Multi-response optimization of process parameters for sustainable machining of AISI 1018 steel with palm kernel oil-assisted minimum quantity lubrication technique

R. A. Kazeem*, D. S. Aregbesola, T. C. Jen, I. G. Akande, S. A. Akinlabi, E. T. Akinlabi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In this study, palm kernel oil, an eco-friendly oil, was extracted from its seeds and then examined for thermal and physiochemical characterization. Subsequently, the performance of palm kernel oil was evaluated in comparison with standard mineral oil during the milling of AISI 1018 steel with a double milling tool using the MQL technique. The influence of cutting conditions such as feed rate, spindle speed, and DOC on response variables (cutting temperature and surface roughness) was studied using a Taguchi L9 orthogonal array. Using the TOPSIS approach (a compensatory method that provides a more realistic form of modeling than non-compensatory methods, and allows trade-offs between criteria, where a poor result in one criterion can be voided by a good result in another criterion) an integrated structure for modeling and optimizing the process was developed. The findings showed that palm kernel oil had a 54% oil yield. In terms of machining, palm kernel oil performed much better than mineral oil lubricants. From the results obtained, palm kernel oil reduced the surface roughness by about 15.6% over mineral oil. Effective cooling in palm kernel oil led to reduced cutting zone temperatures, which in turn extended tool life and improved cutting stability. Additionally, ANOVA was used to show the parameters' significant influence on the output responses. The findings showed that feed rate and depth of cut had the greatest impact on the responses for palm kernel oil and mineral oil, respectively.

Original languageEnglish
Number of pages17
JournalInternational Journal on Interactive Design and Manufacturing
Early online date11 Jan 2024
Publication statusE-pub ahead of print - 11 Jan 2024

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