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. Mahaveerkannan3

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 India.

2Department of Mechanical Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh 520007, 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, Tamil Nadu 600077, India

4Department of Mechanical Engineering, Chandubhai S Patel Institute of Technology, Charusat University,  Gujarat 388421, India

5Department of Computer & Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan 303007, India

6Department of Mechanical Engineering, Anjuman Institute of Technology and Management, Bhatkal, Karnataka 581320, India

7Department of Environment Impact Assessment, Horizon Ventures, Bengaluru, Karnataka 560094, 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