Modelling of laser processing cut quality by an adaptive network-based fuzzy inference system

Sivarao, Subramonian (2009) Modelling of laser processing cut quality by an adaptive network-based fuzzy inference system. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C - JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 223 (10). pp. 2369-2381. ISSN 0959-6518

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Abstract

Real-world problems in precision machining now require intelligent systems that integrate knowledge, techniques, and methodologies. Intelligent systems possess human-like expertise within a specific domain to adapt themselves and to learn to do better in making decisions for an intelligent manufacturing system. An intelligent tool called adaptive network-based fuzzy inference system (ANFIS) was used to model and predict the laser cut quality of a 2.5mm manganese–molybdenum (Mn–Mo) alloy pressure vessel plate in this article. A 3 kW CO2 laser machine with seven selected design parameters was used to carry out 128 experiments based on 2k factorial design with single replication. Because surface roughness (Ra) was the response parameter, it was targeted to be <15μmto meet the requirement and benchmark of the pressure vessel manufacturer who sponsored this project. The DIN 2310-5 German laser cutting of metallic materials standard and procedure was referred to for evaluating surface roughness, where experimentally obtained results were used for Ra predictive modelling. Predictions of non-linear laser processing by ANFISwere found to be extremely promising in supplying the desired output, where Ra was predicted to an excellent degree of accuracy, reaching almost 70 per cent with the experimental pure error below 30 per cent.

Item Type:	Article
Subjects:	T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Manufacturing Engineering > Department of Manufacturing Process
Depositing User:	Assoc. Pror. Ir. Dr. Sivarao Subramonian
Date Deposited:	13 Aug 2013 15:27
Last Modified:	28 May 2015 04:02
URI:	http://eprints.utem.edu.my/id/eprint/9177
Statistic Details:	View Download Statistic

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