A novel real time sensing framework for assessment of thrust force in drilling of composites using Taguchi and NSGA-II

Author:

David Amos Gamaleal1ORCID,Ramalingam Vimal Samsingh2,Sundarsingh Esther Florence3

Affiliation:

1. Department of Mechanical Engineering, Panimalar Institute of Technology, Chennai, Tamil Nadu, India

2. Department of Mechanical Engineering, SSN College of Engineering, Chennai, Tamil Nadu, India

3. Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Chennai, Tamil Nadu, India

Abstract

Monitoring thrust during drilling operations is critical to optimizing drilling parameters and ensuring safe and efficient drilling. In this study, we will monitor variations in thrust force during the drilling process using a sensor coated with carbon black. These sensors measure changes in electrical resistance when the sensor is subjected to a mechanical load, such as drilling force. Carbon black at 5 wt% was used, which was embedded in a fiberglass composite. For this study, an orthogonal arrangement of Taguchi’s L27 sequence was used, with drill diameter D, feed rate F, and spindle speed S as machining parameters. The thrust force was measured with a force dynamometer and the resistance change was measured simultaneously with the developed new system. Analysis of variance was used to find the optimal parameters, and in this study, a mathematical model was proposed to measure the thrust force directly using the electrical resistance detected. NSGA-II algorithm was used to predict solutions for the given parameters for comparison. A comprehensive way of drilling process and data-driven decisions to improve drilling performance and safety was studied, and a correlation between actual thrust and calculated thrust of 0.91 was found. The proposed system was able to monitor and continuously record thrust force in real time during drilling, with electrical resistance as the most important factor with an error percentage of less than 8% compared to the NSGA-II predictions of less than 5%.

Publisher

SAGE Publications

Subject

Mechanical Engineering

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