Abstract. This paper presents a parallel genetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into subpopulations, and in each subpopulation the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighboring ones after certain number of epochs. An implementation of the algorithm is discussed and the performance evaluation is made against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional simulated annealing, parallel genetic algorithm.
Titanium machining poses a great challenge to cutting tools, and yields serious negative influence en tool life, Thus, cooling supply strategies are widely used in its machining process. On account of this, the tool wear perfOrmance from dry cutting, flood cooling and minimum quantity lubrication (MQL) techniques is investigated. This study focuses on cutting tool life under the varied cutting conditions and cooling supply strategies so as to establish effective titanium cutting, Experimental results proved that compared with dry cutting and floocl cooling, MQL aided machining can remarkably and reliably improve tool life in titanium machining.
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