The development of a fuzzy wavelet neural network (FWNN) for the prediction of electricity consumption is presented. The fuzzy rules that contain wavelets are constructed. Based on these rules, the structure of FWNN-based system is described. The FWNN system is applied for modeling and prediction of complex time series. The gradient algorithm and genetic algorithm are used for learning of FWNN parameters. The developed FWNN is applied for prediction of electricity consumption. This process has high-order nonlinearity. The statistical data for the last 10 years are used for the development of FWNN prediction model. The effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based prediction system and with the comparative simulation results of previous related models.

Traditional failure modes and effects analysis (FMEA) methods lack sufficient semantics and structure to provide full traceability between the failure modes and the effects of the complex system. To overcome this limitation, this paper proposes a formal failure knowledge representation model combined with the structural decomposition of the complex system. The model defines the failure modes as the inherent properties of the physical entities at different hierarchical levels, and employs the individual color, unified color, and Boolean matrix of the polychromatic sets to represent the failure modes in terms of their interrelationships and their relations to the physical system. This method is a structure-based modeling technique that provides a simple, yet comprehensive framework to organize the failure modes and their causes and effects more systematically and completely. Using the iterative search process operated on the reasoning matrices, the end effects on the entire system can be achieved automatically, which allows for the consideration of both the single and multiple failures. An example is embedded in the description of the methodology for better understanding. Because of the powerful mathematical modeling capability of the polychromatic sets, the approach presented in this paper makes significant progress in FMEA formalization.

The two critical issues related to product design exploration are addressed: the balance between stylistic consistency and innovation, and the control of design process under a great diversity of requirements. To address these two issues, the view of understanding product design exploration is first sought. In this view, the exploration of designs is not only categorized as a problem-solving activity but also as a problem-finding activity. A computational framework is developed based on this view, and it encompasses the belief that these two activities go hand in hand to accomplish the design tasks in an interactive design environment. The framework adopts an integration approach of two key computational techniques, shape grammars and evolutionary computing, for addressing the above two critical issues. For the issues of stylistic consistency, this paper focuses on the computational techniques in balancing the conflicts of stylistic consistency and innovation with shape grammars. For the issues of controlling design process, the practical concerns of monitoring the design process through various activities starting from the preparation works to the implementation of shape grammars have been emphasized in the development of this framework. To evaluate the effectiveness of the framework, the experiments have been set up to reflect the practical situations with which the designers have to deal. The system generates a number of models from scratch with numerical analysis that can be evaluated effectively by the designers. This reduces the designers' time and allows the designers to concentrate their efforts on performing higher level of design activities such as evaluation of designs and making design decisions.

This research proposes a procedure for solving the multiresponse problem in the Taguchi method utilizing two data envelopment analysis (DEA) approaches, including comparisons of efficiency between different systems (CEBDS) and bilateral comparisons. In this procedure, each experiment in Taguchi's orthogonal array (OA) is treated as a decision-making unit (DMU) with the multiresponses as the inputs and outputs for all DMUs. For each factor of OA, the DMUs are divided into groups, each at the same factor level. Then, DMU's efficiency is separately evaluated by the CEBDS approach and the bilateral comparisons approach for each factor. The level efficiency, or the average of the efficiencies obtained by the CEBDS and the bilateral comparisons approaches for that factor level, is then used to determine the optimal factor levels for multiresponses. Three case studies are provided for illustration; in all, the proposed procedure provides the largest total anticipated improvements. Hence, it should be considered the most effective among all approaches applied in the case studies, including principal component analysis, DEA-based ranking approach, and others. In addition, the proposed procedure is more effective and requires less computational effort when the DMU's efficiency is evaluated by the bilateral comparisons approach instead of the CEBDS approach. In conclusion, the proposed procedure will provide great assistance to practitioners for solving the multiresponse problems in manufacturing applications on the Taguchi method.

Embodiment design (ED) is an early phase of product development. ED problems consist of finding solution principles that satisfy product requirements such as physics behaviors and interactions between components. Constraint satisfaction techniques are useful to solve constraint-based models that are often partial, heterogeneous, and uncertain in ED. This paper proposes new constraint satisfaction techniques to tackle piecewise-defined physics phenomena or skill-based rules and multiple categories of variables arising in design applications. New search heuristics and a global piecewise constraint are introduced in the branch and prune framework. The capabilities of these techniques are illustrated with both academic and real-world problems. Complete models of the latter are presented.

A selection method to support adaptive reuse of parametric finite element analysis (FEA) models is introduced in this paper. Adaptive reuse of engineering artifacts such as FEA models is common in product design, but difficult to automate because of the need to integrate new information. The proposed method factors reusability into selection by evaluating models based on comparative estimates of effort involved in adapting them for reuse to model a query problem. The method is developed for FEA models of component-based designs. FEA modeling of electronic chip packages is used to illustrate the method's usefulness. We conclude with a discussion on the method's advantages and limitations and highlight important issues for further research.