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Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing.

Mozammel Mia1, Grzegorz Królczyk2, Radosław Maruda3

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This study optimizes hard-turning parameters using advanced evolutionary algorithms like teaching-learning-based optimization (TLBO) and bacterial foraging optimization (BFO). TLBO is recommended for its faster convergence and efficiency in smart manufacturing.

Keywords:
cutting temperatureevolutionary algorithmhard turningintelligent optimizationsurface roughness

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Area of Science:

  • Manufacturing Engineering
  • Computational Intelligence
  • Operations Research

Background:

  • Smart manufacturing necessitates intelligent optimization of process parameters to minimize resource waste.
  • Hard-turning processes require efficient methods for parameter selection and optimization.
  • Evolutionary algorithms offer potential for global optimization but face challenges in programming and parameter selection.

Purpose of the Study:

  • To optimize hard-turning process parameters using efficient evolutionary algorithms.
  • To compare the performance of teaching-learning-based optimization (TLBO) and bacterial foraging optimization (BFO) for multi-objective optimization.
  • To identify the most effective algorithm for intelligent and automated hard-turning operations.

Main Methods:

  • Multi-objective optimization of hard-turning parameters using the weighted sum method.
  • Implementation of teaching-learning-based optimization (TLBO) and bacterial foraging optimization (BFO).
  • Comparative analysis of the convergence speed and solution quality of TLBO and BFO.

Main Results:

  • Both TLBO and BFO were successfully applied to optimize hard-turning parameters.
  • The weighted sum method effectively converted multi-objective responses into a single objective.
  • TLBO demonstrated superior performance with faster convergence and efficient global optimization.

Conclusions:

  • The teaching-learning-based optimization (TLBO) approach is recommended for hard-turning process optimization due to its efficiency.
  • This research provides a framework for intelligent and automated optimization in smart manufacturing environments.
  • The findings contribute to reducing material and energy wastage in industrial machining processes.