LINGUISTIC FUZZY MODELING IN LASER MACHINING QUALITY EVALUATION

• Main Author: Sivarao, Subramonian
• Format: Conference or Workshop Item
• Language: English
• Published: Confederation of the Scientific and Technological Associations in Malaysia 2007
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utem.edu.my/id/eprint/9341/1/linguistic.pdf; http://eprints.utem.edu.my/id/eprint/9341/
• Institution: Universiti Teknikal Malaysia Melaka

Computational method combining the knowledge of skilled operator with appropriate rules will enable the modeling of a powerful Fuzzy~Model which helps far more than what human can do in this modern era of precision manufacturing. The resurgence of interest in computational modeling system over the past few decades has opened many new avenues in its applications. Fuzzy computational modeling leads to greater generality and better rapport with reality. It is driven by the need for methods of analysis and design, which can come to grips with the pervasive imprecision of the real world and exploit the tolerance for imprecision to achieve tractability, robustness and low cost solution. Fuzzy modeling and approximation are the most interesting fields where fuzzy theory can be effectively applied. As far as modeling and approximation is concerned, one can say that the main interest is towards its applications. When one intends to apply fuzzy modeling and approximation to an industrial process, one of the key problems to be solved is to find fuzzy rules. In this research, the inputs are the key variables of the design parameters which generates the singleton output to evaluate the cut edge quality in laser machining. The aim of this scientific research is to design knowledge based linguistic rules, algorithm, architecture & learning ability and further develop fuzzy model for laser machining kerf edge quality prediction. Besides that, the author also investigated the effect of using same rules or different work thicknesses of Mn-Mo pressure vessel plates. The finding shows that, the developed lingual fuzzy model has produced a sound output as it matches closely and agrees well to the experimental result.