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

Zhang, Bin; Mahfouf, Mahdi; León, Luis de; Boumaiza, Soufiene; Yates, John R.; Pinna, C.; Greene, Richard

doi:10.3182/20091014-3-cl-4011.00042

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…They are capable of learning from data without needing much prior knowledge about the materials and processing [11], [12]. Fuzzy models are also convenient when combined with optimisation techniques to identify the input parameters that will provide a desirable behaviour profile [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach

Zhang

Mahfouf

Panoutsos

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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Friction Stir Welding (FSW) is a relatively new solidstate joining technique, which is versatile, environment friendly, and energy and time efficient. For a comprehensive understanding of the effects of process conditions, such as tool rotation speed and traverse speed, on characterisations of welded materials, it is essential to establish prediction models for different aspects of the materials' behaviours. Because of the high complexity of the FSW process, it is often difficult to derive accurate and yet transparent enough mathematical models. In such a situation, a systematic data-driven fuzzy modelling approach is developed and implemented in this paper to model FSW behaviour relating to AA5083 aluminium alloy, consisting of microstructural features, mechanical properties, as well as overall weld quality. This methodology allows constructing transparent fuzzy models considering both accuracy and interpretability attributes of fuzzy systems. The elicited models proved to be accurate, interpretable and robust and can be further applied to facilitate the optimal design of process parameters, with the aim of finding the optimal combinations of process parameters to achieve desired welding properties.

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Section: Introductionmentioning

confidence: 99%

Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach

Zhang

Mahfouf

Panoutsos

et al. 2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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“…The limitations of RS represented by tensile residual stresses: Tensile residual stresses turn for increasing service stresses and lead to premature failure of a component and tensile RS leads to distortion, corrosion and cracking. To minimize the damage of residual stresses through: [4] From mechanical ways and heat treatment by reducing heat. The compressive residual stress is very important because it reduces fatigue life, corrosion and breakage of hydrogen compounds where it can be intentionally introduced by techniques such as shot peening and burnishing.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation into Residual Stress During Turning Operation for Stainless Steel AISI 316

Lafta¹,

Tawfiq²

2020

ETJ

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RS (residual stresses) represent the main role in the performance of structures and machined parts. The main objective of this paper is to investigate the effect of feed rate with constant cutting speed and depth of cut on residual stresses in orthogonal cutting, using Tungsten carbide cutting tools when machining AISI 316 in turning operation. AISI 316 stainless steel was selected in experiments since it is used in many important industries such as chemical, petrochemical industries, power generation, electrical engineering, food and beverage industry. Four feed rates were selected (0.228, 0.16, 0.08 and 0.065) mm/rev when cutting speed is constant 71 mm/min and depth of cutting 2 mm. The experimental results of residual stresses were (-15.75, 12.84, 64.9, 37.74) MPa and the numerical results of residual stresses were (-15, 12, 59, and 37) MPa. The best value of residual stresses is (-15.75 and -15) MPa when it is in a compressive way. The results showed that the percentage error between numerical by using (ABAQUS/ CAE ver. 2017) and experimental work measured by X-ray diffraction is range (2-15) %.

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Fuzzy modelling using a new compact fuzzy system: A special application to the prediction of the mechanical properties of alloy steels

Zhang

Mahfouf

2011

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011)

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

Cited by 5 publications

References 12 publications

Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach

Multiple characterisation modelling of friction stir welding using a genetic multi-objective data-driven fuzzy modelling approach

Experimental and Numerical Investigation into Residual Stress During Turning Operation for Stainless Steel AISI 316

Fuzzy modelling using a new compact fuzzy system: A special application to the prediction of the mechanical properties of alloy steels

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