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All Journal Indonesian Journal of Industrial Engineering & Management
Ozule, Chukwuka Prosper
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Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering

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Optimization of MQL-Turning Process Parameters to Produce Environmentally-Benign AISI 4340 Alloy with Nano-Lubricants using Cuckoo Search Algorithm Ozule, Chukwuka Prosper; Oke, Sunday Ayoola; Rajan, John; Jose, Swaminathan; Oyetunji, Elkanah Olaosebikan; Adedeji, Kasali Aderinmoye; Nwankiti, Ugochukwu Sixtus
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 5, No 2: June 2024
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v5i2.22728

Abstract

The current research consists of a machining process involving AISI steel where the input parameters are the cutting depth, feed rate and cutting speed while the responses include the cutting force, surface roughness and tool wear. Usually, heat is generated during the turning process and various machining processes, and to reduce it, coolants are considered. In this work, CuO and Al2O3 were used as nano lubricants (MQL). Data obtained from the machining process were inserted into Minitab 18 software where quadratic objective functions were formulated as related to each output concerning the input parameters. Objective functions were optimized with the aid of C++ programming code. The cuckoo search algorithm was used for the optimization process of the work. This work clearly shows a reduction of the output parameters that is, cutting force from 243N to 127.20N, surface roughness from 0.66µm to 0.368µm and tool wear from 0.069mm to 0.0046mm using CuO as the nano lubricant. While using Al2O3, cutting force was lowered from 363N to 197.63N, surface roughness from 1.98µm to 0.148µm and tool wear from 0.219mm to 0.063mm. This clearly shows that using CuO helps to obtain a better cutting force coupled with elongation of the tool life but Al2O3 best gives a better surface finish.
Evaluation of Ecological Minimum Quantity Lubrication Turning of AISI 4340 Alloy for Parametric Choices Using the Distance from Average Solution (EDAS) Method Ozule, Chukwuka Prosper; Oke, Sunday Ayoola; Rajan, John; Oluwo, Adeyinka; Oyekeye, Manasseh Olusegun
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 5, No 3: October 2024
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v5i3.22752

Abstract

Flood-cutting fluids and minimum quantity lubrication (MQL) schemes are associated with high cutting fluid costs and health effects, which should be regulated through the selection and optimization of parameters. This paper applies the distance from average solution (EDAS) method to assess the criteria value regarding three alternatives (cutting depth, cutting speed and feed) using CuO nano lubricants discharged in minimum quantity lubrication. Three responses, including beneficial (cutting speed, feed and cutting depth) and non-beneficial (cutting force) responses were used. A novel weight determination scheme based on the beneficial and non-beneficial criteria was established for the first time as inputs to the EDAS method. The weights established 0.1504, 0.2832, 0.2832 and 0.2832 for cutting force, cutting speed, feed and cutting depth, respectively. The results show a multi-modal best performance of 0.8438, which occurs in multiple experimental trials of 2, 4, 5, and 8.  The optimization implemented in this study uses all inputs and the CuO nano lubricant was considered in each case using a spreadsheet for the evaluation. Case study data illustrating the uniqueness of the method using the literature data shows that EDAS is robust enough to be applied in machining activities.
Co-Authors Adedeji, Kasali Aderinmoye Jose, Swaminathan Nwankiti, Ugochukwu Sixtus Oke, Sunday Ayoola Oluwo, Adeyinka Oyekeye, Manasseh Olusegun Oyetunji, Elkanah Olaosebikan Rajan, John