Analysis of assembly properties of a product is needed during the initial design stage in order to identify potential assembly problems, which affect product performance in the later stages of life cycle. Assemblability analysis and evaluation play a key role in assembly design, assembly operation analysis and assembly planning. This paper develops a novel approach to assemblability and assembly sequence analysis and evaluation using the concept of the fuzzy set theory and neuro-fuzzy integration. Assemblability is described by assembly-operation difficulty, which can be represented by a fuzzy number between 0 and 1. Assemblability evaluation is therefore fuzzy evaluation of assembly difficulty. The evaluation structure covers not only the assembly parts' geometric and physical characteristics, but also takes into account the assembly operation data necessary to assemble the parts. The weight of each assemblability factor is subject to change to match the real assembly environments based on expert advice. The approach has the flexibility to be used in various assembly methods and different environments. It can be used in a knowledge-based design for assembly expert system with learning ability. Through integration with the CAD system, the developed system can effectively incorporate the concurrent engineering knowledge into the preliminary design process so as to provide users with suggestions for improving a design and also helping to obtain better design ideas. The applications in assembly design and planning show that the proposed approach and system are feasible.

This article addresses computational synthesis systems that attempt to find a structural description that matches a set of initial functional requirements and design constraints with a finite sequence of production rules. It has been previously shown by the author that it is computationally difficult to identify a sequence of production rules that can lead to a satisficing design solution. As a result, computational synthesis, particularly with large volumes of selection information, requires effective design search procedures. Many computational synthesis systems utilize transformational search strategies. However, such search strategies are inefficient due to the combinatorial nature of the problem. In this article, the problem is approached using a completely different paradigm. The new approach encodes a design search problem as a Boolean (propositional) satisfiability problem, such that from every satisfying Boolean-valued truth assignment to the corresponding Boolean expression we efficiently can derive a solution to the original synthesis problem (along with its finite sequence of production rules). A major advantage of the proposed approach is the possibility of utilizing recently developed powerful randomized search algorithms for solving Boolean satisfiability problems, which considerably outperform the most widely used satisfiability algorithms. The new design-as-satisfiability technique provides a flexible framework for stating a variety of design constraints, and also represents properly the theory behind modern constraint-based design systems.

When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and glyphs. Interactive functions used for displaying rule attributes and exploring temporal rules are implemented by utilizing Virtual Reality Modeling Language v2 mechanisms. Furthermore, to give a direction of rule potential for the user, the rule statistical interestingness is evaluated on the basis of combining weighted characteristics of rule and rule matrix. A constraint-based association rule mining tool which creates the virtual model as an output is presented, including the most relevant experiences from the development of the tool. The applicability of the overall approach has been verified by using the developed tool for data mining on a hot strip mill of a steel plant.

Product configuration is the process of generating a product variant from a previously defined product family model and additional product specifications for this variant. The process of finding and sequencing the relevant operations for manufacturing this product is called process planning. This article combines the two principles in a new concept of process configuration that solves the process planning task using product configuration methods. The second section develops characteristics for two process configuration concepts, the interactive process configuration and the automation-based process configuration. Following an overview of the implementation of a process configuration system, the results of a case study in the aluminum rolling industry are presented. The main benefits of the process configuration concept are observed in a reduced knowledge-maintenance effort and in increased problem-solving speed.

This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.

Creative Stimulator (CreaStim) is an intelligent interface for pattern design that behaves as a semiactive partner to human designers rather than as a passive graphical or computational tool. By making adjustments to psychological differentials and/or design parameters, CreaStim is able to help designers to explore innovative pattern designs and to get inspiration, producing different types of novel designs. In this article, the mechanism, the technique, the implementation, and the testing of CreaStim are described. The basic principle of CreaStim is the catastrophe theory, which implies that sudden realization in the thinking process of design may lead to creativity. CreaStim tries to stimulate and/or impact designers' creativity in design process using the output of it, rather than to simulate the sudden realization. The core of the CreaStim is a neural network-based imagining engine, a data repository, and its learning strategies considering psychological factors. The psychological factors, which are thought one of the key influences to creative design, are based on the questionnaires completed by designers about the existing successful designs. The repository contains not only a traditional database storing functional attributes, economic attributes, graphic description, structural description, and psychological attributes, but also methods, rule-based knowledge, and pattern-type knowledge. And it is managed by an application program called Design Template Group (DTG) manager. Trained with 12 pieces of successful pattern designs and 528 pieces of pseudo-examples produced and evaluated by the authors, CreaStim is implemented for a PC and an evaluation poll from five designers shows that designers may most likely get some inspiration from the produced patterns and some of them can even be adopted as the design alternatives directly.