Experimental and numerical investigation of forming defects and stress analysis in laser-welded blanks during deep drawing process

Aminzadeh, Ahmad; Karganroudi, Sasan Sattarpanah; Barka, Noureddine

doi:10.1007/s00170-021-07767-z

Cited by 10 publications

(2 citation statements)

References 40 publications

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“…Since the other method for calculating the variable BHF is the use of test design methods, in this field, several cases are mentioned in general. Recently, several online monitoring methods and intelligent approaches have been proposed to apply real-time determination effect of process parameters on deep drawing and find the optimal process [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-based optimization of Variable Blank Holder Force to reduce residual stress and improve formability; Experimental and statistical analysis

Khaboushani¹,

Parvizi²,

Aminzadeh

et al. 2023

Preprint

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Many contributory factors can influence the quality of the deep drawing process, among which Blank Holder Force (BHF) plays a decisive role. Therefore, controlling the BHF during the deep drawing process can bring many advantages and act as a deterrent against process failures, including tearing, wrinkling, and fatigue due to excessive residual stress and cyclic loads. Variable Blank Holder Force (VBHF), in which the BHF varies along the punch stroke, has recently been a popular method for improving sheet metal quality in the deep drawing process. In this study, VBHF was optimized to improve the formability of the process and reduce the residual stress using two different methods of Artificial Intelligence (AI) and Response Surface Method (RSM). The main purpose of this research is to introduce a new approach based on AI for VBHF optimization and compare the result of which with that of previous methods (Statistical methods). To reach this aim, BHFs in seven different stages of punch stroke were considered as the inputs of the process, and drawn depth at the tearing moment in addition to residual stresses were considered as the outputs of this optimization process. The optimization was carried out in two forms, single and multi-objective optimization to yield the desired results. The deep drawing process was numerically simulated using Abaqus/Explicit Software, with heavy modeling calculations performed on the supercomputer, Simorgh, and the experimental studies were carried out to verify FEM simulation. Additionally, to optimize VBHF using AI methods, Artificial Neural Network (ANN) was used to define a correlation function between inputs and outputs, and a Genetic Algorithm (GA) was used to optimize the function trained by ANN. Optimization results demonstrated that although the trend of optimized VBHF using AI and RSM were considerably similar to each other, the AI results were better than that of RSM in both cases of residual stress reduction and draw ability improvement. The FEM model of deep drawing process was shown to be reliable due to its excellent agreement with experimental studies.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-based optimization of Variable Blank Holder Force to reduce residual stress and improve formability; Experimental and statistical analysis

Khaboushani¹,

Parvizi²,

Aminzadeh

et al. 2023

Preprint

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“…2,3 The process control for the raw material AlSi10Mg has already been implemented by optimizing the parameters laser power, scan speed, and layer thickness, combined with a suitable scan pattern. [3][4][5][6][7] Therefore, the investigation and characterization of the powder material, in particular, appears to be worthwhile, since the powder is an important criterion as the starting product on which the process acts. 1 The decision in favour of AlSi10Mg (powder form; grain size range 15-70 µm) is based on the fact that it is a material that is widely used in the automotive industry (undercarriages, gearbox housings, and engine blocks).…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis of parameters for AlSi10Mg powders from different manufacturers in selective laser melting

Riegraf

Öchsner

Greuling

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part L:

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Customer expectations are increasing, especially with regard to the individualization of products, in which additive manufacturing is described as a disruptive technology. With a view to using the technology in a series application, extensive knowledge of the starting material, the powder, is necessary. In this article, AlSi10Mg in powder form (grain size range: 15–70 µm), which is used for the selective laser melting process, is analysed on the basis of various investigations. A distinction is made from other researchers, as a total of six powders of the same specification but from different manufacturers were used. An investigation of the relative humidity of the powders in the as-delivered condition was carried out using a moisture meter. It could be shown that the moisture lies between 5% and 15% and that the moisture can be reduced by about half by using dry bags. A qualitative examination with a scanning electron microscopy showed that the morphology and size distribution differ. Not only partially round, but also very misshapen particles and particles with and without adhesions are present. The flowability was determined with a calibrated Hall flowmeter. It was found that a lower proportion of fine particles in the powder lead to a more uniform flowability. In the investigation, a distinction was made between powder in the as-received state and dried powder. The particle size distribution was determined with a laser diffraction method and evaluated according to the Mie theory. To be able to carry out porosity analyses on the basis of micrographs, test cubes with the dimensions (10 × 10 × 10 mm3) were used. The parameters scanning speed (400–600 mm/s), hatch spacing (0.19–0.29 mm), and laser power (220–320 W) were varied. The results vary between 0.12% and 5.46% porosity. To combine the results, a pairwise comparison and a use-value analysis were carried out. Overall, the differences in the powders of the different manufacturers were found to be small, so that a recommendation was made for the most economical powder.

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Evaluating the Earing Amount of Materials Under Various Chemical Composition and Heat Treatment Processes with Finite Element Simulations of Cup Drawing Tests

Çaylak¹,

Özçelik²,

Kınacı³

et al. 2023

The Minerals, Metals &Amp; Materials Series

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Experimental and numerical investigation of forming defects and stress analysis in laser-welded blanks during deep drawing process

Cited by 10 publications

References 40 publications

Artificial Intelligence-based optimization of Variable Blank Holder Force to reduce residual stress and improve formability; Experimental and statistical analysis

Artificial Intelligence-based optimization of Variable Blank Holder Force to reduce residual stress and improve formability; Experimental and statistical analysis

Experimental analysis of parameters for AlSi10Mg powders from different manufacturers in selective laser melting

Evaluating the Earing Amount of Materials Under Various Chemical Composition and Heat Treatment Processes with Finite Element Simulations of Cup Drawing Tests

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