Optimizing sustainable machining for magnesium alloys: a comparative study of GRA and TOPSIS

Abstract

This study delves into a comparative analysis of two multi-criteria decision analysis methods, Grey Relational Analysis (GRA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to fine-tune sustainable machining parameters for AZ31 magnesium alloy. In the realm of straight cutting, TOPSIS reveals an optimal configuration, featuring a spindle speed of 1000 rpm, a feedrate of 375 mm/s, and a depth of cut at 0.5 mm. Conversely, GRA prescribes a different setup: spindle speed at 1000 rpm, feedrate at 1900 mm/s, and depth of cut at 2 mm. In the context of angular cutting, TOPSIS suggests a spindle speed of 1000 rpm, a feedrate of 375 mm/s, and a depth of cut at 0.5 mm, while GRA advocates a spindle speed of 1100 rpm, a feedrate of 1900 mm/s, and a depth of cut at 0.5 mm. A closer look reveals that the Sustainability Assessment Index (SAI) signals that GRA delivers superior sustainability outcomes across both cutting modes. These findings present invaluable insights into the optimization of sustainable machining processes, providing decision-makers with the tools to select the most appropriate approach for their unique requirements. The horizon of future research could expand to include additional parameters and materials, further enriching the tapestry of sustainable machining practices.

Graphical Abstract

Keywords:

• Multi-criteria decision analysis
• GRA
• TOPSIS
• sustainable machining
• optimization
• magnesium alloy
• sustainability assessment index

REVIEWING EDITOR:

• Zude Zhou
• Wuhan University of Technology
• China

SUBJECTS:

• Technology
• Engineering & Technology
• Industrial Engineering & Manufacturing
• Operations Research

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Government of Pakistan Planning Commission under Grant Establishment of National Center in Big Data & Cloud Computing (NCBC). The funding provided by the Government of Pakistan Planning Commission was instrumental in enabling the execution of this research project.

Notes on contributors

M. Atif Saeed

Muhammad Atif is currently a PhD student in the field of Computer Science at SZABIST University. He also holds the position of Assistant Professor at the same institution. His research interests encompass a variety of topics including mechanical design, optimal machining, renewable energy, embedded systems, and automation.

Faraz Junejo

Faraz Junejo obtained his PhD (Mechatronics) degree from Loughborough University, UK. He is currently working as Professor in Mechatronics Engineering Department at SZABIST, Karachi. His research interests include Machine Vision, Renewable Energy, Condition monitoring, Robotics and Engineering Mechanics. He is an active member of Higher Education Commission’s National Curriculum review committee for both Mechatronics and Mechanical Engineering.

Imran Amin

Imran Amin is an Professor and Head of Department at SZABIST Institute in Karachi. He has served as the Head for Center of Renewable Energy and Research from 2009 to 2012.