Experimental and Statistical Evaluation of Drilling Induced Damages in Glass Fiber Reinforced Polymer Composites – Taguchi Integrated Supervised Machine Learning Approach

Giridhar Kamath1, Email

Brajesh Mishra2

Shivam Tiwari3

Akshay Bhardwaj4

Sudheer Sankarankutty Marar5

Sanjay Soni6

Rajesh Chauhan7

Anjappa S B8

1Department of Humanites and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
2Department of Computer Science and Engineering, Rameshwaram Institute of Technology and Management, Lucknow, Uttar Pradesh, 227202, India
3Department of Computer Science and Engineering, GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, 201306, India.
4University Institute of Technology, Himachal Pradesh University, Shimla 171005, India.
5Department of Master of Computer Application, Nehru College of Engineering and Research Centre, Kerala, 680597, India
6Industrial and Production Engineering, Jabalpur Engineering College, Jabalpur, Madhya Pradesh, 482011, India
7Department of Industrial and Production Engineering, University Institute of Technology, Himachal Pradesh University, Shimla, 171005, India.
8Department of Mechanical Engineering, Viswam Engineering College, Madanapalle, Andhra Pradesh, 517325, India

 

Abstract

Glass fiber reinforced polymer (GFRP) composites are gaining on its usage in various sectors. The drilling of GFRP composite is an inevitable machining operation. The anisotropy feature of the polymer composite makes it little difficult-to-machine material. The drilling of GFRP composite is accompanied by delamination damage. Moreover, the quality of drill, characterized by the hole’s surface roughness is also an important response variable to consider. The effect of Feed and drilling speed has been always focused by sevral researchers, to minimize the damages caused during drilling of GFRP composites but very few have considered the drill tool geometry as a affecting parameter. The present study thus, investigates the effect of drill tool geometry along with drilling speed and feed on the delamination damage and surface roughness of the drilled hole. The study indicates that drill geometry has the highest significance on the damages considered in the study and contributes more than 75% towards the variance. The supervised learning approach, interms of linear regression is used in the present work to determine the predicting models for the obtained data. The mathematical models developed using the machine learning approach possess high degree of fitness with all the three R2 values being more than 90%.

Experimental and Statistical Evaluation of Drilling Induced Damages in Glass Fiber Reinforced Polymer Composites – Taguchi Integrated Supervised Machine Learning Approach