Citation :

Mangesh Patil, Munna Verma, Pundalik Patil, Krupal Pawar, "Multi-objective Optimization in CNC Turning: A Systematic Review," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 8, pp. 400-412, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I8P126

Abstract

A primary machining operation in today's manufacturing is CNC turning; the optimization of process parameters affects the quality of products manufactured, productivity, and ultimately the cost of manufacturing for all manufactured parts. A systematic review that examines the development and effectiveness of various parametric optimization processes employed in CNC turning was conducted to identify the most effective optimization processes. In contrast to prior reviews, this review will provide an evaluation of optimization processes based on convergence characteristics, computational complexity, scalability, and industrial applications. Both single-objective and multiple-objective optimization methodologies are evaluated in the review, including historical mathematical approaches as well as current artificial intelligence-based methodologies. Metrics such as Surface Finish (Ra), Material Removal Rate (MRR), tool wear, cutting forces, and energy consumption are examined with respect to a variety of workpiece-tool material combinations. Adaptive optimization methodologies in CNC turning that address machining condition variability, real-time adjustment of parameters during machining, and sustainability integration in optimization process frameworks are identified in this review as being major areas of future research. The results of the study indicate a trend from single-objective optimization to multiple-objective optimization in the formulation of optimization processes, and that evolutionary algorithms have demonstrated significantly better performance than other methodologies in addressing nonlinear, multi-constraint industrial problems.

Keywords

CNC Turning, Parametric optimization, Machining parameters, Surface roughness, Tool wear, Multi-objective optimization.

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