Optimum selection of grinding parameters using process modelling and knowledge based system approach

RETRACTED: Optimization of surface grinding operations using a differential evolution approach

“…This makes the application of traditional optimization algorithms quite limited. Rowe et al [1] have provided an extensive review on various approaches based on artificial intelligence to the grinding process, which can be classified into knowledge based expert systems [2], fuzzy logic [3], and neural networks [4]. Amitay [5] has optimized both grind- * Tel.…”

Experimental study on grinding of a sintered friction material

“…This is the evidence of the size effect (Malkin, 1989;Li et al, 2002;Liu et al, 2005;Midha et al, 1991).…”

Automatic design of fuzzy logic controller using a genetic algorithm—to predict power requirement and surface finish in grinding

“…Several trials were also made by various investigators to design a suitable expert system for grinding using parameter optimisation. In this connection, work of Midha et al [29], Tonsohoff et al [30], Sakakura and Inasaki [31], Zhu et al [32], Rowe et al [33], Gupta et al [34] are significant. Neural network (NN) was used for modelling creep feed grinding by Sathyanarayanan et al [35], Liao and Chen [36].…”