OCTOBER 19-20, 2012 - YMCA University of Science & Technology

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Proceedings of the National Conference on
Trends and Advances in Mechanical Engineering,
YMCA University of Science & Technology, Faridabad, Haryana, Oct 19-20, 2012
A REVIEW ON PROCESS PARAMETERS OPTIMIZATION
TECHNIQUES FOR ADVANCED MACHINING PROCESSES
S. Kumar
Department of Mechanical & Automation Engineering, YMCAUST, Faridabad, Haryana, India
email: sanjaykpec@rediffmail.com
Abstract
In this paper an attempt is made to review the literature on Optimization of process parameters of advanced
machining processes. Generally, unconventional or advanced machining processes (AMPs) are used only when
no other traditional machining process can meet the necessary requirements efficiently and economically
because use of most of AMPs incurs relatively higher initial investment, maintenance, operating, and tooling
costs. Therefore, optimum choice of the process parameters is essential for the economic, efficient, and effective
utilization of these processes. Process parameters of AMPs are generally selected either based on the
experience, and expertise of the operator or from the propriety machining handbooks. In most of the cases,
selected parameters are conservative and far from the optimum. This hinders optimum utilization of the process
capabilities. Selecting optimum values of process parameters without optimization requires elaborate
experimentation which is costly, time consuming, and tedious. Process parameters optimization of AMPs is
essential for exploiting their potentials and capabilities to the fullest extent economically.
Various conventional techniques employed for machining optimization include geometric programming,
geometric plus linear programming, goal programming, sequential unconstrained minimization technique,
dynamic programming etc. The latest techniques for optimization include fuzzy logic, scatter search technique,
genetic algorithm, and Taguchi technique and response surface methodology.
Keywords: Advanced machining processes (AMPs), Machining optimization; goal programming; fuzzy logic;
genetic algorithms; Taguchi technique; response surface methodology.
1. Introduction
Advanced engineering materials such as polymers, ceramics, composites, and super alloys play an ever
increasing important role in modern manufacturing industries, especially, in aircraft, automobile, cutting tools,
die and mold making industries Garmo & Kohser [16]. Higher costs associated with the machining of these
materials, and the damage caused during their machining is major impediments in the processing and hence
limited applications. Further, stringent design requirements also pose major challenges to their manufacturing
industries. These include precise machining of complex and complicated shapes and/or sizes (i.e. an aerofoil
section of a turbine blade, complex cavities in dies and molds, etc.), various hole-drilling requirements (i.e. noncircular,
small or micro size holes, holes at shallow entry angles, very deep holes, and burr less curved holes),
machining of low rigidity structures, machining at micro or nano levels with tight tolerances, machining of
inaccessible areas, machining of honeycomb structured materials, fabrication of micro-electro mechanical
systems (MEMS), and nanofinish and surface integrity requirements. Unconventional or advanced machining
processes (AMPs) have been developed since the World War II largely in response to new, challenging, and
unusual machining and or shaping requirements M.K. Groover [28]. Alting [27] classified the AMPs into four
categories according to the type of energy used in material removal: chemical, electro-chemical, mechanical and
thermal. Generally AMPs are characterized by low value of material removal rate (MRR) and high speci fic
energy consumption. AMPs are used only when no other traditional machining process can meet the necessary
requirements efficiently and economically because most of the AMPs are associated with relatively higher initial
investment cost, power consumption and operating cost, tooling fixture and cost, and maintenance cost.
Therefore effective, efficient, and economic utilization of capabilities of AMPs necessitates selection of optimum
process parameters. Generally, values of process parameters of AMPs are selected either based on the
experience, expertise, and knowledge of the operator or from the propriety machining handbooks. Selection of
process parameters based on the operator experience does not completely satisfy the requirements of high
efficiency and good quality. While machining tables can be a better choice in a factory environment for one or
two processes but cannot be used for a wide range of machining processes and their operating conditions. In
most of the cases, selected parameters are conservative and far from the optimum. This hinders optimum
utilization of the process capabilities. Selecting optimum values of process parameters without optimization
requires elaborate experimentation which is costly, time consuming, and tedious. Therefore, to exploit potentials
and capabilities of AMPs to the fullest extent economically, their process parameters optimization is essential.
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