Parametric Optimization of CNC Milling of Aluminium 7075 Using the TOPSIS Multi-Criteria Decision-Making Method

Technical Summary: Parametric Optimization of CNC Milling of Aluminium 7075 Using TOPSIS

Problem Statement
The machining of Aluminium 7075, a high-strength alloy critical for aerospace and defense applications, presents a complex multi-objective optimization challenge. While conventional parameter selection often relies on operator experience or trial-and-error, this approach frequently fails to provide a satisfactory compromise between competing objectives. Specifically, minimizing Specific Cutting Energy (SCE) to improve energy efficiency often conflicts with minimizing resultant cutting forces to ensure process stability and reduce tool loading. The study addresses the need for a systematic framework to simultaneously optimize these non-beneficial responses (SCE and cutting force) under dry machining conditions, where conflicting trends in parameter effects make single-objective optimization insufficient.

Methodology
The research employed a structured experimental and analytical approach:

• Experimental Design: A Taguchi L27 orthogonal array was utilized to investigate the effects of three controllable parameters—spindle speed, feed rate, and depth of cut—each at three levels. The experiments were conducted on a CNC vertical machining centre using a tungsten carbide cutter under dry conditions.
• Data Acquisition: Cutting forces were measured using a KISTLER 9257B piezoelectric dynamometer connected to a National Instruments cDAQ-9174 system, while spindle power was monitored via a Hioki PW3198 power-quality analyser.
• Response Calculation: Two primary responses were derived: Resultant Cutting Force (N) and Specific Cutting Energy (J/mm³). Both were treated as non-beneficial criteria (where lower values are preferred).
• Optimization Technique: The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied to rank the 27 experimental runs. The method normalized the data, applied weights (initially 0.50 for SCE and 0.50 for cutting force), and calculated a closeness coefficient ($C_i$) based on the distance from the positive ideal solution (minimum values) and the negative ideal solution (maximum values).
• Validation: A sensitivity analysis was conducted by varying the weights of the two responses (e.g., 40:60, 30:70, 20:80, 10:90) to test the robustness of the identified optimum.

Key Results

• Parameter Effects:
  • Spindle Speed: Increasing spindle speed generally reduced the resultant cutting force. However, its effect on SCE was less monotonic; SCE tended to increase at low feed rates with higher speeds but decreased at higher feed rates.
  • Feed Rate: A clear trade-off was observed. Increasing the feed rate significantly reduced SCE (improving energy efficiency per unit volume) but increased the resultant cutting force due to higher chip loads.
  • Depth of Cut: Increasing the depth of cut consistently increased the resultant cutting force, making lower depths more favorable for force reduction.
• Optimum Conditions: The primary TOPSIS analysis (50:50 weighting) identified Run 18 as the optimal combination, yielding a closeness coefficient of 0.8584. The specific parameters were:
  • Spindle Speed: 3700 rpm
  • Feed Rate: 450 mm/min
  • Depth of Cut: 0.8 mm
• Sensitivity Analysis: The selected optimum (Run 18) remained the top-ranked alternative across all tested weighting scenarios (from 50:50 to 10:90), confirming the robustness of this parameter combination as a balanced solution.

Significance and Claims
The paper claims that the integration of Taguchi design with TOPSIS provides an effective framework for balancing energy efficiency and cutting-force reduction in the dry milling of Aluminium 7075. The study demonstrates that a single set of parameters can be identified to manage the inherent conflict between minimizing energy consumption and minimizing mechanical load.

The authors position the work as a practical methodology for multi-response optimization that can be extended to include additional criteria such as tool wear, surface roughness, and vibration. The study explicitly limits its claims to the specific experimental domain (the tested parameter ranges, the specific CNC machine, tooling, and dry conditions) and does not assert that the identified parameters are a universal optimum for all Aluminium 7075 milling operations. Future work is suggested to explore hybrid optimization methods and various cooling strategies, but the current contribution is defined by the successful application of TOPSIS to resolve the specific trade-off between SCE and cutting force in the described setup.