the local stress distribution data and the global stress field distribution data to obtain a scale analysis result.

[0140] In an embodiment of the present invention, the global stress field is calculated based on the residual stress and deformation data, and then a comprehensive analysis is performed through scale coupling, which can ensure the uniform distribution of the overall stress of the printed part, reduce defects such as warping and cracking caused by residual stress or deformation, and reduce the risk of structural failure.

[0141] In the embodiment of the present invention, when the multi-scale analysis module performs the function of performing scale coupling according to the local stress distribution data and the global stress field distribution data to obtain the scale analysis result, it is specifically used to:

[0142] Performing feature extraction on the local stress distribution data and the global stress field distribution data to obtain a similar feature set;

[0143] Establishing an association relationship between the local stress distribution data and the global stress field distribution data using the similar feature set;

[0144] Based on the association relationship, the influence range of the local stress distribution data on the global stress field distribution data is evaluated to obtain a scale analysis result.

[0145] In an embodiment of the present invention, the feature extraction refers to performing feature extraction on the local stress distribution data and the global stress field distribution data respectively, such as calculating the variance or standard deviation of the stress field, identifying the high stress area caused by the temperature field, and analyzing the continuity and change trend of stress in the global range; then matching similar features of the extracted similar feature set, determining the influence range of local features on global features by using a spatial distance metric (such as Euclidean distance) and comparing the numerical similarity of local and global features by using a similarity index (such as cosine similarity), such as: whether the local high stress area has an important influence on the overall distribution of the global stress field; whether the local stress gradient area is consistent with the global stress gradient distribution trend.

[0146] In the embodiment of the present invention, a mathematical model or mapping relationship is established to quantify how local stress affects the global stress field, such as establishing a linear model: , where G(x,y,z) is the full set of stress field distribution data, L(x,y,z) is the local stress distribution data, and w is the preset weight coefficient.

[0147] In the embodiment of the present invention, the scope of the effect of local stress changes (such as the expansion or weakening of the stress concentration area) on the global stress distribution is evaluated, including: the global area affected by the local stress change and the delayed effect of the local change on the global distribution. The contribution of local features to the global goal is analyzed through mathematical models or experiments, and scale analysis results are generated based on the scope of influence and contribution. For example, if the local high stress concentration has a significant impact on the global uniformity, optimization suggestions can be put forward, such as reducing the laser power or adjusting the printing path.

[0148] In the embodiment of the present invention, the local stress distribution provides microscopic details, while the global stress field reflects the macroscopic distribution as a whole. By combining the two through scale coupling, the local information can be contextually verified in the global framework, while the global information can be refined to microscopic characteristics, thereby allowing dynamic switching between micro and macro, significantly improving the comprehensiveness and accuracy of the analysis.

[0149] The adaptive adjustment module 104 is used to adaptively calculate printing parameters according to the scale analysis results, and generate control instructions according to the calculated printing parameters.

[0150] In the embodiment of the present invention, by using the scale analysis results and the preset parameter association relationship, the module can quantify the relationship between the parameters and the target in a scientific manner, thereby improving the accuracy of parameter adjustment and the optimization efficiency.

[0151] Ginseng Figure 3 As shown, in the embodiment of the present invention, the adaptive adjustment module performs the function of adaptively calculating the printing parameters according to the scale analysis result, specifically for:

[0152] S31, generating an objective function according to the scale analysis result;

[0153] S32, obtaining the association relationship between the adjustable parameters in the printing process and the preset adjustable parameters and the objective function components, and calculating the performance index of the current adjustable parameters by using the objective function and the association relationship;

[0154] S33, using the performance index and the objective function to calculate the optimal solution of the adjustable parameters to obtain printing parameters.

[0155] In an embodiment of the present invention, an objective function is constructed based on the scale analysis results to clarify the optimization target, such as minimizing the local stress peak value or maximizing the uniformity of the global stress distribution. The objective function is generated by setting different weights for the global stress field distribution data and the local stress data using the scale analysis results.

[0156] In an embodiment of the present invention, the adjustable parameters in the printing process include but are not limited to: laser power, printing path, scanning speed, layer thickness, scanning spacing, etc.; the correlation between the adjustable parameters and the components of the objective function, such as the influence of laser power on thermal stress and stress uniformity, the influence of printing path planning on local stress concentration areas, etc., the correlation is to express the influence of the adjustable parameters on the stress field in mathematical form through experimental fitting, theoretical derivation or data-driven modeling.

[0157] In the embodiment of the present invention, calculating the performance index of the current adjustable parameter means substituting the current adjustable parameter value into the objective function, and then quantifying the performance of the current parameter configuration on the optimization target through a normalized method.

[0158] In the embodiment of the present invention, the optimal solution calculation refers to dynamically adjusting the adjustable parameters using an optimization algorithm to optimize the objective function.

[0159] In the embodiment of the present invention, the following formula can be used to calculate the optimal solution:

[0160]

[0161] in, is the final printing parameter, To dynamically adjust adjustable parameters, is the objective function, The constraints for dynamically adjusting adjustable parameters are preset. is the preset penalty factor, It is a mathematical symbol, which means to find The value of , so that the objective function in the brackets reaches the minimum value.

[0162] In an embodiment of the present invention, the movement path of the print head is calculated based on the printing parameters, the three-dimensional model is divided into multiple two-dimensional layers, and a printing path is generated for each layer, including the path of contour lines, fill lines and support structures. Finally, the calculated parameters and paths are converted into control instructions that can be executed by the printer, and the printing parameters and printing paths are output by using a preset control instruction format to obtain control instructions.

[0163] In the embodiment of the present invention, by adjusting the adjustable parameters in real time, it is possible to dynamically respond to stress changes or anomalies during the printing process, effectively reduce problems such as local stress concentration and uneven global stress distribution, and avoid cracks or deformation caused by stress concentration.

[0164] like Figure 4 FIG. 1 is a flow chart of a method for monitoring residual stress in laser fuse 3D printing with ultrasonic stress release provided by an embodiment of the present invention. In an embodiment of the present invention, the method for monitoring residual stress in laser fuse 3D printing with ultrasonic stress release comprises:

[0165] S401, acquiring signal data of an ultrasonic sensor, temperature data of a temperature sensor, and deformation data of laser fuse 3D printing, and performing data coupling on the signal data, the temperature data, and the deformation data to obtain residual stress characteristics;

[0166] S402, performing stress field calculation on the residual stress characteristics to obtain local stress distribution data and global stress field distribution data;

[0167] S403, performing scale coupling according to the local stress distribution data and the global stress field distribution data to obtain a scale analysis result;

[0168] S404: adaptively calculating printing parameters according to the scale analysis results, and generating control instructions according to the calculated printing parameters.

[0169] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology.

[0170] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0171] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in the system can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0172] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. Laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release, characterized in that: The system includes: a signal processing module, a stress analysis module, a multi-scale analysis module, and an adaptive adjustment module. Specifically: The signal processing module is used to obtain the signal data of the ultrasonic sensor, the temperature data of the temperature sensor and the deformation data of the laser fuse 3D printing, and couple the signal data, the temperature data and the deformation data to obtain the residual stress characteristics. When the signal processing module performs the function of coupling the signal data, the temperature data and the deformation data to obtain the residual stress characteristics, it is specifically used to: perform ultrasonic signal processing on the signal data to obtain ultrasonic signal processing characteristics; perform time series analysis on the temperature data to obtain temperature transformation data; perform strain feature calculation on the deformation data to obtain strain change data, and calculate the stress change data by the following formula: in, is the stress change data, is the acoustic elastic constant of the preset material, The ultrasonic wave propagation time variation data is preset; the ultrasonic wave signal processing feature, the temperature change data and the strain variation data are feature-fused to obtain the residual stress feature; A stress analysis module, used for stratifying the residual stress characteristics by spatial position to obtain several layers of feature sets; performing feature clustering on the feature sets one by one to obtain several local categories; using the residual stress characteristics to calculate stress values ​​for the local categories to obtain local stress states; using the residual stress characteristics to identify stress concentration areas for the local stress states to obtain local stress distribution data; calculating multi-layer stress cumulative distribution data based on the local stress distribution data; constructing a global stress field based on the residual stress characteristics and the multi-layer stress cumulative distribution data; performing stress field simulation based on the global stress field to obtain global stress field distribution data; a multi-scale analysis module, used for performing scale coupling based on the local stress distribution data and the global stress field distribution data to obtain scale analysis results; The adaptive adjustment module is used to adaptively calculate printing parameters according to the scale analysis results, and generate control instructions according to the calculated printing parameters.

2. The laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release according to claim 1, characterized in that: When the signal processing module performs the function of performing ultrasonic signal processing on the signal data to obtain ultrasonic signal processing characteristics, it is specifically used to: Perform time-frequency analysis on the signal data to obtain time-frequency characteristics, and use the following formula to perform time-frequency analysis: in, For the Signal data corresponding to the time Shift by a preset amount in time and preset frequency The corresponding time-frequency characteristics are: For the The signal data corresponding to the moment, For the preset time window Panning on the timeline Units after the signal data Alignment, is the integral variable, which means that the integral process is in time The entire range of is the complex exponential function, is an imaginary unit, is a frequency variable; Performing ultrasonic time delay calculation on the signal data based on the time-frequency characteristics to obtain ultrasonic propagation time variation data; Calculating stress variation data based on the ultrasonic wave propagation time variation data; Performing echo analysis on the signal data to obtain waveform characteristics; The time-frequency features, the stress change data and the waveform features are feature spliced ​​to obtain ultrasonic signal processing features.

3. The laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release according to claim 1, characterized in that: When the stress analysis module performs the function of identifying the stress concentration area of ​​the local stress state by using the residual stress characteristics to obtain local stress distribution data, it is specifically used to: Performing significant analysis on the ultrasonic wave propagation time variation data and waveform characteristics in the residual stress characteristics to obtain significant characteristics; converting the local stress state into a stress distribution diagram; The significant features are used to perform threshold screening on each data in the stress distribution map to obtain local stress distribution data.

4. The laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release according to claim 1, characterized in that: When the stress analysis module performs the function of calculating the multi-layer stress cumulative distribution data according to the local stress distribution data, it is specifically used to: Initialize a three-dimensional array; Accumulating the local stress distribution data into a three-dimensional array one by one to obtain an accumulated array; The accumulated array is smoothed to obtain multi-layer stress cumulative distribution data.

5. The laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release according to claim 1, characterized in that: When executing the function of constructing a global stress field according to the residual stress characteristics and the multi-layer stress cumulative distribution data, the stress analysis module is specifically used to: Construct a three-dimensional finite element model based on the preset printout; Meshing the three-dimensional finite element model to obtain a mesh model; The multi-layer stress cumulative distribution data is loaded into the grid model as an initial stress field to obtain an initial model; The residual stress characteristics are used to adjust the stress value of the initial model to obtain a global stress field.

6. The laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release as claimed in claim 1, characterized in that: When the multi-scale analysis module performs the function of performing scale coupling according to the local stress distribution data and the global stress field distribution data to obtain a scale analysis result, it is specifically used to: Performing feature extraction on the local stress distribution data and the global stress field distribution data to obtain a similar feature set; Establishing an association relationship between the local stress distribution data and the global stress field distribution data using the similar feature set; Based on the association relationship, the influence range of the local stress distribution data on the global stress field distribution data is evaluated to obtain a scale analysis result.

7. The laser fuse 3D printing residual stress monitoring system based on ultrasonic stress release according to claim 1, characterized in that: The adaptive adjustment module performs the function of adaptively calculating the printing parameters according to the scale analysis result, and is specifically used for: generating an objective function according to the scale analysis result; Obtaining the correlation between the adjustable parameters in the printing process and the preset adjustable parameters and the objective function components, and calculating the performance index of the current adjustable parameters by using the objective function and the correlation; The performance index and the objective function are used to calculate the optimal solution of the adjustable parameters to obtain the printing parameters, and the optimal solution is calculated using the following formula: in, is the final printing parameter, To dynamically adjust adjustable parameters, is the objective function, The constraints for dynamically adjusting adjustable parameters are preset. is the preset penalty factor, It is a mathematical notation, which means to find The value of , so that the objective function in the brackets reaches the minimum value.

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