Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Development of four in-process surface recognition systems to predict surface roughness in end milling
Improvement of surface roughness models for face milling operations through dimensionality reduction
Integrated Computer-Aided Engineering
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A study is presented to model surface roughness in end milling using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs). The machining parameters, namely, the spindle speed, feed rate, depth of cut and the workpiece-tool vibration amplitude have been used as inputs to model the workpiece surface roughness. The number and the parameters of membership functions used in ANFIS along with the most suitable inputs are selected using GAs maximising the modelling accuracy. The ANFIS with GAs (GA-ANFIS) are trained with a subset of the experimental data. The trained GA-ANFIS are tested using the set of validation data. The procedure is illustrated using the experimental data of a CNC vertical machining centre in end-milling of 6061 aluminum. Results are compared with other soft computing techniques like genetic programming (GP) and artificial neural network (ANN). The results show the effectiveness of the proposed approach in modelling the surface roughness.