Abstract

The structural strength evaluation of crash boxes is predicted by machine learning in this study. The training data was obtained from the dynamic elastic plastic analysis of the crash box. The input physical quantities are barrier angle, box thickness, material properties and mass equivalent to vehicle weight. The output physical quantity is the reaction force. Buckling occurs in the analysis and different directions of corruptions are one of the most interesting phenomenon from a point of engineering view.

Abstract

The objective of this study is to predict the degree of danger to the human body from motion information such as acceleration, velocity and displacement during a collision between a car and a human body. As a preliminary step, the maximum bending moment that occurs in the leg was predicted using a convolutional neural network. The responses which are represented by learning data generated by 1D-CAE system. A number of training data sets are varied in order to show the enough number to predict. The predictor's accuracy is evaluated by the test data sets. We'd like to discuss necessisty of a total number of training data sets and effectiveness of data augmentation technique. In addition, the technique to utilize classification by the t-SNE method to improve accuracy is also examined. t-SNE is based on classification algorithm, however an engineering interpolation should be computed based on physical meanings and influential parameters.

Abstract

A convolutional neural network, which reproduce a function by data, was used to predict the amount of warp distortion of a four-layer circuit board in a reflow soldering process. The data used for training are material properties such as Young's modulus, board thickness, and residual copper content as input data, and the predicted warp distortion data is the amount of warpage of the circuit board obtained from the measurement. Since a number of distortion data to be predicted was insufficient to be used for training, a newly proposed data augmentation method used to increase a number of data. The augmentation method is evaluated through the result of the predictions and discussed.