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Experimental Study In Cutting Engineering Ceramics Based On A Genetic Algorithm In Optimization

Updated August 27, 2022

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Experimental Study In Cutting Engineering Ceramics Based On A Genetic Algorithm In Optimization essay

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Engineering ceramics, which have interest of grinding of advanced ceramics over the last two decades, are widely used in several areas for example in the aerospace, petrochemical, marine, electrical, automobile and manufacturing industry. The machining technology of engineering ceramics use laser, electro discharge machining (EDM), ultrasound, plasma technology, cutting, grinding and turning in order to improve efficiency and reduce expenses. They are typical because of their high hardness, high strength, and brittleness. Therefore, the optimization of cutting parameters such as cutting speed, feed speed, cutting depth, and tool cutting edge angle should be determined before experiments were carried out. Moreover, the optimal results are that materials removal rate ? is relatively large and the cutting tool wear rate ? sis relatively small.

Multi-objective optimization was made to optimize cutting parameters for prediction models using response surface methodology. In this case genetic algorithms were usually used to optimize cutting parameters. Moreover in the following investigation the reaction surface approach was used to develop into regression model cutting force by manipulating experimental measurements from these cutting forces. The regression model was then combine with genetic algorithm to establish optimum end mill process parameter.

The cutting speed was the dominant factor, followed by the cutting feed rate, and the axial depth of cut. Genetic algorithms was used to get regression equations between material removal rate, surface roughness, and input parameters such as cutting speed, feed rate, and depth of cut, etc. The genetic algorithm-based approach yielded maximum value of material removed rate (MRR). In addition, materials removal rate and cutting tool wear rate were predicted by the least squares method. Finally, the principal objective optimization of cutting parameters was obtained from genetic algorithms, and those parameters will be explained in detail in the following paragraphs.

Cutting tests of engineering ceramics with gage sections 30mm in diameter and 180 mm in axial length-Fluorophlogopite (a type of mineral) were conducted on CK6136 type machine tools. The bending strength of the material is 108 MPa (mega pascal) and thermal conductivity is 2.1 W/m·K (Watts, meters, kelvin). The mechanical performances of cemented carbide cutting tool (YT) are on the next table (The work tool). The cutting procedure includes different cutting parameters for example, cutting speed, feed speed, and cutting depth. Single factor tests were firstly carried out to machine the engineering ceramics material—Fluorophlogopite, as shown below.

Materials removal rate ? Historically it has been shown that input parameters, for example, cutting pace, feed speed, and cutting profundity has impact on the reaction of material removal rate. The info parameters for material removal rate had expanding pattern. It is also well known that material removal rate in cutting operations was defined as the volume of material that was removed per unit time in millimeters cube per minute. For each revolution of workpiece, a ring shaped layer of material was removed.

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Experimental Study In Cutting Engineering Ceramics Based On A Genetic Algorithm In Optimization. (2019, Aug 03). Retrieved from