Prediction of characteristic points of single-peak flow curve through statistical technique and artificial neural network

ABSTRACT

Artificial neural network (ANN) is an input–output modeling technique that has been successfully implemented in the field of material science to predict material behavior in terms of constitutive models. The material behavior considered here is the stress–strain behavior of the material at elevated temperatures that is characterized by a single-peak flow curve consisting of characteristic points viz. critical stress, peak stress and steady-state stress. The present work focuses on predicting these stress values along with the corresponding strain values through statistical regression analysis (SRA), ANN, and multi-layer complex neural network (MLCNN) with reasonable accuracy. The flow curves obtained from the axisymmetric compression test of 304LN austenitic stainless steel, performed at constant temperatures and constant strain rates, are used to train the models. The MLCNN has performed best while SRA has performed relatively worst with the current dataset due to rigorous and thorough computation. Both MLCNN and ANN are observed to perform better than SRA because of their non-parametric nature of handling data.

KEYWORDS:

• Constitutive model
• neural network
• peak stress
• critical stress
• steady-state stress
• stainless steel

Acknowledgments

The experimental facilities provided by Indian Institute of Technology Kharagpur, India, is greatly acknowledged by the authors.

Disclosure statement

The authors declare they do not have any conflict of interest.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Debasish Das

Dr. Debasish Das received his B.Tech. and M. Tech. from National Institute of Technology (NIT) Durgapur, West Bengal in 2010 and 2013, respectively. He obtained his Ph.D. from Indian Institute of Technology (IIT) Kharagpur, West Bengal in 2021. He has worked as an Assistant Professor at Punjab Engineering College (PEC) (Deemed to be University), Chandigarh and Mallabhum Institute of Technology (MIT) Bishnupur, West Bengal. He is currently pursuing his Post-doctoral studies at Indian Institute of Science Education and Research, Mohali (IISER) Mohali, Punjab. His research areas include electron and laser beam welding, non-conventional machining, mechanical testing, microstructure, finite element and soft computing-based modelling.

Navneet Thakur

Mr. Navneet Thakur received his B.Tech. in Mechanical Engineering from Jawaharlal Nehru Government Engineering College, Sunder Nagar, India in 2021. He is currently pursuing masters from Punjab Engineering College (Deemed to be University), Chandigarh, India. His focus lies specifically in exploring advanced techniques and processes such as welding, friction stir processing, laser material processing, and additive manufacturing. He is deeply fascinated by these fields and aims to contribute to the development of cutting-edge technologies in these areas.

Matruprasad Rout

Dr. Matruprasad Rout received his B. Tech in Mechanical Engineering, from Biju Patnaik University of Technology, Odisha, India in 2009. He obtained his M. Tech in Production Engineering from the National Institute of Technology (NIT) Rourkela, Odisha, and Ph.D. from Indian Institute of Technology (IIT) Kharagpur, West Bengal, India in 2011 and 2018, respectively. He is currently working as an Assistant Professor in the Department of Production Engineering, National Institute of Technology Tiruchirappalli, Tamil Nadu, India. His research areas include bulk metal forming, mechanical metallurgy, and material characterization