https://doi.org/10.26706/ijaefea.4.9.20221203

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Abstract:-

The optimization of machining process parameters plays a critical role in enhancing productivity, product quality, and cost efficiency in manufacturing engineering. With increasing demand for high precision and sustainable production, numerous optimization techniques have been developed to determine optimal combinations of machining parameters such as cutting speed, feed rate, and depth of cut. This Technical Paper aims to provide a comprehensive analysis of various optimization techniques applied in machining processes, highlighting their effectiveness, advantages, and limitations. The study covers classical methods such as the Taguchi technique and response surface methodology, as well as advanced approaches including genetic algorithms, particle swarm optimization, artificial neural networks, and hybrid optimization models. A systematic review of recent literature has been conducted to examine the application of these techniques in different machining operations such as turning, milling, and drilling. The findings indicate that advanced optimization methods, particularly hybrid and AI-based techniques, offer superior performance in handling complex, multi-objective optimization problems compared to traditional approaches. However, challenges such as computational complexity, requirement of large datasets, and implementation difficulties remain significant concerns. This paper contributes by presenting a structured comparison of optimization techniques and identifying research gaps for future exploration. The outcomes of this review provide valuable insights for researchers and practitioners to select appropriate optimization strategies for improving machining performance and achieving efficient manufacturing processes.

Index Terms:-

Machining Optimization, Taguchi Method, Genetic Algorithm, Response Surface Methodology, Artificial Intelligence, Manufacturing Engineering .

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