Molten pool morphology evolution prediction method driven by mechanism guide data in selective laser melting process

Abstract

The invention discloses a mechanism-guided data-driven molten pool morphology evolution prediction method in a selective laser melting process. The method comprises the following steps: 1, constructing a molten pool image data set; 2, processing the molten pool image, and extracting the length, the width, the area, the perimeter, the aspect ratio and other morphological characteristics of the molten pool; 3, constructing a molten pool sample and a label; 4, calculating a theoretical temperature field of the surface of the molten pool by utilizing a mechanism analysis model; 5, calculating an included angle between a laser moving direction and a protective airflow direction; 6, constructing a mechanism-guided data-driven molten pool morphology evolution prediction model; 7, constructing a loss function; and 8, training the model. According to the method, the melting mechanism and the sensing data in the selective laser melting process are fused, so that the evolution trend of the molten pool morphology can be accurately predicted in advance, the prediction of the abnormal disturbance quantity of the molten pool morphology evolution process caused by the historical scanning track can be enhanced, and the accurate prediction of the molten pool morphology evolution is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of metal additive manufacturing process monitoring, and in particular to a mechanism-guided data-driven method for predicting the morphology evolution of a molten pool in a selective laser melting process. Background Art

[0002] Selective laser melting technology has unique advantages in manufacturing high-quality components with complex structures and internal features in the fields of biomedical engineering, automobiles, aerospace, etc. It is a very promising and environmentally friendly metal additive manufacturing technology. However, the manufactured parts usually have defects such as pores, cracks and warping, which increases the manufacturing cost caused by trial and error and post-processing, and hinders its large-scale industrial application. The selective laser melting process involves complex heat transfer and fluid flow. The resulting molten pool is affected by surface tension, Marangoni force, recoil pressure and other factors. Its abnormal evolution will lead to the formation of defects, which in turn leads to microstructural inhomogeneity and significantly affects the mechanical properties and fatigue life of the formed parts. Therefore, in order to ensure the final quality of the parts, it is necessary to control the evolution of the molten pool morphology during the selective laser melting process, and accurate prediction of the evolution of the molten pool morphology is an important basis for achieving melting state control and reducing process defects.

[0003] There are currently methods based on physical models, process data, and historical sensor data to model the evolution of the molten pool morphology in the selective laser melting process. The mainstream methods based on physical models include numerical models based on finite elements and analytical models. Numerical models based on finite elements require a lot of time and storage costs due to their fine discretization in time and space. The analytical model retains the basic heat transfer characteristics at the expense of some complex physical properties of the molten pool contained in the numerical model, and integrates material properties, process parameters, and scanning strategies in a mechanism-consistent manner to calculate the melting temperature field, and quickly calculate the evolution of the molten pool morphology of multiple tracks or even multiple layers on the part scale. The method based on process data uses machine learning models to achieve faster predictions, but the integration method of different process data may not conform to the melting mechanism, resulting in low generalization ability. In the actual manufacturing process, fluctuations in process parameters caused by non-fixed material properties such as machine errors and absorptivity and random interference during the movement of the molten pool may cause unexpected changes in the molten pool morphology. Therefore, it is difficult for methods based on physical models and process data to accurately capture the transient evolution of the molten pool morphology. There is inevitably noise or even erroneous data in the sensing data caused by environmental factors and sensor errors. Due to the lack of guidance and constraints from physical knowledge, the model may learn a false relationship between the historical melting state and the melt pool evolution, thereby reducing the performance of the prediction model. Summary of the invention

[0004] The present invention aims to address the deficiencies in the above-mentioned prior art and proposes a mechanism-guided data-driven method for predicting the molten pool morphology evolution in the selective laser melting process, in order to achieve accurate prediction of the transient evolution of the molten pool morphology in the selective laser melting process across working conditions, thereby providing a basis for controlling the molten pool morphology evolution process and maintaining a good melting process, thereby reducing or even eliminating defects in the forming process, thereby improving the three-dimensional morphology accuracy, mechanical properties, and fatigue life of the formed parts, and ensuring the quality of the final product.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0006] The invention discloses a method for predicting the evolution of molten pool morphology in a selective laser melting process driven by a mechanism-guided data, which comprises the following steps:

[0007] Step 1: Use a temperature-calibrated high-speed infrared camera to collect the molten pool image of the selective laser melting process in a coaxial manner and at a fixed frequency, and construct a molten pool image dataset ;in, represents the melt pool image at the i-th moment, is the number of melt pool images; L and H are the length and height of the melt pool image respectively;

[0008] Step 2: By taking the melt pool image at the i-th moment Processing is performed to extract the morphological features of the molten pool at the i-th moment , including: length ,width ,area ,perimeter , aspect ratio , thereby constructing a melt pool morphology feature dataset ;

[0009] Step 3: Melt pool image at this moment To Melt pool image at this moment As a sample , the morphological characteristics of the molten pool at the i-th moment To Morphological characteristics of the molten pool at each moment As a sample Tags ; Morphological characteristics of the molten pool at each moment To Morphological characteristics of the molten pool at each moment As a sample Tags Corresponding historical melt pool feature sequence ;

[0010] Step 4: Calculate the theoretical temperature field of the molten pool surface at the i-th moment based on material parameters, process parameters and scanning strategy ; thus obtaining the Time to The theoretical temperature field of the molten pool surface at time t, thus obtaining the label The corresponding theoretical temperature field sequence of the molten pool surface ;

[0011] Step 5: Calculate the labels The corresponding angle between the laser moving direction and the protective airflow direction in the clockwise direction ;in, It represents the angle between the laser moving direction and the protective airflow direction in the clockwise direction at the i-th moment;

[0012] Step 6: Construct a prediction network for the molten pool morphology evolution, including: theoretical temperature field feature extraction module, improved attention mechanism module, mechanism and data feature fusion module, and , and Process it and get Time to Moment-by-moment prediction of melt pool morphology feature sequences ;

[0013] Step 7: Use formula (13) to construct the loss function :

[0014] (13)

[0015] In formula (10), is the predicted value of the melt pool morphology feature sequence, is the difference value of the molten pool morphology feature sequence, is the predicted value of the sequence difference of the molten pool morphology characteristics, A parameter to determine the relative importance of this loss term;

[0016] Step 8: Use the gradient descent method to train the molten pool morphology feature prediction network, and calculate the total loss function loss to update the model parameters. When the total loss function loss converges, the trained molten pool morphology feature prediction model is obtained, which is used to predict the future molten pool morphology feature evolution process during the selective laser melting process.

[0017] The mechanism-guided data-driven selective laser melting process molten pool morphology evolution prediction method described in the present invention is also characterized in that step 2 includes the following steps:

[0018] Step 2.1: Liquidus temperature of a given powder material , extract the melt pool image at the i-th moment In, with The absolute value of the difference is less than the error The pixel points are extracted and the least square method is used to fit the extracted pixel point set into the i-th closed curve ;

[0019] Step 2.2: Move two first straight lines parallel to the direction of laser movement from the center of the spot to both sides. When the two first straight lines intersect the i-th closed curve When there is only one intersection point, calculate the distance between the two first straight lines to obtain the i-th closed curve Width;

[0020] Move two second straight lines perpendicular to the moving direction of the laser from the center of the spot to both sides. When there is only one intersection point, calculate the distance between the two second straight lines to obtain the i-th closed curve Length;

[0021] According to the instantaneous field of view of the high-speed infrared camera, the i-th closed curve After converting the length, width, area and perimeter of the molten pool, the length of the molten pool is obtained. ,width ,area and perimeter ;

[0022] The aspect ratio of the melt pool is obtained by calculating the ratio between the length and width of the melt pool. .

[0023] Further, step 4 includes the following steps:

[0024] Step 4.1: Take the center of the laser spot at the i-th moment As the base point, create a rectangle with the length along the laser moving direction at the i-th moment and the width perpendicular to the laser moving direction at the i-th moment; among them, the center of the laser spot at the i-th moment is The distances to the front and back edges of the rectangle are set to and , The distance to the left and right edges is set to ;

[0025] Step 4.2: Set the point set resolution to , extracted from the rectangle The coordinates of the points ,in, is the coordinate of the jth point at the i-th moment in the working coordinate system of the selective laser melting equipment;

[0026] Step 4.3: Calculate the label using equations (1) to (3) The corresponding theoretical temperature of the jth point at the i-th moment :

[0027] (1)

[0028] (2)

[0029] (3)

[0030] In formula (1) to formula (3), To calculate the The number of historical laser scanning points used when measuring the theoretical temperature at the moment; is the thermal diffusion coefficient of the powder alloy, is the density of the powder material, is the initial temperature of the environment; For The specific heat capacity at the temperature, m is the coefficient of the specific heat capacity changing with temperature; P is the laser power, is the absorption coefficient of the powder particles, is the beam radius, is the beam intensity distribution factor of the laser in the XY plane, is the center of the laser spot at the i-th moment The coordinates in the XY plane of the working coordinate system, is the time-integrated variable; is the time-integrated variable The time difference to the i-th moment; is the time-integrated variable The integrated parameter of laser properties at the i-th moment, h is the height of the body heat source;

[0031] Step 4.4: Follow the process in step 4.3 to calculate the label Corresponding Middle Point to Points and Point to The theoretical temperature at the point is obtained, so as to obtain the theoretical temperature field of the molten pool surface at the i-th moment .

[0032] Further, step 5 includes the following steps:

[0033] Step 5.1: Calculate the label using formula (4) The angle between the laser moving direction and the protective airflow direction in the clockwise direction at the corresponding moment i :

[0034] (4)

[0035] In formula (4), is the laser moving unit direction vector at the i-th moment, is the unit direction vector of the protective airflow at the i-th moment, is the function for calculating the clockwise angle;

[0036] Step 5.2: Follow the procedure in step 5.1 to calculate the Time to The angle between the laser moving direction at the moment and the protective airflow direction in the clockwise direction and the Time to The angle between the laser moving direction at the moment and the protective airflow direction in the clockwise direction is obtained to obtain the label Corresponding laser moving direction and protective airflow angle sequence .

[0037] Further, step 6 includes the following steps:

[0038] Step 6.1: The theoretical temperature field feature extraction module includes: two convolutional pooling layers and one fully connected layer;

[0039] Will Input the theoretical temperature field feature extraction module for processing and obtain the label Corresponding mechanism characteristic sequence ,in, Indicates The mechanistic characteristics of the moment;

[0040] Step 6.2: The improved attention mechanism module includes: a spatiotemporal difference extraction unit, a fully connected layer and a Softmax function;

[0041] The spatiotemporal difference extraction unit uses equations (5) to (7) to calculate the Moment and Time difference , No. Laser spot center at the moment With Laser spot center at the moment Distance in X direction , Y direction distance , Euclidean distance , and constitute the The time-space difference sequence of the historical melt pool corresponding to the moment , thus obtaining Time to The time-space difference sequence of the historical melt pool corresponding to the moment ;

[0042] (5)

[0043] (6)

[0044] (7)

[0045] In formula (6), For the The center of the laser spot at the moment Coordinates in the XY plane;

[0046] Will Input to the fully connected layer for processing, and get the The spatiotemporal characteristics of moments , and continue to input it into the Softmax function for processing, and get the Time to The molten pool at the moment Attention weight of the moment melt pool ; thus obtaining the label The corresponding historical melt pool Time to Attention weight at each moment ;in, Indicates The attention weight of the melting pool at the moment; where, Indicates the first moment corresponding to The spatial and temporal characteristics of the moment, Indicates the first moment corresponding to Attention weight of melt pool features at each moment;

[0047] Step 6.3: The mechanism and data feature fusion module includes: an encoder module and a decoder module;

[0048] Step 6.3.1: The encoder module is an encoder based on the encoding neural ODE-GRU structure, and uses equations (8) to (9) to calculate the first The encoded input hidden state at time , thus obtaining Time to The sequence of encoded hidden states at time ;

[0049] (8)

[0050] (9)

[0051] In formula (8) to formula (9), represents the output of the encoded neural ODE The pre-hidden state at the moment, For the The encoded input hidden state at time t, is the fully connected layer in the encoding neural ODE, To encode the parameters of the neural ODE, GRU stands for encoding gated recurrent unit; For the Encoded input features at time instant; Indicates The mechanistic characteristics of the moment;

[0052] Step 6.3.2: Use formula (10) to calculate the context vector at the i-th moment ; thus obtaining the label The corresponding time from the i-th moment to the Sequence of context vectors at each moment ;

[0053] (10)

[0054] In formula (10), is the encoded input hidden state at the kth moment, represents the attention weight of the melting pool feature at the kth moment corresponding to the i-th moment;

[0055] Step 6.3.3: The decoder is a decoder based on the decoding neural ODE-GRU, and uses equations (11) to (12) to calculate the first The decoded input hidden state at time , thus obtaining Time to The decoded hidden state sequence at time ;

[0056] (11)

[0057] (12)

[0058] In formula (11) to formula (12), represents the output of the decoded neural ODE The pre-hidden state at the moment, For the The decoded input hidden state at time t, To decode the fully connected layer in the neural ODE, To decode the parameters in the neural ODE; For the Decoded input features at time instant;

[0059] Will After passing through a fully connected layer, the output Time to Moment-by-moment prediction of melt pool morphology feature sequences .

[0060] The electronic device of the present invention includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the method for predicting the molten pool morphology evolution in the selective laser melting process, and the processor is configured to execute the program stored in the memory.

[0061] The present invention provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, executes the steps of the method for predicting the morphology evolution of a molten pool in a selective laser melting process.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. The present invention integrates material properties, process parameters and scanning strategies through a mechanism analysis model, and combines machine learning methods to extract theoretical temperature field characteristics for the prediction of molten pool morphology evolution, overcoming the compatibility problem between prediction speed and accuracy of existing numerical simulation technology, and greatly improving the efficiency of accurate modeling of molten pool morphology evolution.

[0064] 2. The present invention predicts the evolution of the molten pool morphology by fusing the sensor data features recorded in the historical scanning trajectory with the mechanism features, thereby overcoming the problem in the prior art that it is impossible to consider the abnormal disturbance of the historical scanning trajectory on the molten pool morphology evolution process, thereby improving the accuracy of the transient evolution modeling of the molten pool morphology.

[0065] 3 The present invention utilizes advanced technology in the field of machine learning to extract theoretical temperature field features through the CNN model, and integrates them with the laser moving direction, the protective airflow angle features, and the historical molten pool features and inputs them into the encoder module and decoder module based on ODE-GRU to predict the molten pool morphology evolution, and establishes an attention mechanism that is more in line with the actual processing process, thereby improving the generalization of the molten pool morphology evolution prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the molten pool infrared camera monitoring;

[0067] Figure 2 This is the structural diagram of the theoretical temperature field feature extraction module of the molten pool surface;

[0068] Figure 3 This is the improved attention mechanism module diagram structure diagram;

[0069] Figure 4 It is the structural diagram of the mechanism and data feature fusion module. DETAILED DESCRIPTION

[0070] In this embodiment, a mechanism-guided data-driven method for predicting the morphology evolution of the molten pool in the selective laser melting process is a method that can integrate the melting process mechanism with the sensor data recorded in the historical scanning trajectory, thereby realizing accurate prediction of the transient evolution of the molten pool morphology. Specifically, this method is applied to Figure 1 In the scenario shown, a high-speed coaxial infrared camera is used to coaxially monitor the temperature image of the molten pool during the selective laser melting process. The monitoring system mainly includes a laser, a scanning galvanometer system, a linear polarizer, a beam splitter, a 1:1 imaging lens, and an infrared band pass filter. The monitoring system optically aligns the camera with the laser axis to obtain a still image of the molten pool temperature field. Specifically, the following steps are included:

[0071] Step 1: Use a temperature-calibrated high-speed infrared camera to collect the molten pool image of the selective laser melting process in a coaxial manner and at a fixed frequency, and construct a molten pool image dataset ;in, represents the melt pool image at the i-th moment, is the number of melt pool image samples; L and H are the length and height of the image respectively; in this embodiment, L and H are 350 respectively;

[0072] In this embodiment, the image data set used is collected by an infrared camera through coaxial monitoring. The camera model is IRC 912 infrared camera, and the sampling frequency is 3 kHz.

[0073] In the specific example, the BLT-A160 equipment was used to implement the additive manufacturing process. The metal powder material used in the experiment was SS316L, and nitrogen was used as the protective gas. A serpentine scanning strategy was adopted during the construction process. The scanning spacing, layer thickness, and laser spot diameter were 120 µm, 30 µm, and 100 µm, respectively. During the entire experiment, a nominal constant scanning speed of 700 mm / s was maintained, and parts of three different geometric shapes, namely cubes, triangular prisms, and cylinders, were constructed under different laser powers. A bandpass filter of 1350 nm~1600 nm was selected to accurately capture the molten pool area. Figure 1 Schematic diagram of the infrared camera monitoring process used in this example.

[0074] Step 2: By processing the molten pool image, the morphological features such as length, width, area, perimeter, and aspect ratio of the molten pool are extracted;

[0075] Step 2.1: Liquidus temperature of a given powder material , extract melt pool image Zhongyu The absolute value of the difference is less than the error The pixel points are extracted and the least squares method is used to fit the extracted point set into a closed curve. In this embodiment, is 1723 K, is 5K.

[0076] Step 2.2: Move two first straight lines parallel to the direction of laser movement from the center of the spot to both sides. When the two first straight lines intersect the i-th closed curve When there is only one intersection point, calculate the distance between the two first straight lines to obtain the i-th closed curve Width;

[0077] Move two second straight lines perpendicular to the moving direction of the laser from the center of the spot to both sides. When there is only one intersection point, calculate the distance between the two second straight lines to obtain the i-th closed curve Length;

[0078] According to the instantaneous field of view of the high-speed infrared camera, the i-th closed curve After converting the length, width, area and perimeter of the molten pool, the length of the molten pool is obtained. ,width ,area and perimeter ;

[0079] The aspect ratio of the melt pool is obtained by calculating the ratio between the length and width of the melt pool. .

[0080] Step 3: Melt pool image at this moment To Melt pool image at this moment As a sample , the morphological characteristics of the molten pool at the i-th moment To Morphological characteristics of the molten pool at each moment As a sample Tags ; Morphological characteristics of the molten pool at each moment To Morphological characteristics of the molten pool at each moment As a sample Tags Corresponding historical melt pool feature sequence ; In this embodiment, q is 41 and Q is 61.

[0081] Step 4: Calculate the theoretical temperature field of the molten pool surface at the i-th moment based on material parameters, process parameters and scanning strategy ; thus obtaining the Time to The theoretical temperature field of the molten pool surface at time t, thus obtaining the label The corresponding theoretical temperature field sequence of the molten pool surface ;

[0082] Step 4.1: Take the center of the laser spot at the i-th moment As the base point, create a rectangle with the length along the direction of laser movement at the i-th moment and the width perpendicular to the direction of laser movement at the i-th moment; the center of the laser spot is The distances to the front and back edges of the rectangle are set to and , the distance to the left and right edges is set to In this embodiment, 0.1 mm, 0.6mm, is 0.25 mm.

[0083] Step 4.2: Set the point set resolution to , extracted from the rectangle The coordinates of the points In this embodiment, 0.05 mm / point, is 140; among them, It is the coordinate of the jth point at the i-th moment in the working coordinate system of the selective laser melting equipment. The working coordinate system is determined by the machine manufacturer. First, an origin is determined, and two mutually perpendicular X-axis and Y-axis are determined on a horizontal plane parallel to the substrate, as well as a Z-axis perpendicular to the substrate and upward.

[0084] Step 4.3: Calculate the label using equations (1) to (3) The corresponding theoretical temperature of the jth point at the i-th moment :

[0085] (1)

[0086] (2)

[0087] (3)

[0088] In formula (1) to formula (3), To calculate the The number of historical laser scanning points used when the theoretical temperature at the moment is, in this embodiment, is 100; is the thermal diffusion coefficient of the powder alloy, is the density of the powder material, is the initial temperature of the environment; For The specific heat capacity at the temperature, m is the coefficient of the specific heat capacity changing with temperature. In this embodiment, 5.58e-6 m 2 / s, 7400 kg / m 3 , is 300 K, is 280 J / (kg•K), and m is 0.89×10 -3 ; P is the laser power, is the absorption coefficient of the powder particles, is the beam radius, is the beam intensity distribution factor of the laser on the XY plane. In this embodiment, is 0.6, 0.05 mm, is 1 is the center of the laser spot at the i-th moment The coordinates in the XY plane, is the time integral variable; in formula (2), is the integration variable The time difference to the i-th moment; in formula (3), is the time-integrated variable The laser property integration parameter at the i-th moment, h is the height of the body heat source. In this embodiment, h is 0.04 mm.

[0089] Step 4.4: Follow the process in step 4.3 to calculate the label The corresponding i-th moment From the first point to the The theoretical temperature of the point Point to The theoretical temperature at the point is obtained, so as to obtain the theoretical temperature field of the molten pool surface at the i-th moment ;

[0090] Step 4.5: Calculate the Time to The theoretical temperature field of the molten pool surface at time Time to The theoretical temperature field of the molten pool surface at the moment, thus obtaining the label The corresponding theoretical temperature field sequence of the molten pool surface .

[0091] Step 5: Calculate the labels The corresponding angle between the laser moving direction and the shielding airflow direction in the clockwise direction;

[0092] Step 5.1: Calculate the label using formula (4) The angle between the laser moving direction and the protective airflow direction in the clockwise direction at the corresponding moment i :

[0093] (4)

[0094] In formula (4), is the laser moving unit direction vector at the i-th moment, is the unit direction vector of the protective airflow at the i-th moment, is the function for calculating the clockwise angle;

[0095] Step 5.2: Follow the procedure in step 5.1 to calculate the Time to The angle between the laser moving direction at the moment and the protective airflow direction in the clockwise direction and the Time to The angle between the laser moving direction at the moment and the protective airflow direction in the clockwise direction is obtained to obtain the label Corresponding laser moving direction and protective airflow angle sequence .

[0096] Step 6: Construct a prediction network for the molten pool morphology evolution, including: theoretical temperature field feature extraction module, improved attention mechanism module, mechanism and data feature fusion module, and , and Process it and get Time to Moment-by-moment prediction of melt pool morphology feature sequences .

[0097] Step 6.1: The theoretical temperature field feature extraction module is as follows: Figure 2 As shown, it includes: two convolutional pooling layers and one fully connected layer. In this embodiment, the convolution kernel size is 3×3, and the number is 16 and 32;

[0098] Will Input the theoretical temperature field feature extraction module for processing and obtain the label Corresponding mechanism characteristic sequence ,in, Indicates The mechanism characteristics of the moment.

[0099] Step 6.2: The improved attention mechanism module is as follows Figure 3 As shown, it includes: a spatiotemporal difference extraction unit, a fully connected layer and a Softmax function. In this embodiment, the number of neurons in the fully connected layer is 128;

[0100] The spatiotemporal difference extraction unit uses equations (5) to (7) to calculate the Moment and Time difference , No. Laser spot center at the moment With Laser spot center at the moment Distance in X direction , Y direction distance , Euclidean distance , and constitute the The time-space difference sequence of the historical melt pool corresponding to the moment , thus obtaining Time to The time-space difference sequence of the historical melt pool corresponding to the moment ;

[0101] (5)

[0102] (6)

[0103] (7)

[0104] In formula (6), For the The center of the laser spot at the moment Coordinates in the XY plane.

[0105] Will Input to the fully connected layer for processing, and get the The spatiotemporal characteristics of moments , and continue to input it into the Softmax function for processing, and get the Time to The molten pool at the moment Attention weight of the moment melt pool ; thus obtaining the label The corresponding historical melt pool Time to Attention weight at each moment ;in, Indicates The attention weight of the melting pool at the moment; where, Indicates the first moment corresponding to The spatial and temporal characteristics of the moment, Indicates the first moment corresponding to Attention weight of melt pool features at a certain moment.

[0106] Step 6.3: The mechanism and data feature fusion module is as follows Figure 4 As shown, it includes: an encoder module and a decoder module;

[0107] Step 6.3.1: The encoder is an encoder based on the neural ODE-GRU structure. Using equations (8) to (9), the first The encoded input hidden state at time , thus obtaining Time to The sequence of encoded hidden states at time ;

[0108] (8)

[0109] (9)

[0110] In formula (8) to formula (9), represents the first encoding of the neural network ordinary differential equation encoding the neural ODE output The pre-hidden state at the moment, For the The encoded input hidden state at time t, is the fully connected layer in the encoding neural ODE, To encode the parameters of the neural ODE, GRU stands for encoding gated recurrent unit; For the Encoded input features at time instant; Indicates The mechanistic characteristics of the moment;

[0111] The first Time to The melt pool characteristic sequence at time , No. Time to The laser moving direction and the protective airflow angle sequence at each moment and Time to The mechanism characteristic sequence of moments After splicing, the fusion coding features are obtained , and input into the encoder module for processing.

[0112] Step 6.3.2: Use formula (10) to calculate the context vector at the i-th moment ; thus obtaining the label The corresponding time from the i-th moment to the Sequence of context vectors at each moment ;

[0113] (10)

[0114] In formula (10), is the encoded input hidden state at the kth moment, represents the attention weight of the melting pool feature at the kth moment corresponding to the i-th moment;

[0115] Step 6.3.3: The decoder is a neural ODE-GRU based decoder, and uses equations (11) to (12) to calculate the first The decoded input hidden state at time , thus obtaining Time to The decoded hidden state sequence at time ;

[0116] (11)

[0117] (12)

[0118] In formula (11) to formula (12), Denotes the output of the decoded neural network ordinary differential equation decoded neural ODE The pre-hidden state at the moment, For the The decoded input hidden state at time t, To decode the fully connected layer in the neural ODE, To decode the parameters in the neural ODE; For the Decoded input features at time instant;

[0119] The context vector sequence , Laser moving direction and protective airflow angle sequence and mechanism characteristic sequence After splicing, the fusion features are obtained , and input into the decoder module to get Time to The decoder hidden state sequence at time ;

[0120] The decoder hidden state sequence After passing through a fully connected layer, the output Time to Moment-by-moment prediction of melt pool morphology feature sequences .

[0121] Step 7: Use formula (13) to construct the loss function :

[0122] (13)

[0123] In formula (10), the first term is the mean square error of the molten pool morphology characteristics, and the second term is the mean square error of the first-order difference of the molten pool morphology characteristics, which is used to improve the prediction accuracy of the molten pool morphology mutation. is the predicted value of the melt pool morphology feature sequence, is the difference value of the molten pool morphology feature sequence, is the predicted value of the first-order difference of the molten pool morphology feature sequence, To determine the parameters of the relative importance of the loss term, in this example, is 0.8.

[0124] Step 8: Use the gradient descent method to train the molten pool morphology feature prediction model, and calculate the total loss function loss to update the model parameters. When the total loss function loss converges, the trained molten pool morphology feature prediction model is obtained, which is used to predict the future molten pool morphology feature evolution process during the selective laser melting process.

[0125] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0126] In this embodiment, a computer-readable storage medium stores a computer program on the computer-readable storage medium, and the computer program executes the steps of the above method when executed by a processor.

Claims

1. A mechanism-guided data-driven prediction method for the molten pool morphology evolution in the selective laser melting process, characterized in that: The following steps are involved: Step 1: Use a temperature-calibrated high-speed infrared camera to collect the molten pool image of the selective laser melting process in a coaxial manner and at a fixed frequency, and construct a molten pool image dataset ;in, represents the melt pool image at the i-th moment, is the number of melt pool images; L and H are the length and height of the melt pool image respectively; Step 2: By taking the melt pool image at the i-th moment Processing is performed to extract the morphological features of the molten pool at the i-th moment , including: length ,width ,area ,perimeter , aspect ratio , thereby constructing a melt pool morphology feature dataset ; Step 3: Melt pool image at this moment To Melt pool image at this moment As a sample , the morphological characteristics of the molten pool at the i-th moment To Morphological characteristics of the molten pool at each moment As a sample Tags ; Morphological characteristics of the molten pool at each moment To Morphological characteristics of the molten pool at each moment As a sample Tags Corresponding historical melt pool feature sequence ; Step 4: Calculate the theoretical temperature field of the molten pool surface at the i-th moment based on material parameters, process parameters and scanning strategy ; thus obtaining the Time to The theoretical temperature field of the molten pool surface at time t, thus obtaining the label The corresponding theoretical temperature field sequence of the molten pool surface ; Step 5: Calculate the labels The corresponding angle between the laser moving direction and the protective airflow direction in the clockwise direction ;in, It represents the angle between the laser moving direction and the protective airflow direction in the clockwise direction at the i-th moment; Step 6: Construct a prediction network for the molten pool morphology evolution, including: theoretical temperature field feature extraction module, improved attention mechanism module, mechanism and data feature fusion module, and , and Process it and get Time to Moment-by-moment prediction of melt pool morphology feature sequences ; Step 7: Use formula (13) to construct the loss function : (13) In formula (10), is the predicted value of the melt pool morphology feature sequence, is the difference value of the molten pool morphology feature sequence, is the predicted value of the sequence difference of the molten pool morphology characteristics, A parameter to determine the relative importance of this loss term; Step 8: Use the gradient descent method to train the molten pool morphology feature prediction network, and calculate the total loss function loss to update the model parameters. When the total loss function loss converges, the trained molten pool morphology feature prediction model is obtained, which is used to predict the future molten pool morphology feature evolution process during the selective laser melting process.

2. According to claim 1, a mechanism-guided data-driven method for predicting the evolution of molten pool morphology in a selective laser melting process is characterized in that: Step 2 includes the following steps: Step 2.1: Liquidus temperature of a given powder material , extract the melt pool image at the i-th moment In, with The absolute value of the difference is less than the error The pixel points are extracted and the least square method is used to fit the extracted pixel point set into the i-th closed curve ; Step 2.2: Move two first straight lines parallel to the direction of laser movement from the center of the spot to both sides. When the two first straight lines intersect the i-th closed curve When there is only one intersection point, calculate the distance between the two first straight lines to obtain the i-th closed curve Width; Move two second straight lines perpendicular to the moving direction of the laser from the center of the spot to both sides. When there is only one intersection point, calculate the distance between the two second straight lines to obtain the i-th closed curve Length; According to the instantaneous field of view of the high-speed infrared camera, the i-th closed curve After converting the length, width, area and perimeter of the molten pool, the length of the molten pool is obtained. ,width ,area and perimeter ; The aspect ratio of the melt pool is obtained by calculating the ratio between the length and width of the melt pool. .

3. The mechanism-guided data-driven selective laser melting process molten pool morphology evolution prediction method according to claim 2 is characterized in that: Step 4 includes the following steps: Step 4.1: Take the center of the laser spot at the i-th moment As the base point, create a rectangle with the length along the laser moving direction at the i-th moment and the width perpendicular to the laser moving direction at the i-th moment; among them, the center of the laser spot at the i-th moment is The distances to the front and back edges of the rectangle are set to and , The distance to the left and right edges is set to ; Step 4.2: Set the point set resolution to , extracted from the rectangle The coordinates of the points ,in, is the coordinate of the jth point at the i-th moment in the working coordinate system of the selective laser melting equipment; Step 4.3: Calculate the label using equations (1) to (3) The corresponding theoretical temperature of the jth point at the i-th moment : (1) (2) (3) In formula (1) to formula (3), To calculate the The number of historical laser scanning points used when measuring the theoretical temperature at the moment; is the thermal diffusion coefficient of the powder alloy, is the density of the powder material, is the initial temperature of the environment; For The specific heat capacity at the temperature, m is the coefficient of the specific heat capacity changing with temperature; P is the laser power, is the absorption coefficient of the powder particles, is the beam radius, is the beam intensity distribution factor of the laser in the XY plane, is the center of the laser spot at the i-th moment The coordinates in the XY plane of the working coordinate system, is the time-integrated variable; is the time-integrated variable The time difference to the i-th moment; is the time-integrated variable The integrated parameter of laser properties at the i-th moment, h is the height of the body heat source; Step 4.4: Follow the process in step 4.3 to calculate the label Corresponding Middle Point to Points and Point to The theoretical temperature at the point is obtained, so as to obtain the theoretical temperature field of the molten pool surface at the i-th moment .

4. The mechanism-guided data-driven selective laser melting process molten pool morphology evolution prediction method according to claim 3 is characterized in that: Step 5 includes the following steps: Step 5.1: Calculate the label using formula (4) The angle between the laser moving direction and the protective airflow direction in the clockwise direction at the corresponding moment i : (4) In formula (4), is the laser moving unit direction vector at the i-th moment, is the unit direction vector of the protective airflow at the i-th moment, is the function for calculating the clockwise angle; Step 5.2: Follow the procedure in step 5.1 to calculate the Time to The angle between the laser moving direction at the moment and the protective airflow direction in the clockwise direction and the Time to The angle between the laser moving direction at the moment and the protective airflow direction in the clockwise direction is obtained to obtain the label Corresponding laser moving direction and protective airflow angle sequence .

5. The mechanism-guided data-driven selective laser melting process molten pool morphology evolution prediction method according to claim 4 is characterized in that: Step 6 includes the following steps: Step 6.1: The theoretical temperature field feature extraction module includes: two convolutional pooling layers and one fully connected layer; Will Input the theoretical temperature field feature extraction module for processing and obtain the label Corresponding mechanism characteristic sequence ,in, Indicates The mechanistic characteristics of the moment; Step 6.2: The improved attention mechanism module includes: a spatiotemporal difference extraction unit, a fully connected layer and a Softmax function; The spatiotemporal difference extraction unit uses equations (5) to (7) to calculate the Moment and Time difference , No. Laser spot center at the moment With Laser spot center at the moment Distance in X direction , Y direction distance , Euclidean distance , and constitute the The time-space difference sequence of the historical melt pool corresponding to the moment , thus obtaining Time to The time-space difference sequence of the historical melt pool corresponding to the moment ; (5) (6) (7) In formula (6), For the The center of the laser spot at the moment Coordinates in the XY plane; Will Input to the fully connected layer for processing, and get the The spatiotemporal characteristics of moments , and continue to input it into the Softmax function for processing, and get the Time to The molten pool at the moment Attention weight of the moment melt pool ; thus obtaining the label The corresponding historical melt pool Time to Attention weight at each moment ;in, Indicates The attention weight of the melting pool at the moment; where, Indicates the first moment corresponding to The spatial and temporal characteristics of the moment, Indicates the first moment corresponding to Attention weight of melt pool features at each moment; Step 6.3: The mechanism and data feature fusion module includes: an encoder module and a decoder module; Step 6.3.1: The encoder module is an encoder based on the encoding neural ODE-GRU structure, and uses equations (8) to (9) to calculate the first The encoded input hidden state at time , thus obtaining Time to The sequence of encoded hidden states at time ; (8) (9) In formula (8) to formula (9), represents the output of the encoded neural ODE The pre-hidden state at the moment, For the The encoded input hidden state at time t, is the fully connected layer in the encoding neural ODE, To encode the parameters of the neural ODE, GRU stands for encoding gated recurrent unit; For the Encoded input features at time instant; Indicates The mechanistic characteristics of the moment; Step 6.3.2: Use formula (10) to calculate the context vector at the i-th moment ; thus obtaining the label The corresponding time from the i-th moment to the Sequence of context vectors at each moment ; (10) In formula (10), is the encoded input hidden state at the kth moment, represents the attention weight of the melting pool feature at the kth moment corresponding to the i-th moment; Step 6.3.3: The decoder is a decoder based on the decoding neural ODE-GRU, and uses equations (11) to (12) to calculate the first The decoded input hidden state at time , thus obtaining Time to The decoded hidden state sequence at time ; (11) (12) In formula (11) to formula (12), represents the output of the decoded neural ODE The pre-hidden state at the moment, For the The decoded input hidden state at time t, To decode the fully connected layer in the neural ODE, To decode the parameters in the neural ODE; For the Decoded input features at time instant; Will After passing through a fully connected layer, the output Time to Moment-by-moment prediction of melt pool morphology feature sequences .

6. An electronic device, comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the method for predicting the molten pool morphology evolution in the selective laser melting process as described in any one of claims 1-5, and the processor is configured to execute the program stored in the memory.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the morphology evolution of the molten pool in the selective laser melting process according to any one of claims 1 to 5 are executed.

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