Lian-Yin Zhai

Feature extraction using rough set theory and genetic algorithms--an application for the simplification of product quality evaluation

Feature extraction is an important aspect in data mining and knowledge discovery. In this paper an integrated feature extraction approach, which is based on rough set theory and genetic algorithms (GAs), is proposed. Based on this approach, a prototype feature extraction system has been established and illustrated in an application for the simplification of product quality evaluation. The prototype system successfully integrates the capability of rough set theory in handling uncertainty with a robust search engine, which is based on a GA. The results show that it can remarkably reduce the cost and time consumed on product quality evaluation without compromising the overall specifications of the acceptance tests.

Design concept evaluation in product development using rough sets and grey relation analysis

Design concept evaluation plays a critical role in the early phases of product development as it has significant impact on the downstream development processes as well as on the success of the product developed. Essentially, design concept evaluation is a complex multi-criteria decision-making process involving large amount of data and expert knowledge which are usually imprecise and subjective. Aiming to improve the effectiveness and objectivity of the design concept evaluation process, this paper proposes a novel method based on grey relation analysis and rough set theory. By integrating the strength of rough sets in handling vagueness and the merit of grey relation analysis in modeling multi-criteria decision-making, a rough number enabled grey relation analysis (called rough-grey analysis) is proposed to evaluate design concepts. The result of an example shows that the proposed rough-grey analysis has provided a novel alternative to perform design concept evaluation, in which the vague design information and expert knowledge can be modeled and analyzed more effectively and objectively.

A dominance-based rough set approach to Kansei Engineering in product development

Keen competitions in the global market have led product development to a more knowledge-intensive activity than ever, which requires not only tremendous expert knowledge but also effective analysis of design information. Kansei Engineering as a customer-oriented methodology for product development, often has to analyse imprecise design information inherent with nonlinearity and uncertainty. This paper proposes a systematic approach to Kansei Engineering based on the dominance-based rough set theory. Two novel concepts known as category score and partition quality have been developed and incorporated into the proposed approach. The new approach proposed is able to identify and analyse two types of inconsistencies caused by indiscernibility relations and dominance principles respectively. The result of an illustrative case study shows that the proposed approach can effectively extract Kansei knowledge from imprecise design information, and it can be easily integrated into an expert system for customer-oriented product development.

An evolutionary approach for cooling system optimization in plastic injection moulding

The cooling process is of great importance in plastic injection moulding as it has a direct impact on both productivity and product quality. Cooling process optimization is a sophisticated task which includes not only the design of cooling channels but also the selection of process parameters. Most existing optimization systems focus on either cooling channel design or process parameter selection but not both. This paper explores an approach to optimize both cooling channel design and process condition selection simultaneously through an evolutionary algorithm. The prototype system proposed in this paper is an integration of the genetic algorithm and CAE (Computer-Aided Engineering) technology. The aim is to launch a computerized system that can guide the optimization of the cooling process in plastic injection moulding. The objective is to achieve the most uniform cavity surface temperature to assure product quality.

Towards a QFD-based expert system: A novel extension to fuzzy QFD methodology using rough set theory

Quality function deployment (QFD) has been widely recognized as an effective means to develop quality products that can maximize customer satisfactions. This paper presents a novel extension to the fuzzy QFD methodology using rough set theory, with the aim to facilitate decision making in the early stages of product development and lead to the establishment of a QFD-based expert system for product design. The proposed rough-fuzzy QFD system combines fuzzy arithmetic operations with the two novel concepts of rough number and rough boundary interval that are derived from rough set theory. A comparison between the proposed methodology and the traditional fuzzy QFD was performed. It has been shown that the proposed methodology not only can provide more insights into the vague voices of customers and technologists, but also can suppress the enlargement of boundary intervals after each arithmetic operation in QFD analysis. This would help in improving the discernibility of design objectives and thus facilitate the decision making in product development.

Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective

The ability to acquire knowledge from empirical data or the environment is an important requirement in better understanding many natural and artificial organisms. This ability relies heavily on the quality of the raw information available about the target system. In reality, these raw information/data may contain uncertainty and fuzziness, that is, it may be imprecise or incomplete. A number of techniques, such as the Dempster-Shafer theory of belief functions and fuzzy set theory, have been developed to handle knowledge acquisition in environments that exhibit uncertainty and fuzziness. However, the advent of the rough set theory in the early 80’s provides a novel and promising way of dealing with vagueness and uncertainty. This chapter will address the issue systematically by covering a broad area including knowledge acquisition / extraction, uncertainty in general, and techniques for handling uncertainty. The basic notions of rough set theory as well as some recent applications are also included. Two simple case studies related to fault diagnosis in manufacturing systems a reused to illustrate the concepts presented in this chapter.