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

Zhang, Qian, Mahfouf, Mahdi, de Leon, Luis, Boumaiza, Soufiene, Yates, John R., Pinna, Christophe, 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. 1 (1). pp. 231-236. (doi: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) (KAR id:50547)

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.
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)
DOI/Identification number:	10.3182/20091014-3-CL-4011.00042
Subjects:	Q Science > Q Science (General) > Q335 Artificial intelligence T Technology > TA Engineering (General). Civil engineering (General) > TA168 Systems engineering T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User:	Qian Zhang
Date Deposited:	18 Sep 2015 15:59 UTC
Last Modified:	05 Nov 2024 10:36 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/50547 (The current URI for this page, for reference purposes)

University of Kent Author Information

Zhang, Qian.

Creator's ORCID:
CReDIT Contributor Roles:

Depositors only (login required):

Altmetric

Download Statistics

Total unique views for this document in KAR since July 2020. For more details click on the image.