Abstract
Abstract
Owing to the significant role that machining parameters play in performing successful and efficient machining operations, the determination of optimum machining parameters is a subject of high importance. The problems are well-known complex puzzles and are treated as a strongly non-deterministic polynomial (NP)-hard problem. In this research, a multi-pass turning problem has been modelled, taking into consideration several technological constraints pertaining to force, power, chip-tool interface temperature, tool life, stable cutting region, etc.; while aiming to satisfy the objective of minimizing the unit production cost. The solution of such a problem, even one of modest size, is marked by excessive computational complexity and therefore random search optimization techniques are needed in order to resolve the problem. In this paper, a heuristic has been developed to determine the number of passes and a new kind of genetic algorithm (GA), incorporating the features of chromosome differentiation and simulated annealing, has been developed and applied to address multi-pass turning problems. The proposed algorithm overcomes the drawbacks of simple GAs and the methodology adopted here has the capability of achieving a better balance between exploration and exploitation, and of escaping from local minima. The proposed algorithm has been tested on various case studies adopted from the literature, as well as on ten simulated data sets. Intensive computational experiments revealed its superiority over earlier approaches.