Uncertainty analysis of deep drawing using surrogate model based probabilistic method

Huang, Changwu; Radi, Bouchaïb; Hami, Abdelkhalak El

doi:10.1007/s00170-016-8436-4

Cited by 45 publications

(11 citation statements)

References 25 publications

(22 reference statements)

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“…Senn and Link [28,29] as well as Breitsprecher et al [41] used neural networks for surrogate modelling in deep drawing whereas Morand et al [42] and Huang et al [43] used kriging models for this task. Our research [44] related to modelling of draw-in as well as drawing aid force signals showed highest modelling accuracy by using linear regression approaches which is in accordance with the results in [23, see Table 4], especially compared to neural networks for regression.…”

Section: Modelling Of Sensor Signalsmentioning

confidence: 99%

Surrogate model–based inverse parameter estimation in deep drawing using automatic knowledge acquisition

Ryser

Neuhauser

Hein

et al. 2021

Int J Adv Manuf Technol

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In this paper, we propose a new approach for the simulation-based support of tryout operations in deep drawing which can be schematically classified as automatic knowledge acquisition. The central idea is to identify information maximising sensor positions for draw-in as well as local blank holder force sensors by solving the column subset selection problem with respect to the sensor sensitivities. Inverse surrogate models are then trained using the selected sensor signals as predictors and the material and process parameters as targets. The final models are able to observe the drawing process by estimating current material and process parameters, which can then be compared to the target values to identify process corrections. The methodology is examined on an Audi A8L side panel frame using a set of 635 simulations, where 20 out of 21 material and process parameters can be estimated with an R2 value greater than 0.9. The result shows that the observational models are not only capable of estimating all but one process parameters with high accuracy, but also allow the determination of material parameters at the same time. Since no assumptions are made about the type of process, sensors, material or process parameters, the methodology proposed can also be applied to other manufacturing processes and use cases.

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Section: Modelling Of Sensor Signalsmentioning

confidence: 99%

Surrogate model–based inverse parameter estimation in deep drawing using automatic knowledge acquisition

Ryser

Neuhauser

Hein

et al. 2021

Int J Adv Manuf Technol

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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%

“…Two input features related to process parameters were also considered: the friction coefficient and the blank holder force (BHF). The mean value of the friction coefficient is 0.144 for all materials, with r/l ¼ 20% [19]. For the BHF, two mean values were considered, which correspond to a lower and an upper level of the process window.…”

Section: Simulated Data Setsmentioning

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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“…For these reasons, the stochastic modelling and uncertainties quantification of sheet metal forming processes are of current industrial interest. In recent years, several researchers have modelled the influence of the uncertainty sources on the final product variability, by resorting to Monte Carlo method [3,4], design of experiences techniques [5,6] and metamodels [7,8].…”

Section: Intr Introduction Oductionmentioning

confidence: 99%

Numerical study of the square cup stamping process: a stochastic analysis

Pereira¹,

Ruivo²,

Oliveira³

et al. 2021

Esaform 2021

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The industrial demand for products with better quality and lower production costs have encouraged the widespread application of the finite element analysis (FEA) in the development and optimization of sheet metal forming processes. To ensure that the FEA solutions are reliable and robust it is important to take into account the uncertainties that inevitably arise in a real industrial environment. In this context, a numerical study on the influence of the material and process uncertainty in the stamping results of a square cup is presented. In this analysis, it is assumed uncertainty in the elasticity properties, hardening law parameters, anisotropy coefficients, blank thickness, friction coefficient and in the blank holder force. The effect of the uncertainty in these input parameters is evaluated in the punch force, equivalent plastic strain, thickness and cup geometry. Firstly, quasi-Monte Carlo method was used to evaluate the variability in the simulation outputs, considering the uncertainty of the input parameters. This analysis shows that the geometry is the output most sensitive to the uncertainty of the input parameters. Afterwards, a variance-based sensitivity analysis was carried out to identify the input parameters that most influence the output variability. It was concluded that the hardening law parameters and the anisotropy coefficients have the most influence in the stamping results variability of a square cup.

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Uncertainty analysis of deep drawing using surrogate model based probabilistic method

Cited by 45 publications

References 25 publications

Surrogate model–based inverse parameter estimation in deep drawing using automatic knowledge acquisition

Surrogate model–based inverse parameter estimation in deep drawing using automatic knowledge acquisition

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

Numerical study of the square cup stamping process: a stochastic analysis

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