Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method

Area of Science:

• Manufacturing Engineering
• Materials Science
• Mechanical Engineering

Background:

• Optimizing machining processes is crucial for efficiency and product quality.
• Dry machining environments present unique challenges for parameter selection.
• Taguchi's design of experiments offers a systematic approach to process optimization.

Purpose of the Study:

• To optimize process parameters for material removal rate (MRR) and surface roughness (Ra) in dry turning operations.
• To identify the most influential parameters affecting MRR and Ra.
• To explore multi-response optimization using Taguchi and TOPSIS methods.

Main Methods:

• Utilized Taguchi's design of experiment, specifically the L9 orthogonal array.
• Analyzed Signal-to-Noise (S/N) ratios for both larger-the-better (MRR) and smaller-the-better (Ra) approaches.
• Conducted confirmation tests and main effects plots analysis.

Main Results:

• Optimal parameters for MRR: cutting speed (level 3), feed (level 2), depth of cut (level 3).
• Optimal parameters for Ra: cutting speed (level 1), feed (level 1), depth of cut (level 3).
• Depth of cut significantly impacts MRR, while cutting speed significantly impacts Ra.

Conclusions:

• Achieved optimized process parameters for distinct MRR and Ra goals in dry turning.
• Identified key parameters influencing machining responses.
• Demonstrated the potential of combining Taguchi and TOPSIS for multi-response optimization in manufacturing.