Optimization of Turning Process Parameters by Genetic Algorithm Approach

Year : 2025 | Volume : 15 | Issue : 01 | Page : 33 41
    By

    Yash Dhabarde,

  • Prashant Kamble,

  • Rakesh Adakane,

  • Rajesh Bodkhe,

  • Shilpa Sahare,

  1. Student, Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India
  2. Professor, Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India
  3. Professor, Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India
  4. Professor, Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India
  5. Professor, Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, Maharashtra, India

Abstract

In this research, turning parameters were optimized through a genetic algorithm for the purpose to minimize surface roughness and to maximize the material removal rate. High finish quality is guaranteed through minimum surface roughness, and efficient process planning is facilitated through maximum material removal rate optimization. For predicting surface roughness and material removal rate with respect to spindle speed, feed rate, and depth of cut, the empirical models were developed through regression analysis. The high-speed steel cutting tool and aluminium alloy 64430 were employed in experiments on a Craft Master TL-20 lathe machine. The optimal machining conditions were determined using the Genetic Algorithm, which was coded using Python’s Distributed Evolutionary Algorithms in Python package. The highest material removal rate of 3463.3918 mm³/min and surface roughness of 0.6574 µm were the optimal results achieved. The validity of the model was established through p-values of 0.1688 for surface roughness and 0.6227 for material removal rate, which were achieved through a paired t-test between experimental and estimated values. This approach provides the industry with the liberty to select parameters for specific application needs.

Keywords: Genetic algorithm, regression modelling, surface roughness, material removal rate, python DEAP.

[This article belongs to Journal of Production Research & Management ]

How to cite this article:
Yash Dhabarde, Prashant Kamble, Rakesh Adakane, Rajesh Bodkhe, Shilpa Sahare. Optimization of Turning Process Parameters by Genetic Algorithm Approach. Journal of Production Research & Management. 2025; 15(01):33-41.
How to cite this URL:
Yash Dhabarde, Prashant Kamble, Rakesh Adakane, Rajesh Bodkhe, Shilpa Sahare. Optimization of Turning Process Parameters by Genetic Algorithm Approach. Journal of Production Research & Management. 2025; 15(01):33-41. Available from: https://journals.stmjournals.com/joprm/article=2025/view=213328


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Regular Issue Subscription Original Research
Volume 15
Issue 01
Received 20/05/2025
Accepted 24/05/2025
Published 10/06/2025
Publication Time 21 Days
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