Optimization of process parameters in explosive cladding of mild steel and aluminum

Abstract

Explosive cladding is best known for its capability to join a wide variety of both similar and dissimilar combinations of metals that cannot be joined by other conventional metal joining techniques. An attempt has been made to optimize the tensile and shear strengths of an explosive clad interface using fuzzy logic and genetic algorithm. The parameters considered for this study include flyer plate thickness, loading ratio, angle of inclination, and stand off distance. The experimental data was trained and simulated using fuzzy logic and the optimization of process parameters was performed using genetic algorithm. The optimized process parameters were validated using experimental results.

Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool

“…This theory has proved to be an effective means for dealing with objectives that are linguistically specified. Linguistic terms, such as 'low,' 'medium' and 'high' may be defined by fuzzy sets [11]. Since its introduction, fuzzy set theory has attracted the attention of researchers in mathematical and engineering fields [12].…”

Explosive Welding of Titanium/Stainless Steel by Controlling Energetic Conditions

“…Although the detonation of explosive generates considerable heat, there is no time for heat transfer to the component metals; thus there is no appreciable change in the microstructure and mechanical properties of the components. [4][5][6][7][8][9][10][11] The result of literature search indicates that very limited attempt has been made to study microstructure of Ti/stainless steel clad produced by explosive welding technique. 12,13) When explosive cladding titanium to steel, it is known that good bonding is achieved when titanium exhibiting low strength and high ductility, TP270 (ASTM Grade1), is clad to steel.…”

Analysis and optimisation of machinability behavior of GFRP composites using fuzzy logic

“…Fuzzy logic technology provides decision support and expert systems with powerful reasoning capabilities bound by a minimum of rules (Zimmermann, 1985;Cox, 1992). A fuzzy logic unit consists of a fuzzifier, membership functions, a fuzzy rule base, an inference engine, and a defuzzifier (Raghukandan et al, 2004;Ross, 1992). The fuzzifier uses membership functions to fuzzify the S/N ratios.…”