The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets

Jenab, Arash; Sarraf, Iman Sari; Rahmaan, Taamjeed; Worswick, Michael J.

doi:10.1016/j.matdes.2016.01.038

Cited by 93 publications

(26 citation statements)

References 43 publications

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“…ML-based models have been pursued as an alternative to this type of models (e.g. [21,23]), since the neural network does not require any prior assumption on the mathematical formulation between the input and output variables. The prime value added by ML is the ability to unveil the intrinsic response of a material in case of convoluted experimental data [24].…”

Section: Sheet Metal Formingmentioning

confidence: 99%

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Dib

Oliveira

Marques

et al. 2019

Neural Comput & Applic

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This paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.

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Section: Sheet Metal Formingmentioning

confidence: 99%

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Dib

Oliveira

Marques

et al. 2019

Neural Comput & Applic

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“…A network with 3 hidden layers each containing 10 neurons was selected for this study. Adding input layer, output layer and constant bias nodes, the number of neurons for each layer becomes [4,11,11,11,1], which corresponds to 297 independent weight parameters in total.…”

Section: Deformation Resistancementioning

confidence: 99%

“…The wide variety of existing visco-plasticity models can be classified into physics-based models [3] and phenomenological models. Phenomenological constitutive functions are widely implemented in numerical models, especially in finite element simulations used for metal forming [4]. Many established phenomenological models for dynamic loading conditions are based upon the Johnson-Cook model [5], which multiplicatively decomposes the flow stress into three terms that depend on the strain, strain rate and temperature, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Large Deformation Behavior of High Strength Steel Under Extreme Loading Conditions: High Temperature and High Strain Rate Experiments and Modeling

Roth

Mohr

2018

EPJ Web Conf.

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Plasticity and fracture experiments are carried out on flat smooth and notched tensile specimens extracted from DP800 steel sheets. A split Hopkinson pressure bar testing system equipped with a load inversion device is utilized to reach high strain rates. Temperature dependent experiments ranging from 20°C to 300°C are performed at quasi-static strain rates. The material exposes a monotonic strain hardening behaviour with a non-monotonic temperature dependency. The rate-independent material behaviour at room-temperature is described with a non-associated Hill’48 plasticity model and an Swift-Voce strain hardening. A machine learning based model is used multiplicatively to capture the rate and temperature responses. A good agreement between measured and simulated force-displacement curves as well as local surface is obtained. The loading paths to fracture are then extracted to facilitate further development of a temperature dependent fracture initiation model.

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“…Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are the most popular evolutionary algorithms with several applications in manufacturing, quality control, production, and design [21][22][23][24][25][26][27][28][29]. However, the effectiveness of a recent technique called cuckoo search (CS) for multi-modal design applications [30][31][32][33], and its superiority in benchmark comparisons [34,35] against PSO and GA makes it an intelligent choice for designing multi-element HEAs.…”

mentioning

confidence: 99%

Cuckoo searching optimal composition of multicomponent alloys by molecular simulations

et al. 2017

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A robust computational design framework that couples the metaheuristic cuckoo search technique with classical molecular dynamics simulations is employed to optimize the composition of multicomponent alloys for increased tensile strength.Model binary, ternary, and quinary multi-principal element alloys are chosen as test beds to predict the influence of atomic concentration of one constituent element (design variable) on the ultimate tensile strength (objective function) of the material.The design solutions that correlate the elemental atomic fraction to strength over a number of computational search cycles are in qualitative agreement with earlier experiments. The predictive scheme explores a vast materials landscape and accelerates the elemental selection for discovery of novel multicomponent alloys.

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The Use of genetic algorithm and neural network to predict rate-dependent tensile flow behaviour of AA5182-O sheets

Cited by 93 publications

References 43 publications

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Large Deformation Behavior of High Strength Steel Under Extreme Loading Conditions: High Temperature and High Strain Rate Experiments and Modeling

Cuckoo searching optimal composition of multicomponent alloys by molecular simulations

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