02-05-2014, 03:35 PM
STUDY ON OPTIMIZATION OF MACHINING PARAMETERS IN TURNING PROCESS USING EVOLUTIONARY ALGORITHM WITH EXPERIMENTAL VERIFICATION
STUDY ON OPTIMIZATION OF MACHINING .pdf (Size: 145.12 KB / Downloads: 18)
ABSTRACT
Optimization of cutting parameters is one of the most important elements in any
process planning of metal parts. Economy of machining operation plays a key role in
competitiveness in the market. Turning machines produce finished components from
cylindrical bar.
Finished profile from a cylindrical bar is done in two stages, rough machining and
finish machining. Generally more than one passes are required for rough machining and
single pass is required for finishing. The machining parameters in multipass turning are depth
of cut, cutting speed and feed. The machining performance is measured either by the
minimum production time or minimum cost.
In this paper the optimal machining parameters for continuous profile machining
turning are determined with respect to the minimum production cost, subject to a set of
practical constraints, cutting force, power, dimensional accuracy and surface finish. Due to
complexity of this machining optimization problem, genetic algorithm (GA) and particle
swarm optimization are applied and results are compared.
INTRODUCTION
It has long been recognized that conditions during cutting, such as feed rate, cutting
speed and depth of cut should be selected to optimize the economics of machining operations,
as assessed by productivity, total manufacturing cost per component or some other suitable
criterion. Agapiou [1] formulated machining parameter optimization problem considering
both multi-pass rough machining operations and single-pass finishing. Production cost and
total time were taken as objectives and a weighting factor was assigned to prioritize the two
objectives in the objective function. The author optimized the number of passes, depth of cut,
cutting speed and feed rate in his model, through a multi-stage solution process called
dynamic programming. Several physical constraints were considered and applied in this
model. In his solution methodology, every cutting pass is considering as independent of the
previous pass, hence the optimality for each pass is not reached simultaneously. Ruy
Mesquita [2] used the Hook–Jeeves search method for finding the optimum operating
parameters.
INTRODUCTION
It has long been recognized that conditions during cutting, such as feed rate, cutting
speed and depth of cut should be selected to optimize the economics of machining operations,
as assessed by productivity, total manufacturing cost per component or some other suitable
criterion. Agapiou [1] formulated machining parameter optimization problem considering
both multi-pass rough machining operations and single-pass finishing. Production cost and
total time were taken as objectives and a weighting factor was assigned to prioritize the two
objectives in the objective function. The author optimized the number of passes, depth of cut,
cutting speed and feed rate in his model, through a multi-stage solution process called
dynamic programming. Several physical constraints were considered and applied in this
model. In his solution methodology, every cutting pass is considering as independent of the
previous pass, hence the optimality for each pass is not reached simultaneously. Ruy
Mesquita [2] used the Hook–Jeeves search method for finding the optimum operating
parameters.
Genetic Algorithm Methodology
Genetic algorithms are computerized search and optimization algorithms based on the
mechanics of natural genetics and natural selection. Optimization can be done by the
generation of the population.
Genetic algorithms (GA) are a best population search based technique. GA are
different from traditional optimizations in the following ways.
1. GA goes through solution space starting from a group of points and not from a single point.
2. GA search from a population of points and not a single point.
3. GA use information of a fitness function, not derivatives or other auxiliary knowledge.
4. GA use probabilistic transitions rules, not deterministic rules.
5. It is very likely that the expected GA solution will be a global solution.
Selection and Reproduction
Reproduction selects good strings in a population and forms a mating pool. The
reproduction operator is also called a selection operator. In this work rank order selection is
used. In Table 1 Column 4 shows the output generated using this method. Column 5 is the
corresponding rank of the string. A lower ranked string will have a lower fitness value or a
higher objective function and vice versa.
The higher cumulative probability value in the range is chosen as one of the parents. In
Table 1 for the first string the generated random number is 0.237122. The string number
(rank) 1, which has a cumulative probability of 0.2485000, is selected as the parent, and this
process is repeated for the entire population.
CONCLUSION
All types of CNC machines have been used to produce continuous finished profiles. A
continuous finished profile has many types of operations such as facing, taper turning and
circular turning. To model the machining process, several important operational constraints
have been considered. These constraints were taken to account in order to make the model
more realistic. A model of the process has been formulated with non-traditional algorithms;
GA and PSO have been employed to find the optimal machining parameters for the
continuous profile. PSO produces better results. Using this technique production cost can be
further minimized. The results show that the production cost is minimized by using the
optimized machining parameter.