Machining is one of the most important and widely used manufacturing processes. Due to complexity and uncertainty of the machining processes, of late, soft computing techniques are being preferred to physics-based models for predicting the performance of the machining processes and optimizing them. Major soft computing tools applied for this purpose are neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and particle swarm optimization. The present paper reviews the application of these tools to four machining processes—turning, milling, drilling, and grinding. The paper highlights the progress made in this area and discusses the issues that need to be addressed.

Application of soft computing techniques in machining performance prediction and optimization: a literature review

https://romisatriawahono.net/lecture/rm/survey/softcomputing/Manjarres%20-%20Harmony%20Search%20Algorithm%20-%202013.pdf

Survey A survey on applications of the harmony search algorithm

Parameter optimization of modern machining processes using teaching-learning-based optimization algorithm

The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a derivative-free real parameter optimization algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper analyzes the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. A simple but very useful modification to the classical HS has been proposed in light of the mathematical analysis undertaken here. A comparison with the most recently published variants of HS and four other state-of-the-art optimization algorithms over 15 unconstrained and five constrained benchmark functions reflects the efficiency of the modified HS in terms of final accuracy, convergence speed, and robustness.

Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization

Highlights? Several evolutionary techniques are reviewed to optimize machining parameter. ? It was found that genetic algorithm was widely applied by researchers. ? The most employed machining operation was multipass-turning. ? The most considered machining performance was surface roughness. In highly competitive manufacturing industries nowadays, the manufactures ultimate goals are to produce high quality product with less cost and time constraints. To achieve these goals, one of the considerations is by optimizing the machining process parameters such as the cutting speed, depth of cut, radial rake angle. Recently, alternative to conventional techniques, evolutionary optimization techniques are the new trend for optimization of the machining process parameters. This paper gives an overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining. Five techniques are considered, namely genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial bee colony (ABC) algorithm. Literature found that GA was widely applied by researchers to optimize the machining process parameters. Multi-pass turning was the largest machining operation that deals with GA optimization. In terms of machining performance, surface roughness was mostly studied with GA, SA, PSO, ACO and ABC evolutionary techniques.

O. Zarei

Papers

Optimization of multi-pass face-milling via harmony search algorithm

Simulation of three-dimension cutting force and tool deflection in the end milling operation based on finite element method