OPTIMIZATION OF SURFACE ROUGHNESS IN MICRO-MILLING OF NITI SHAPE MEMORY ALLOYS USING MODIFIED DIFFERENTIAL EVOLUTION ALGORITHM

Authors

  • MOHD ZAKIMI ZAKARIA Dynamics Modeling & Control System Research Group (DyCoS), Department of Mechanical, Faculty of Mechanical Engineering & Technology Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • MOHD SAZLI SAAD Dynamics Modeling & Control System Research Group (DyCoS), Department of Manufacturing, Faculty of Mechanical Engineering & Technology Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • ZAILANI ZAINAL ABIDIN Department of Manufacturing, Faculty of Mechanical Engineering & Technology Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
  • AZUWIR MOHD NOR Dynamics Modeling & Control System Research Group (DyCoS), Department of Manufacturing, Faculty of Mechanical Engineering & Technology Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • MOHAMAD EZRAL BAHARUDIN Dynamics Modeling & Control System Research Group (DyCoS), Department of Manufacturing, Faculty of Mechanical Engineering & Technology Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia.
  • ROSHALIZA HAMIDON Department of Manufacturing, Faculty of Mechanical Engineering & Technology Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, 02600 Arau, Perlis, Malaysia

DOI:

https://doi.org/10.11113/jm.v48.650

Keywords:

Micro-milling, Nickel-Titanium, Differential Evolution, Surface Roughness, Minimum Quantity Lubrication

Abstract

This study is conducted to observe the optimal effect of feed per tooth to cutting edge radius ratio, nanoparticle composition in percentage, and cutting environment on the surface roughness (Ra) of Nickel-Titanium (NiTi) Shape Memory Alloys (SMAs) in micro-milling. NiTi alloys are challenging to machine due to their high ductility and temperature sensitivity, often resulting in significant Ra. In this connection, surface quality was enhanced through Minimum Quantity Lubrication (MQL) in combination with solid lubrication using Boron Nitride (BN) nanoparticles. The Taguchi method developed a regression model from the experimental machining data. This Ra regression model was used as a fitness function for the Modified Differential Evolution (MDE) algorithm. Hence, the outcome of this study demonstrated that the MDE optimization technique can identify the process parameters that achieve reduced Ra. MDE application significantly reduces the Ra to 0.7115 µm, which is lower than experimental data (0.7210 µm) and standard Differential Evolution (DE) optimization (0.7122 µm). In conclusion, this research shows how the parameter optimization process can benefit from a combination of regression modeling with the MDE algorithm, yielding the results necessary for sustainable and efficient NiTi SMA machining.

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Published

2025-11-17

How to Cite

ZAKARIA, M. Z., SAAD, M. S., ZAINAL ABIDIN, Z., MOHD NOR, A., BAHARUDIN, M. E., & HAMIDON, R. (2025). OPTIMIZATION OF SURFACE ROUGHNESS IN MICRO-MILLING OF NITI SHAPE MEMORY ALLOYS USING MODIFIED DIFFERENTIAL EVOLUTION ALGORITHM. Jurnal Mekanikal, 48(2), 201–219. https://doi.org/10.11113/jm.v48.650

Issue

Vol 48 : December 2025

Section

Mechanical

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