...% for h π  = 50, and −39% for h π  = 100. 13 06 2010 26 09 2011 11 01 2012 11 01 2012 costing design engineering genetic algorithms investment manufacturing systems order processing stock control manufacturing system design multiple products inventory control...

...) represent the final blank shape after a training process and genetic algorithms find the optimum initial blank. The finite element method is employed for simulating the multilayer plate deep drawing process to provide training data for ANN. The GONNS results were verified through experiment in which...

... to all feasible configurations. For this reason, a hybrid search method is developed to ensure a global search over the MRRs’ workspace for each feasible configuration. By combining a genetic algorithm method with a Monte Carlo method, this method includes three levels of search, namely, pose, workspace...

... the scrap and press tonnage minimizations. A genetic algorithm is used to optimize the strip working sequence with the objective of minimizing the moment difference between two sides of the die. A moment-optimum strip layout will extend the life expectancy of the die, and the maintenance cost...

... sensitive to changes in compaction pressure, exhibiting their best performances for values not higher than 400 MPa. Neural network solutions were used to model the DSP. Radial basis function (RBF) neural network trained with back propagation algorithm was found to be the fittest model. Genetic algorithm (GA...

...) the fixture system robustness against environmental noises in order to minimize product dimensional variability. The proposed methodology is based on a two-step optimization which involves the integration of genetic algorithm and Hammersley sequence sampling. First, genetic algorithm is used for design space...

..., material handling cost, and work-in-process cost incurred in producing a batch of product variants. Due to the combinatorial nature of the problem, a genetic algorithm (GA) is proposed to provide quick and near-optimal solutions. A case study is conducted using a steering column to illustrate...

... by applying a genetic algorithm in overcoming the lack of explicit normal equations. Then the forces occurring due to flank wear are modeled by extending a 2D worn tool force modeling approach into a three-dimensional analysis to accommodate the effect of low feed rate, small depth of cut, and relatively...

... problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined. The operation factor for a performance criterion...

... and to calibrate the constitutive and damage models. Parametric study is done to probe the effect of process parameters and finally, a genetic algorithm (GA) based design method is used to determine process parameters for minimum burr formation. ( a ) The finite element model setup of the edge shearing problem...

... of the stiffness is addressed using a genetic algorithm. Contributed by the Technical Committee on Vibration and Sound for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING . Manuscript received February 2001; Revised November 2001. Associate Editor: E. DeMeter. 01 February 2001...

...J. S. Chung, Researcher,; S. M. Hwang, Associate Professor, A genetic algorithm based approach is presented for process optimal design in forging. In this approach, the optimal design problem is formulated on the basis of the integrated thermo-mechanical finite element process model so as to cover...

... elements and the clamping forces. Previous methods for fixture design optimization have treated fixture layout and clamping force optimization independently and/or used nonlinear programming methods that yield sub-optimal solutions. This paper deals with application of the genetic algorithm (GA...

... be the use of exact- and/or linear-solution approaches. This paper introduces a different approach by applying a modified genetic algorithm (GA). A “best” solution is generated without searching the complete candidate space, while search is performed on a sequence population basis. The GA is modified to cope...