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• W4311187310 abstract "Grinding is one of the most complex and accurate machining processes, and the efficiency of the grinding wheel depends significantly on its surface properties. This work aims to propose an algorithmic manner that reduces the cost and time to conduct grinding of an optimized DIN 1.2080 tool steel (SPK) using a soft computing technique to obtain the best combination of input parameters including depth of cut (20, 40, 60 <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <mi>μ</mi> <mi mathvariant=normal>m</mi> </math> ), wheel speed (15, 20, 25 <math xmlns=http://www.w3.org/1998/Math/MathML id=M2> <mi mathvariant=normal>m</mi> <mo>/</mo> <mi mathvariant=normal>s</mi> </math> ), feed rate (100, 300, 500 <math xmlns=http://www.w3.org/1998/Math/MathML id=M3> <mi mathvariant=normal>m</mi> <mi mathvariant=normal>m</mi> <mo>/</mo> <mi mathvariant=normal>s</mi> </math> ), and incidence angle (0, 30, 45 <math xmlns=http://www.w3.org/1998/Math/MathML id=M4> <mi mathvariant=normal>d</mi> <mi mathvariant=normal>e</mi> <mtext> </mtext> <mi mathvariant=normal>g</mi> <mi mathvariant=normal>r</mi> <mi mathvariant=normal>e</mi> <mi mathvariant=normal>e</mi> <mi mathvariant=normal>s</mi> </math> ) with respect to output parameters consisting of average surface roughness and specific grinding energy. According to the input parameters and their levels, an experiment using fractional factorial design of experiment (RFDOE) was designed. Later on, two parallel feed-forward backpropagation (FFBPNN) networks with similar topology made up of 4, 11, and 1 units in their input, hidden, and output layers are trained, respectively. After sensitivity analyses of networks for determination of the relative importance of input variables, a genetic algorithm (GA) adopting linear programming (LP) based on Euclidean distance is coupled to networks to seek out the best combinations of input parameters that result in minimum average surface roughness and minimum specific grinding energy. The findings revealed that RFDOE provides valid data for training FFBP networks with a total goodness value of more than 1.99 in both cases. The sensitivity analyses showed that feed rate (38.97%) and incidence angle (33.94%) contribute the most in the case of average surface roughness and specific grinding energy networks, respectively. Despite the similar surface quality based on scanning electron microscopy (SEM), the optimization resulted in an optimized condition of the depth of cut of 25.23 <math xmlns=http://www.w3.org/1998/Math/MathML id=M5> <mi>μ</mi> <mi mathvariant=normal>m</mi> </math> , wheel speed of 15.02 <math xmlns=http://www.w3.org/1998/Math/MathML id=M6> <mi mathvariant=normal>m</mi> <mi mathvariant=normal>m</mi> <mo>/</mo> <mi mathvariant=normal>s</mi> </math> , feed rate of 369.45 <math xmlns=http://www.w3.org/1998/Math/MathML id=M7> <mi mathvariant=normal>m</mi> <mi mathvariant=normal>m</mi> <mo>/</mo> <mi mathvariant=normal>s</mi> </math> , and incidence angle of 44.98 <math xmlns=http://www.w3.org/1998/Math/MathML id=M8> <mi mathvariant=normal>d</mi> <mi mathvariant=normal>e</mi> <mtext> </mtext> <mi mathvariant=normal>g</mi> <mi mathvariant=normal>r</mi> <mi mathvariant=normal>e</mi> <mi mathvariant=normal>e</mi> <mi mathvariant=normal>s</mi> </math> , which had a lower cost value (0.0146) than the optimum one (0.0953). Thus, this study highlights that RFDOE with a hybrid optimization using FFBP networks-GA/LP can effectively minimize both average surface roughness and specific grinding energy of SPK." @default.
• W4311187310 created "2022-12-24" @default.
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• W4311187310 date "2022-12-12" @default.
• W4311187310 modified "2023-10-18" @default.
• W4311187310 title "Optimization of Grinding Parameters of Tool Steel by the Soft Computing Technique" @default.
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• W4311187310 doi "https://doi.org/10.1155/2022/3042131" @default.
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