论文信息 - Genetic Algorithm (GA) for Multivariable Surface Grinding Process Optimisation Using a Multi-objective Function Model

Genetic Algorithm (GA) for Multivariable Surface Grinding Process Optimisation Using a Multi-objective Function Model

A genetic algorithm (GA) based optimisation procedure has been developed to optimise the surface grinding process using a multi-objective function model. The following ten process variables are considered in this work: wheel speed, workpiece speed, depth of dressing, lead of dressing, cross-feedrate, wheel diameter, wheel width, grinding ratio, wheel bond percentage, and grain size. The procedure evaluates the production cost and production rate for the optimum grinding conditions, subject to constraints such as thermal damage, wheel-wear parameters, machine-tool stiffness and surface finish. A worked example is used to illustrate how this procedure can be used to produce optimum production rate, low production cost, and fine surface quality for the surface grinding process.

M. Sachithanandam | R. Saravanan | R. Saravanan | M. Sachithanandam

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