Fuzzy-logic-based ranking of steel grades generated by material design KBS

Shivathaya, S.S.; Fang, Xiangling

doi:10.1016/s0952-1976(99)00014-7

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Cited by 4 publications

(2 citation statements)

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“…One possible improved approach is suggested by developments in the area of fuzzy theory [14], which was first developed by Zadeh [15]. In the previous two decades, the theory of fuzzy sets had advanced in a variety disciplines [16,17], including the calculation of quality indices from environmental monitoring data [18][19][20]. Fuzzy methods are appealing because they are suited to modelling the continuum characteristic of the underlying complex environmental interactions that the quality data seek to measure.…”

Section: Introductionmentioning

confidence: 99%

Application of two-stage fuzzy set theory to river quality evaluation in Taiwan

Liou

2003

Water Research

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Section: Introductionmentioning

confidence: 99%

Application of two-stage fuzzy set theory to river quality evaluation in Taiwan

Liou

2003

Water Research

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“…A material design system has been developed, utilising mathematical modelling and knowledge-based approaches by Shivathaya and Fang (1999). The Knowledge-Based System (KBS) generates about 15-30 different target compositions for steelmaking for each customer order.…”

Section: Hybrid Systemsmentioning

confidence: 99%

Intelligent systems in manufacturing: current developments and future prospects

Meziane

Vadera

Kobbacy

et al. 2000

Integrated Manufacturing Systems

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Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

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