Abstract
The selection of the optimal external cylindrical grinding conditions importantly contributes to increase of productivity and quality of the products. The external cylindrical grinding is a method of finishing machine elements surface with an indeterminate blade shape. External cylindrical grinding can process surfaces that require high gloss and precision, although it can also be used to remove large surplus stock. Therefore, multi objective optimization for the external cylindrical grinding process is a problem with high complexity. In this study, an experimental study was performed to improve the productivity and quality of grinding process. By using the experimental date, the surface roughness, cutting force, and vibrations were modeled. To achieve the minimum value of surface roughness and maximum value of material removal rate, the optimal values of external cylindrical grinding conditions were determined by using the combination of Genetic Algorithms (GAs) and weighting method. The optimum values of surface roughness and material removal rate are 0.510 μm and 5.906 mm2/s, respectively. The obtained optimal values of cutting parameters were a feed rate of 0.3 mm/rev, a workpiece speed of 188.1 rpm, a cutting depth of 0.015 mm, and a workpiece Rockwell hardness of 54.78 HRC. The optimal values of cutting parameters, and workpiece hardness were successfully verified by comparing of experimental and predicted results. The approach method of this study can be applied in industrial machining to improve the productivity and quality of the products in external cylindrical grinding process of the T1 tool steel
Highlights
Machining process in general and the grinding process in particular, the productivity and surface roughness are the two important goals that the engineers and workers always want to aim
Many studies have been conducted to investigate the influence of cutting parameters on the surface roughness, cutting forces, vibrations, and material removal rate
By analyzing of the experimental data, the surface roughness in the grinding process was modeled as the exponential function (12)
Summary
Machining process in general and the grinding process in particular, the productivity and surface roughness are the two important goals that the engineers and workers always want to aim. The optimal problem of two or more goals is called the multi-objective optimization problem. This is a complex problem containing many boundary conditions and constraints with large search space, so to solve this problem requires an appropriate algorithm. Many studies have been conducted to investigate the influence of cutting parameters on the surface roughness, cutting forces, vibrations, and material removal rate. A novel modelling schemes and optimization methods for surface roughness was proposed based on evolutionary algorithms in the grinding processes [2]. An available model with single criteria material removal rate was applied to obtain the optimum grinding parameters for silicon carbide grinding process using the particle swarm optimization algorithm [3]. A combination method of the target tree method and the genetic algorithm was used to optimize the grinding process [4]
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