Multi-objective optimization model of cutting parameters for a sustainable multi-pass turning process

Authors

  • Wahyu Widhiarso Universitas Setia Budi Surakarta https://orcid.org/0000-0002-7668-2453
  • Ibnu Abdul Rosid Universitas Jenderal Achmad Yani Yogyakarta
  • Rieska Ernawati Universitas Islam Sultan Agung Semarang

DOI:

https://doi.org/10.30656/jsmi.v7i1.5747

Keywords:

Cutting parameter, MOORA, Multi-objective optimization, Multi-pass turning, Sustainable

Abstract

The turning process involves the linear removal of material from the work-piece and requires a relatively high amount of energy. The high energy consumption of the machining process increases carbon emissions, which affects the environment. Moreover, production costs will rise as the cost of energy rises. Energy savings during the machining process are crucial for achieving sustainable manufacturing. In order to determine and optimize the cutting parameters, this study creates a multi-pass turning processes optimi¬zation model. It considers cutting speeds, feed rates, and depth of cut. In this study, the model uses multi-objective optimization by incorporating three objective functions: processing time, energy consumption and product¬ion costs. OptQuest completed the proposed model in Oracle Crystal Ball software, then normalized and weighted the sum. Ordering preferences, the Multi-Objective Optimization based on Ratio Analysis (MOORA) approach is utilized. It ranks items based on their higher priority values. This paper provides a numerical example to demonstrate the application of an optimi¬zation model. Based on the preference order ranking results, the optimal values for three objective functions are as follows: total processing time of 4.953 min, the total energy consumption of 5.434 MJ, and total production cost of 395.21$.

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Published

2023-06-14

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Vol. 7 No. 1 (2023): June

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Research Article

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[1]
“Multi-objective optimization model of cutting parameters for a sustainable multi-pass turning process”, j. sist. manaj. ind., vol. 7, no. 1, pp. 1–14, Jun. 2023, doi: 10.30656/jsmi.v7i1.5747.
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