Application of Neural Networks for Real-Time Decision Support in Virtual Approval of Brake Components

Lucas Marcon, Alexandre Vieceli, Leandro Corso
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Abstract

This study aims to present a virtual numerical validation procedure for durability in brake system components, using artificial neural networks and based on experimental bench tests. The study focus was concentrated on the drum brake spider component, responsible for mechanically connecting the brake system subassemblies. To develop the validation procedure, engineering software such as ABAQUS, Fe-Safe, Minitab, and MATLAB was used. These were crucial for carrying out stress analyses, statistical data validation, and construction of an Artificial Neural Network (ANN) capable of predicting finite element responses, fatigue life, and supporting real-time decision-making for structural validation of mechanical components. The results obtained from these tools allowed the calibration of a numerical virtual model using the Finite Element Method (FEM) based on mechanical theories and results obtained in bench tests with the brake system, thus, a finite element database was generated for the application of the ANN, containing 130 data from a total of 4,800 possible combinations. The training, validation, and testing of the ANN were determined using a performance analysis algorithm. Finally, the results obtained with the artificial neural network were compared with the results of finite elements and computational fatigue life. The efficiency of the real-time response prediction method was measured using the Mean Squared Error (MSE). With the use of ANN, it was possible to obtain an average error of 0.85% for predicting maximum principal stress and an error of 10.33% for predicting fatigue life. For the classification of fatigue life results, the ANN presented an accuracy of 100%, enabling decision-making in real-time.
应用神经网络为制动器部件虚拟审批提供实时决策支持
本研究旨在基于实验台测试,利用人工神经网络提出制动系统部件耐久性的虚拟数值验证程序。研究重点集中在负责机械连接制动系统组件的鼓式制动器蜘蛛组件上。为了开发验证程序,使用了 ABAQUS、Fe-Safe、Minitab 和 MATLAB 等工程软件。这些软件对于进行应力分析、统计数据验证和构建人工神经网络(ANN)至关重要,该网络能够预测有限元响应和疲劳寿命,并支持机械部件结构验证的实时决策。从这些工具中获得的结果可以根据机械理论和制动系统的台架试验结果,使用有限元法(FEM)校准数值虚拟模型,从而为应用人工神经网络生成有限元数据库,其中包含来自总共 4,800 种可能组合的 130 个数据。使用性能分析算法确定了人工神经网络的训练、验证和测试。最后,将人工神经网络获得的结果与有限元和计算疲劳寿命的结果进行了比较。实时响应预测方法的效率用平均平方误差(MSE)来衡量。使用 ANN 预测最大主应力的平均误差为 0.85%,预测疲劳寿命的平均误差为 10.33%。在对疲劳寿命结果进行分类时,ANN 的准确率达到了 100%,从而实现了实时决策。
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