31,207 research outputs found
An improvement of a cellular manufacturing system design using simulation analysis
Cell Formation (CF) problem involves grouping the parts into part families and machines into manufacturing cells, so that parts with similar processing requirements are manufactured within the same cell. Many researches have suggested methods for CF. Few of these methods; have addressed the possible existence of exceptional elements (EE) in the solution and the effect of correspondent intercellular movement, which cause lack of segregation among the cells. This paper presents a simulation-based methodology, which takes into consideration the stochastic aspect in the cellular manufacturing (CM) system, to create better cell configurations. An initial solution is developed using any of the numerous CF procedures. The objective of the proposed method which provides performances ratings and cost-effective consist in determine how best to deal with the remaining EE. It considers and compares two strategies (1) permitting intercellular transfer and (2) exceptional machine duplication. The process is demonstrated with a numerical exampleCell Formation; Exceptional Elements; Simulation; Alternative costs; Improvement
A new stochastic mixed integer programming to design integrated cellular manufacturing system: A supply chain framework
This research defines a new application of mathematical modeling to design a cellular manufacturing system integrated with group scheduling and layout aspects in an uncertain decision space under a supply chain characteristics. The aim is to present a mixed integer programming (MIP) which optimizes cell formation, scheduling and layout decisions, concurrently where the suppliers are required to operate exceptional products. For this purpose, the time in which parts need to be operated on machines and also products' demand are uncertain and explained by set of scenarios. This model tries to optimize expected holding cost and the costs regarded to the suppliers network in a supply chain in order to outsource exceptional operations. Scheduling decisions in a cellular manufacturing framework is treated as group scheduling problem, which assumes that all parts in a part group are operated in the same cell and no inter-cellular transfer is required. An efficient hybrid method made of genetic algorithm (GA) and simulated annealing (SA) will be proposed to solve such a complex problem under an optimization rule as a sub-ordinate section. This integrative combination algorithm is compared with global solutions and also, a benchmark heuristic algorithm introduced in the literature. Finally, performance of the algorithm will be verified through some test problems
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
Drape optimization in woven composites manufacture.
This paper addresses the optimisation of forming in manufacturing of composites.
A simplified finite element model of draping is developed and implemented. The
model incorporates the non-linear shear response of textiles and wrinkling due
to buckling of tows. The model is validated against experimental results and it
is concluded that it reproduces successfully the most important features of the
process. The simple character of the model results in low computational times
that allow its use within an optimisation procedure. A genetic algorithm is used
to solve the optimisation problem of minimising the wrinkling in the formed
component by selecting a suitable holding force distribution. The effect of
regularisation is investigated and the L-curve is used to select a
regularisation parameter value. Optimised designs resulting from the inversion
procedure have significantly lower wrinkling than uniform holding force
profiles, while regularisation allows force gradients to be kept relatively low
so that suggested process designs are feasible
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