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

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

Abstract

This study presents a harmony search (HS) algorithm to determine the optimum cut-ting parameters for multi-pass face-milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed and feed is obtained to min-imize total production cost while considering technological constraints such as allowable speed, feed, surface finish, tool life andmachine tool capabilities. An illustrative example is used to demonstrate the ability of the HS algorithm and for validation purpose, the genetic algorithm (GA) is used to solve the same problem. Comparison of the results reveals that the HS algorithm converges to optimum solution with higher accuracy in comparison with GA.

Introduction

The cost, time and quality of production are highly depen-dent on the cutting parameters such as the number of passes, depth of cut for each pass, speed, and feed. So, determinationof optimal cutting parameters with regard to technological equirements, capability of machine tool, cutting tool and the partmaterial is a crucial task in the process planning of parts. Several methods have been used for the optimization of cutting parameters. Feasible direction method has been used by Tolouei-Rad and Bidhendi (1997) to determine opti- mum machining parameters for milling operations. Sonmez et al. (1999) used the dynamic programming (DP) optimiza- tion method to determine the optimum cutting parameters for multi-pass milling operations like plain-milling and face- milling. The geometric programming (GP) method has been used for optimization by Sonmez et al. (1999), Jha (1990),

Petropoulos (1973) and Wang (1993). Jha (1990) has concluded that the GP-based program is very slow to produce good results. Genetic algorithm has been used by Shunmugam et al. (2000) to optimize the cutting parameters for multi-pass milling operation like plain-milling and face-milling. Wang et al. (2005) presented an approach to select the optimal machining parameters for multi-pass milling based on GA and simulated annealing (SA) to avoid the premature con-vergence of GA by exploiting the local selection strategy of SA. Baskar et al. (2006) used the memetic algorithm that is the hybrid of genetic algorithm and a hill climbing algorithm to determine the optimum cutting parameters for multi-tool milling operations like face-milling, corner milling, pocket milling and slot milling. More recently, similar hybrid appli-cation was adopted by Oktem et al. (2005), for minimization of surface roughness in milling of mold surfaces. Further-more, Bouzid (2005) employed empirical models to optimize

production rates considering tool life, roughness and cutting force whose coefficients were determined experimentally. In another investigation Manna and Salodkar (2008) employed dynamic programming in optimization of production costs of turning operations.

The main objective of this study is to optimize the total production cost inmulti-pass face-milling operation. The opti-mum number of passes and optimal values of the cutting parameters are found by harmony search algorithm which is a recently developedmeta-heuristic algorithm. An illustrative example is used to demonstrate the capability of the HS algo-rithm. For validation purpose GA is used to solve the same problem and the HS results will be compared with those of GA.

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