Multi-objective Optimization Using Backpropagation Neural Network and Teaching–Learning-Based-Optimization Method in Surface Grinding Under Dry and Minimum Quantity Lubrication Conditions (MQL)

Dinny Harnany, M. Khoirul Effendi*, H. C. Kis Agustin, Bobby O.P. Soepangkat, Sampurno, Rachmadi Norcahyo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The present work focuses on the performance modeling of surface grinding to attain an optimum parameter setting for the minimum coefficient of friction and surface roughness. The experimental data were collected during the surface grinding process using dry conditions and minimum quantity lubrication (MQL) as a clean technology lubricant. The usage material was SKD 61 tool steel. The varied surface grinding parameters were the depth of cut and table speed, wherein each had three levels. The surface grinding operation was performed by using a full factorial design 2 × 3 × 3. Backpropagation neural network (BPNN) was first applied to obtain the modeling of the surface grinding experiment, the objective function, the predictions of coefficient of friction, and surface roughness. The objective function is then modified into a fitness function. Finally, this fitness function is utilized in multi-objective optimization using the teaching–learning-based optimization (TLBO) method to attain the surface grinding parameters’ levels that simultaneously produce a minimum coefficient of friction and surface roughness. Based on our experimental results, the combination of BPNN-TLBO can be applied to simultaneously minimize the coefficient of friction and surface roughness in the grinding of SKD 61 by implementing MQL and setting the feeding speed at 150 mm/s and the depth of cut at 0.01 mm. As the result, the minimum surface roughness is 0.376 μm, and the coefficient of friction is 0.333.

Original languageEnglish
Title of host publicationRecent Advances in Mechanical Engineering - Select Proceedings of ICOME 2021
EditorsIvan Tolj, M.V. Reddy, Achmad Syaifudin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages334-342
Number of pages9
ISBN (Print)9789811908668
DOIs
Publication statusPublished - 2023
Event5th International Conference on Mechanical Engineering, ICOME 2021 - Virtual, Online
Duration: 25 Aug 202126 Aug 2021

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference5th International Conference on Mechanical Engineering, ICOME 2021
CityVirtual, Online
Period25/08/2126/08/21

Keywords

  • Backpropagation neural network
  • Clean technology
  • Minimum quantity lubrication
  • Surface grinding
  • Teaching–learning-based optimization

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