Research On PCB Component Warpage Prediction And Reflow Process Inversion Technology Based On Machine Learning

Large printed circuit board components have become more common as electronic products have evolved toward multi-function and high integration,and in the face of more complex application environments,higher standards for their dependable assembly have been proposed.According to studies,70% of electronic device failures are due to solder joint failure during the assembly process,and as the size of the device increases,the probability of solder joint failure increases.Because the materials in the PCB assembly have different coefficients of thermal expansion,a large warpage deformation occurs in the traditional welding process,and the reliability of the electronic device is greatly reduced,so an efficient welding process is required.For the warpage deformation defects generated by the PCB components in the vacuum vapor phase welding environment,the mechanism analysis of the vacuum vapor phase welding was first performed,the process parameter settings were set,the structure and parameters of the study were defined,and finally the welding experiments were completed for the PCB components,and the warpage deformation data was collected,and the warpage deformation data set of the study was established,to lessen the likelihood of warpage deformation Because of the benefits of stable welding temperature,uniform heating of components,and reduced void rate,vacuum vapor phase welding has become popular.As a result,in this paper,the warpage deformation of PCB components is used as the research object,and the relevant warpage deformation data is obtained by vacuum vapor phase welding experiments,and the warpage deformation prediction model and inversion model are established by machine learning methods,and the warpage prediction deformation prediction and process parameter inversion based on the machine learning PCB components are finally completed by combining the two models.Aiming at warpage deformation defects caused by PCB components in the vacuum vapor phase welding environment,the principles of vacuum vapor phase welding technology are first briefly explained,and the process parameters in the relevant welding process are expounded;the influencing factors of component warpage in the welding process are analyzed,the welding experiment design for different PCB components is carried out,and the experimental collection device is constructed.The objective function is established with the goal of welding warpage deformation of PCB components,the model structure is designed based on cn N network and LSTM network,the network structure is optimized and parameter debugged,the weights of the model are adjusted by the Adopt mechanism,and finally the PCB component warpage deformation prediction model based on CNN-LSTM is built.This predictive model predicts the amount of warpage deformation,and experimental verification shows that the model’s accuracy reaches 88 percent.The relationship between structural parameters,layout parameters,and warpage deformation is obtained using the CNN-LSTM warpage deformation prediction model,the warpage deformation of components is targeted,the model structure is designed using machine learning inversion,and the vacuum vapor phase welding process parameter inversion model for warpage is established through optimization and parameter setting of the network structure,and subsequent verification.The model’s vacuum vapor phase welding process parameters are more accurate,and the inversion model’s accuracy reaches84 percent.