Abstract
Industrial engineering design is a crucial issue in manufacturing. To meet the competitive global market, manufacturers are continuously seeking solutions to design industrial products and systems inventively. Su-Field analysis, which is one of the TRIZ analysis tools for inventive design problems, has been used to effectively improve the performance of industrial systems. However, the inventive standards used for engineering design are summarized and classified according to a large number of patents in different fields. They are built on a highly abstract basis and are independent of specific application fields, making their use require much more technical knowledge than other TRIZ tools. To facilitate the use of invention standards, in particular to capture the uncertainty or imprecision described in the standards, this paper proposes a rule-based heuristic approach. First, Su-Field analysis ontology and fuzzy analysis ontology are constructed to represent precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) reasoning and fuzzy reasoning are executed to generate heuristic conceptual solutions. Finally, we develop a software prototype and elaborate the resolution of “Auguste Piccard’s Stratostat ” in the prototype.
Highlights
Based on the analytics above, to address the aforemention challenges, this paper aims to facilitate the process of Su-Field analysis in industrial engineering design in a more accurate and appropriate way
Engineering design is a crucial issue in the industry
As one of the major contributions of TRIZ, Su-Field analysis can lead users to model the physical structure of a problematic system accurately, identify inventive design problems rapidly and find innovative solution to these identified problems efficiently
Summary
AI technologies have been pervasively used to solve industrial engineering problems. Most of the introduced ontological models and logic rules are based on crisp logic and has weakness in expressing imprecision in Su-Field modeling and inventive standards. Since the idea of fuzzy set and fuzzy logic theory was first proposed by Zadeh [18], various approaches were proposed to try to handle uncertainty within ontologies [19,20] We sort these contributions into two categories: the first type of methods deal with vague information by providing a procedure information within current standard languages and tools, and the second type of methods that extend current semantic web languages and tools to cope with vagueness. F-SWRL extends traditional SWRL rules with the ability to perform fuzzy reasoning. We extend the fuzzy reasoning abilities of traditional ontologies and rules by using fuzzy knowledge base and fuzzy inference techniques
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