One of the most important factors in hard turning is tool wear, since tool condition affects the quality of the product, tool life, and, consequently, the efficiency of the machining process. Modern methods of cooling and lubricating such is high pressure cooling provides possibility to reduce intensive wear of cutting tool due to better penetration of the fluid into the chip-tool and workpiece-tool interfaces. This paper investigates the potential of fuzzy expert system, where the fuzzy system is optimized using two bio-inspired algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO), for tool wear prediction in hard turning. Experiments have been conducted on a 100Cr6 (AISI 52100) steel workpieces with 62 HRC hardness using inexpensive coated carbide tools under high pressure cooling conditions. The estimated values of tool wear obtained from developed GA and PSO based fuzzy expert systems were compared with the experimental data and very good agreement was observed.
Fuzzy logic Bio-inspired algorithms Tool wear
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