Prediction of Machining Induced Residual Stresses in Aluminium Alloys Using a Hierarchical Data-Driven Fuzzy Modelling Approach

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.

[1]  Mahdi Mahfouf,et al.  A new Reduced Space Searching Algorithm (RSSA) and its application in optimal design of alloy steels , 2007, 2007 IEEE Congress on Evolutionary Computation.

[2]  T. Altan,et al.  Prediction of residual stresses in quenched aluminum blocks and their reduction through cold working processes , 2006 .

[3]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[4]  Mahdi Mahfouf,et al.  Mamdani-Type Fuzzy Modelling via Hierarchical Clustering and Multi-Objective Particle Swarm Optimisation (FM-HCPSO) , 2008 .

[5]  Giuseppina Ambrogio,et al.  A hybrid finite element method–artificial neural network approach for predicting residual stresses and the optimal cutting conditions during hard turning of AISI 52100 bearing steel , 2008 .

[6]  Adnan Sözen,et al.  Determination of residual stresses based on heat treatment conditions and densities on a hybrid (FLN2-4405) powder metallurgy steel using artificial neural network , 2007 .

[7]  Derek A. Linkens,et al.  Rule-base self-generation and simplification for data-driven fuzzy models , 2004, Fuzzy Sets Syst..

[8]  Héctor Pomares,et al.  Self-organized fuzzy system generation from training examples , 2000, IEEE Trans. Fuzzy Syst..

[9]  L. Zadeh A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges , 1972 .

[10]  R. C. McClung,et al.  A literature survey on the stability and significance of residual stresses during fatigue , 2007 .

[11]  Qian Zhang,et al.  Nature-inspired multi-objective optimisation and transparent knowledge discovery via hierarchical fuzzy modelling , 2008 .

[12]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[13]  P. Withers,et al.  Residual stress. Part 1 – Measurement techniques , 2001 .