HAIPING xur and HSU-PIN (BEN) WANGtGroup Technology (GT) is one of the key issues in a successful implementation of flexible manufacturing systems(FMSs).The objective ofGT is, through the use of a part-family (PF) formation scheme, to reduce unnecessary variation proliferation. A pari family is a group of parts presenting similar geometry and/or requiring a similar production process. Traditional schemes such as Classification and coding and production flow analysis do not consider uncertainty or impreciseness in PF formation. In order to incorporate the uncertainty which is inherent in the measurement of similarities between parts, fuzzy mathematics is employed in this research. Two differentapproaches of fuzzycluster analysis.fuzzyclassification and fuzzy equivalence, are introduced in the process of part-family formation. In addition, a dynamic part-family assignment procedure is presented using the methodology of fuzzy pattern recognition to assign new parts to existing PFs. A computer program is developed, and several rotational parts from a 10c!!1 company have been tested with satisfactory results. In this paper the theoretical foundation is detailed, along with real world examples.

Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extracted with an auto-regressive (AR) model. Data fusion is then implemented with a Cascade-Correlation (CC) neural network. The results clearly show that multisensor data-fusion-based diagnostics outperforms the single sensor diagnostics with statistical significance.

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Tolerance is one of the most important parameters in design and manufacturing. The allocation of design and machining tolerances has a significant impact on manufacturing cost and product quality. This article presents an analytical model for simultaneously allocating design and machining tolerances based on the least-manufacturing-cost criterion. In this study, tolerance allocation is formulated as a non-linear optimisation model based on the cost-tolerance relationship. A new global optimisation algorithm, simulated annealing, is employed to solve the non-linear programming problem. An example for illustrating the optimisation model and the solution procedure is provided.