Comparison of Artificial Intelligence Methods for Prediction of Mechanical Properties. Issue 1 (November 2020)

• Record Type: Journal Article
• Authors: Lee, Kyungmin; Hong, Charmgil; Lee, Eun-Ho; Yang, WooHo
• Is Part Of: IOP conference series. Volume 967:Issue 1(2020)
• Publication Date: 2020-11
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/967/1/012031
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: This paper compares artificial intelligence (AI) methods to predict mechanical properties of sheet metal in stamping processes. The deviation of the mechanical properties of each blank leads to unpredicted failures in stamping processes, such as fracture and spring back. The research team of this paper has been building a real time control system for stamping process in a smart factory. In order to facilitate that, it is necessary to predict the mechanical properties of each blank with non-destructive testing. The regression models based on the linear algebraic scheme have traditionally brought reliable results in terms of matching the measured non-destructive testing values to the mechanical properties. With a parallel to algebraic regression models, in recent studies on various domains, AI models have been adopted to improve the accuracy of the end-results and effectiveness of the models. This paper discusses the applicability of AI models for predicting the mechanical properties based on the eddy-current non-destructive testing method. For the study, 6 input features are collected through the eddy-current non-destructive testing to map eddy-current input data to mechanical properties of the blank. Yield stress and uniform elongation were predicted by using five AI methods, i.e., regularized linear regression, support vector regularized linear regression, support vector regression, multi-layer neural network, random forest regression, and gradient boosting regression were compared. The model performance, validated with 20% of test data that are intact during the training phase, is the main discussion point of this paper. Future works to improve the predictive accuracy of AI models is also discussed.