Statistical Evaluation of Machining Parameters in Drilling of Glass Laminate Aluminum Reinforced Epoxy Composites using Machine Learning Model

Nanjangud Mohan1,Email

Sd. Abdul Kalam2,Email

R. Mahaveerakannan3

Maulik Shah4

Jitendra Singh Yadav5

Vivek Sharma5

Padmayya S Naik6

Dhanaraj Bharathi Narasimha7

1Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
2Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520007, Andhra Pradesh, India
3Department of Artificial Intelligence Institute of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600077, Tamil Nadu, India
4Department of Mechanical Engineering, Chandubhai S Patel Institute of Technology, Charusat University, Changa 388421, Gujarat, India
5Department of Computer & Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur 303007, Rajasthan, India
6Department of Mechanical Engineering, Anjuman Institute of Technology and Management, Bhatkal 581320, Karnataka, India
7Department of Environment Impact Assessment, Horizon Ventures, Bengaluru 560094, Karnataka, India

Abstract

Glass laminate aluminum reinforced epoxy (GLARE), which is used in the aerospace industry, is being researched as a primary fiber reinforced metal composite material. This work presented attempts to construct empirical correlations to predict the thrust force in drilling GLARE composites with a commercial 10 mm solid carbide drill. The empirical correlations were created using multiple linear regression models, the most basic and simplest type of machine learning (ML) model. Response surface methodology's face-centered central composite design is used as the base for experimental design. Machining parameters considered are speed (rpm) and feed rate (mm/min). The results revealed that combining a low drilling speed with a low feed rate minimizes the thrust forces while drilling GLARE composites. Furthermore, the provided machine learning-based linear regression model can be utilized to accurately estimate the thrust force in drilling GLARE composites within the parameters and restrictions presented.

Statistical Evaluation of Machining Parameters in Drilling of Glass Laminate Aluminum Reinforced Epoxy Composites using Machine Learning Model