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

Proceedings of the National Conference on
Trends and Advances in Mechanical Engineering,
YMCA University of Science & Technology, Faridabad, Haryana, Oct 19-20, 2012
Different researchers have carried out process parameters optimization of different types of AMPs from time to
time using different optimization models and solution techniques.
2. Review of traditional optimization techniques
The appropriate meanings of the word “experiment” as given in Webster’s Dictionary are: “a trial or special
observation made to confirm or disapprove something doubtful, especially one under conditions determined by
the experimenter; an act or operation undertaken in order to discover some unknown principle or effective or to
test, establish, or illustrate some suggested or known truth.” The honor of discovering the idea of design of
experiment belongs to Sir Ronald Fisher. Box and Wilson [9] in their paper proposed that an investigator
organizes consecutive small no of trials, in each of which all the factors are simultaneously varied according to
definite rules. The series are so organized that after mathematical processing of preceding ones it will be possible
to further select the conditions for conducting the experiment that is to design the experiment. The design of
experiment is the procedure of selecting the number of trials conditions for running them, essential and sufficient
for solving the problem that has been set with the required precision. The purpose of the theory of design
experiment is to ensure that the experimenter obtains data relevant to his hypothesis in as economical a way as
possible following a sequential way of analysis. The need for selecting and implementing optimal machining
conditions and the most suitable cutting tool has been felt over the last few decades. Optimal machining
conditions are implemented by various traditional design of experimentation technique; most of them are used
previously in metal cutting in CNC turning and milling. Taylor’s early work on establishing optimum cutting
speeds in single pass turnings after that progress has been slow since all the process parameters need to be
optimized. Furthermore, for realistic solutions, the many constraints met in practice, such as low machine tool
power, torque, force limits and component surface roughness must be overcome. Traditionally, the selection of
cutting conditions for metal cutting is left to the machine operator. In such cases, the experience of the operator
plays a major role, but even for a skilled operator it is very difficult to attain the optimum values each time.
Following the pioneering work of Taylor (1907) [38] and his famous tool life equation; different analytical and
experimental approaches for the optimization of machining parameters have been investigated. Gilbert (1950)
[17] studied the optimization of machining parameters in turning with respect to maximum production rate and
minimum production cost as criteria. Armarego & Brown (1969) [5] investigated unconstrained machineparameter
optimization using differential calculus. A number of nomograms were worked out to facilitate the
practical determination of the most economic machining conditions. Brewer (1966) [8] suggested the use of
Lagrangian multipliers for optimization of the constrained problem of unit cost, with cutting power as the main
constraint. Bhattacharya (1970) [10]optimized the unit cost for CNC turning, subject to the constraints of surface
roughness and cutting power by the use of Lagrange’s method. Walvekar & Lambert (1970) [46] discussed the
use of geometric programming to selection of machining variables. Petropoulos (1973) [36] investigated optimal
selection of machining rate variables by geometric programming. Sundaram (1978) [41] applied a goalprogramming
technique in metal cutting for selecting levels of machining parameters in a fine turning operation.
Machining process parameters in advanced machining processes are different for different type of machining.
For process parameters optimization of AMPs, type of objective functions and constraints, number of objectives,
and extent of the importance or priority to be given to each objective depend on: (i) type of the application (i.e.
rough or finish machining), (ii) volume of production (i.e. mass, batch, job-shop), (iii) nature of the work
material (i.e. metallic or non-metallic, brittle or ductile, electrically/thermally conductive or non-conductive,
etc.), and (iv) shape to be produced. Main objective for the bulk material removal processes is to maximize MRR
subjected to constraints on surface roughness produced, power consumption, and tools (or nozzle) wear. The
setting of these parameters determines the quality characteristics of AMPs.
The non-availability of the required technological performance equation represents a major obstacle to
implementation of optimized cutting conditions in practice. This follows since extensive testing is required to
establish empirical performance equations for each tool coating–work material combination for a given
machining operation, which can be quite expensive when a wide spectrum of machining operations is
considered. Further the performance equations have to be updated as new coatings; new work materials and new
cutting tools are introduced. While comprehensive sets of equations are found in some Chinese and Russian
handbooks (Ai et al 1966; Ai & Xiao 1985; Kasilova & Mescheryakov 1985) [2,3], as well in the American
handbook (ASME 1952) and Kroneberg’s (1966) [30], textbook most authors have not included discussions on
the more modern tools, new work materials and tool coatings. Difficulties are experienced in locating the
empirical performance equations for modern tool designs because these are hidden under computerized databases
in proprietary software, as noted in recent investigations [4].It is observed that the conventional methods are not
robust because:
- The convergence to an optimal solution depends on the chosen initial solution.
- Most algorithms tend to become stuck on a suboptimal solution.
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