Modelling steel heat treatment data using granular data compression and multiple granularity modelling. (22nd November 2010)

• Record Type: Journal Article
• Authors: Panoutsos, George; Mahfouf, Mahdi
• Is Part Of: International journal of granular computing, rough sets and intelligent systems. Volume 1:Number 4(2010)
• Page Start: 382
• Page End: 392
• Publication Date: 2010-11-22
• Subjects: GrC -- granular computing -- granular data compression -- multiple granularity modelling -- granular neural-fuzzy modelling -- mechanical properties -- heat treated steel -- Charpy toughness -- modelling -- steel heat treatment -- neural networks -- fuzzy logic; Intelligent agents (Computer science) -- Periodicals; 006.3
• Languages: English
• ISSNs: 1757-2703

Abstract

In this paper, a systematic modelling approach is presented, involving two algorithmic procedures: a data pre-processing and data compression algorithm using granular computing and statistics; and a granular neural-fuzzy ensemble network consisting of multiple granularity models. Both algorithmic procedures aim to reduce the data and modelling scatter often found in real industrial complex data. The study focuses on the prediction of the mechanical property of heat treated steel, in particular Charpy Toughness. This mechanical property yields high data scatter caused by unknown underlying fractural dynamics. The proposed methodology is shown to successfully model the process under investigation using a real industrial dataset.