بهينه سازي نيروي گيره بندي در سيستم قطعه كار فيكسچر تحت فرآيند ماشينكاري با استفاده از شبكه هاي عصبي و الگوريتم ژنتيك

Optimizing Clamping Force in Work piece-Fixture System under Machining Operations Using Neural Networks and the Genetic Algorithm

An efficient workholding plan must include an optimized clamping scheme. This article uses finite element analysis with a supportive combination of neural network and genetic algorithm to compute an optimized set of clamping forces. A frictional finite element model of workpart-fixture system under cutting and clamping forces is solved. The results are utilized for training and querying an artificial neural network (ANN). The ANN is employed to recognize a pattern between the clamping forces and contact status in the workpiece-fixture system and maximum elastic deformation of the workpiece. Using this pattern as fitness function, a genetic algorithm based program determines the optimum values of clamping forces. These forces while hold the component securely, would not cause its excessive deformation or stress. Therefore they can be prescribed confidently for process planning. The advantage of this work against similar studies is manifestation of the state of contact between fixture elements and workpart. The study facilitates fixture design task in a design for manufacture environment and contributes to computer aided process planning.