Numerical study on the effect of mechanical properties variability in sheet metal forming processes

Prates, Pedro A.; Adaixo, A. S.; Oliveira, M. C.; Fernandes, J.V.

doi:10.1007/s00170-018-1604-y

Cited by 19 publications

(16 citation statements)

References 25 publications

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“…Forming and stress measurement: (a) ISF Schematic, (b) a formed geometry with the state of strain, (c) the tool path, and (d) the measurement of residual stress along with the state of residual stress. The design of experiments (DoE) is a statistical approach that has been frequently applied in sheet metal forming to analyze the process parameters effects and for finding optimum process conditions for single or multiple objectives [23,24]. This approach was therefore applied for the present investigations.…”

Section: Methodsmentioning

confidence: 99%

On the Free-Surface Roughness in Incremental Forming of a Sheet Metal: A Study from the Perspective of ISF Strain, Surface Morphology, Post-Forming Properties, and Process Conditions

Al‐Ghamdi

Hussain

2019

Metals

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Due to absence of any supporting die, the free surfaces in incremental sheet forming (ISF) experience uneven deformation. This results in rough surfaces, possibly leading to the reduced service life of components. Therefore, it is necessary to analyze and quantify the effects of the application of strain on the free-surface roughness. Moreover, in order to control roughness, both on the free surface and the opposite contact surface, the nature of correlation between the two types of roughnesses needs to be identified by classifying the significance of different process conditions. The present work is a fundamental study to address these points. A series of specimens are produced by subjecting a metallic sheet to a range of ISF strains (13% to 98%). These specimens are then subjected to a number of characterization tests, namely roughness, uniaxial tension, and residual stress tests. The results reveal that the mean free-surface roughness increases non-linearly as the normal strain (stretching + bending) on the free surface increases (where strain state on the surface is as follows: ɛ1 = 0, ɛ2 > 0, γmax = ɛ2 and 1 and 2 are principal directions). The roughness also increases, although linearly, with the post-forming sheet strength, residual stress, and forming force, thereby showing that strain hardening has a direct influence on the roughness in a way that sheet strengthening is achieved at the cost of surface quality. The surface morphology reveals that the free surfaces contained orange peel, slip lines, and micro-voids, with density increasing with strain application, thus indicating the possible influence of tensile stresses on free surface deformation and roughening at an increasing degree with strain. Further analysis of roughness results discloses that the free-surface roughness and the contact-surface roughness are inversely related, because the responses of the two to ISF processing were mutually exclusive. Based on the obtained results, future research directions are also discussed.

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Section: Methodsmentioning

confidence: 99%

On the Free-Surface Roughness in Incremental Forming of a Sheet Metal: A Study from the Perspective of ISF Strain, Surface Morphology, Post-Forming Properties, and Process Conditions

Al‐Ghamdi

Hussain

2019

Metals

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“…[46]). Therefore, much of the recent work has focused on statistical descriptions of variability within FEM, for assessing the sensitivity of defect predictions to the scatter of the parameters under analysis [19,35,47]. In FEM, the material properties are commonly described using physicsbased constitutive models.…”

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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“…Fei Han et al (2013) [8] developed a coupled FEA and ANN technique, through which the springback responses were simulated and predicted while changing processing parameters. Marretta et al (2010) [9] and Prates et al (2018) [10] performed numerical studies on springback prediction in rail-shaped sheet components, under the variability of material properties, using FEA coupled with RSM metamodeling techniques. Dib et al (2018Dib et al ( , 2019 [11,12] developed a Machine Learning-based approach to predict the occurrence of springback in sheet metal forming processes, under the variability of material properties and process parameters.…”

Section: Introductionmentioning

confidence: 99%

Springback Prediction in Sheet Metal Forming, Based on Finite Element Analysis and Artificial Neural Network Approach

Spathopoulos

Stavroulakis

2020

Applied Mechanics

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Sheet metal forming is one of the most important manufacturing processes applied in many industrial sectors, with the most prevalent being the automotive and aerospace industries. The main purpose of that operation is to produce a desired formed shape blank, without any material failures, which should lie well within the acceptable tolerance limits. Springback is affected by factors such as material properties, sheet thickness, forming tools geometry, contact and friction, etc. The present paper proposes a novel neural network system for the prediction of springback in sheet metal forming processes. It is based on Bayesian regularized backpropagation networks, which have not been tested in the literature, according to the authors’ best knowledge. For the creation of training examples a carefully prepared Finite Element model has been created and validated for a test case used in similar industrial studies.

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Numerical study on the effect of mechanical properties variability in sheet metal forming processes

Cited by 19 publications

References 25 publications

On the Free-Surface Roughness in Incremental Forming of a Sheet Metal: A Study from the Perspective of ISF Strain, Surface Morphology, Post-Forming Properties, and Process Conditions

On the Free-Surface Roughness in Incremental Forming of a Sheet Metal: A Study from the Perspective of ISF Strain, Surface Morphology, Post-Forming Properties, and Process Conditions

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

Springback Prediction in Sheet Metal Forming, Based on Finite Element Analysis and Artificial Neural Network Approach

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