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Abstract
A study is presented to model surface roughness in end milling process. Three types of intelligent networks have been considered. They are (i) radial basis function neural networks (RBFNs), (ii) adaptive neurofuzzy inference systems (ANFISs), and (iii) genetically evolved fuzzy inference systems (G‐FISs). The machining parameters, namely, the spindle speed, feed rate, and depth of cut have been used as inputs to model the workpiece surface roughness. The goal is to get the best prediction accuracy. The procedure is illustrated using experimental data of end milling 6061 aluminum alloy. The three networks have been trained using experimental training data. After training, they have been examined using another set of data, that is, validation data. Results are compared with previously published results. It is concluded that ANFIS networks may suffer the local minima problem, and genetic tuning of fuzzy networks cannot insure perfect optimality unless suitable parameter setting (population size, number of generations etc.) and tuning range for the FIS, parameters are used which can be hardly satisfied. It is shown that the RBFN model has the best performance (prediction accuracy) in this particular case.
Highlights
End milling is one of the most common metal removal operation-encountered in industrial processes
The critical parameters of the genetic algorithm (GA) are the size of the population, crossover rate, mutation rate, number of iterations, that is the number of generations, and so forth
Findings show that the radial basis function neural networks (RBFNs) has achieved the least training error (RMSE = 0.0) and least testing error (RMSE = 0.0295)
Summary
End milling is one of the most common metal removal operation-encountered in industrial processes. It is widely used in a variety of manufacturing industries including the aerospace and automotive sectors, where quality is an important factor in the production of slots, pockets, precision molds, and dies. The quality of the surface plays a very important role in the performance of milling as a good-quality milled surface significantly improves fatigue strength, corrosion resistance, and creep life. Surface roughness affects several functional attributes of parts, such as contact causing surface friction, wearing, light reflection, heat transmission, ability of distributing and holding of lubricant, coating, and resisting fatigue. The mechanism behind the formation of surface finish is very complicated and process dependent, it is very difficult to calculate its value through analytical formula
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