As is typical of stochastic-optimization problems, the multivariate integration of the probability-density function is the most difficult task in the optimal allotment of tolerances. In this paper, a truncated Monte Carlo simulation and a genetic algorithm are used as analysis (i.e. multivariate-integration) and synthesis (i.e. optimization) tools, respectively. The new method has performed robustly in limited experiments, and was able to provide a significant reduction in optimal cost when compared with results published in a previous study.

Simultaneous engineering processes involve multifunctional teams; team members simultaneously make decisions about many parts of the product-production system and aspects of the product life cycle. This paper argues that such simultaneous distributed decisions should be based on communications about sets of possibilities rather than single solutions. By extending Taguchi's parameter design concepts, we develop a robust and distributed decision-making procedure based on such communications. The procedure shows how a member of'a design team can make appropriate decisions based on incomplete information from the other members of the team. More specifically, it (1) treats variations among the designs considered by other members of the design team as conceptual noise;(2) shows how to incorporate such noises into decisions that are robust against these variations; (3) describes a method .for using the same data to provide preference information back to the other team members; and (4) provides a procedure for determining whether to release the conceptually robust design or to wait for further decisions by others. The method is demonstrated by part of a distributed design process for a rotary CNC milling machine. While Taguchi's approach is used as a starting point because it is widely known, these results can be generalized to use other robust decision techniques.

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Fixtures are used to position and hold parts for a series of assembly operations. In automotive body assembly, these fixtures conventionally have been dedicated, therefore they must be replaced whenever there are model changes in an auto body assembly plant. In recent years, however, the automotive industry has been changing from high volume to small-to-medium volume production per model with an increasing number of models because customer tastes are diversifying. To cope with this change, auto companies need to be capable of producing a variety of models in small-to-medium volume, and they rely on flexible assembly lines and flexible fixtures. These flexible fixtures use robots as programmable fixture elements so that they can be reprogrammed for different stamped sheet metal parts. When designing flexible fixtures, fixture designers need to be concerned with fixture workspaces for a set of different stampings. However, existing fixture design methods address the fixturing of one stamping only. This paper presents a system that fixture designers can use to synthesize flexible fixture workspaces for a set of different stampings. Based on circular workspaces for flexible fixture robots, this system finds optimal workspace sizes and centers on a fixture base plate with a graphical display for visual checking. This system is simple to use and produces results quickly.

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