Comparative Efficiency of Artificial Neural Network to Predict Spring-Back Value in U-Bending Process of High Strength Steel

Abstract:

High strength steel (HSS) was used as raw material in the automotive industry, which required lightweight and high strength, such as chassis and etc. However, the strength and hardness of the steel are relatively high, leading to the low permeability and large spring-back occurring after forming operation. As a result, the work piece is not shaped desire. This research proposes neural network for predicts the spring back values, in U-bending process, that the materials were differences in mechanical properties, such as SPFH590 (JIS) and SPEC980Y (JIS). In the experiment, the input factors for predictable data consists as the punch radius (RP), die radius (Rd), clearance (Cl) and counter punch force (Fc). After that, the input data were analyzed relation with spring-back values by the Pearson Correlation of One-tailed. Next, It was selected by Leave-one-out and k-fold Cross validation (K-fold and LOOCV), to improve efficiency of the prediction process. Moreover, the result was a measurable performance with Root Mean Square Error (RMSE) technique, equal to 0.788 and 2.10 respectively. In the final analysis, the neural network is effective to predict the Spring-back values of SPFH590 (JIS) rather than SPEC 980Y (JIS) in U-bending process.

• Periodical: Applied Mechanics and Materials (Volume 749)
• Pages: 319-326
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.749.319
• Online since: April 2015
• Authors: Arkhom Songkroh*, Bhadpiroon Sresomroeng
• Keywords: Artificial Neural Network, Cross-Validation, High Strength Steel, Spring-Back, U-Bending Process

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