Prediction of machining induced residual stresses in aluminium alloys using a hierarchical data-driven fuzzy modelling approach

Zhang, Qian and Mahfouf, Mahdi and de Leon, Luis and Boumaiza, Soufiene and Yates, John R. and Pinna, Christophe and Greene, Richard J. (2009) Prediction of machining induced residual stresses in aluminium alloys using a hierarchical data-driven fuzzy modelling approach. In: Automation in Mining, Mineral and Metal Processing. pp. 231-236. (doi:https://doi.org/10.3182/20091014-3-CL-4011.00042) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://doi.org/10.3182/20091014-3-CL-4011.00042

Abstract

The residual stresses created during shaping and machining play an important role in determining the integrity and durability of metal components. An important aspect of making safety critical components is to determine the machining parameters that create compressive surface stresses, or at least minimise tensile surface stresses. These machining parameters are usually found by trial and error experimentation backed up by limited numerical modelling using Finite Element Methods (FEM) and guided by expert knowledge. The shortcomings of FEM approaches are the length of time needed for the solution of complex models and the inability to learn from data. To solve these problems, a fuzzy modelling approach is presented in this paper and is shown to be successful in modelling machining induced residual stresses.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering, cybernetics and intelligent systems
T Technology > TA Engineering (General). Civil engineering (General) > TA 403 Materials Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Qian Zhang
Date Deposited: 18 Sep 2015 15:59 UTC
Last Modified: 22 Sep 2015 08:14 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50547 (The current URI for this page, for reference purposes)
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