Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

  • A. T. Abbas
  • D. Yu. Pimenov
  • I. N. Erdakov
  • T. Mikolajczyk
  • E. A. El Danaf
  • M. A. Taha
Open Access
ORIGINAL ARTICLE
  • 263 Downloads

Abstract

High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of high-strength steel featuring three factors—cutting speed V, feed rate f, and depth of cut t—on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.

Keywords

Artificial neural network High-strength steel Turning operation Optimization Edgeworth–Pareto method Surface roughness Data mining 

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Copyright information

© The Author(s) 2017

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • A. T. Abbas
    • 1
  • D. Yu. Pimenov
    • 2
  • I. N. Erdakov
    • 3
  • T. Mikolajczyk
    • 4
  • E. A. El Danaf
    • 1
  • M. A. Taha
    • 5
  1. 1.Department of Mechanical Engineering, College of EngineeringKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Automated Mechanical EngineeringSouth Ural State UniversityChelyabinskRussia
  3. 3.Department of Pyrometallurgical and Casting TechnologiesSouth Ural State UniversityChelyabinskRussia
  4. 4.Department of Production EngineeringUTP University of Science and TechnologyBydgoszczPoland
  5. 5.Department of Mechanical Design and Production, Faculty of EngineeringZagazig UniversityZagazigEgypt

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