Stamping Process Parameter Optimization with Multiple Regression Analysis Approach

Badgujar, Tushar Y.; Wani, Vijay P.

doi:10.1016/j.matpr.2017.12.019

Cited by 16 publications

(3 citation statements)

References 15 publications

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“…The parameter range set in Section 4.1 was converted to input X values into the WOA-BP neural network, and the output Y value was set to be the minimum [23]. The prediction using the WOA-BP neural network yielded a convex die wear of 0.99× 10 −7 mm, a friction coefficient of 0.12, a stamping speed of 22 mm/s, a die hardness of 62 HRC, and a die clearance of 0.88 mm.…”

Section: Optimization Of Process Parameters Based On Woa-bpmentioning

confidence: 99%

Design of Drawing Die for Rear Wheel Cover Outer Panel and Optimization of Wear Process Parameters

Gong

Wang

Yang

2023

Preprint

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Aiming at the problem of the wear caused by the mutual movement between the convex die and the sheet material in the stamping process, which results in a decrease in the die service life. In the paper, the three-dimensional design of the drawing die for the outer plate of the rear wheel cover was carried out by using UG, the simulation of the die stamping process was carried out by using Deform-3D, the main wear positions of the die were determined, the four process parameters of die clearance, friction coefficient, stamping speed and die hardness were selected as the test factors, the amount of die wear was used as the evaluation index to establish an orthogonal test, the multiple linear regression analysis of the test results was carried out by using SPSS software, and the empirical formula for the surface wear of the drawing die was established. Finally, the BP neural network model between process parameters and wear amount was constructed using MATLAB. The weights and thresholds of the nodes in the implicit layer of the model were optimized by using the whale algorithm to obtain the optimal combination of process parameters with minimum wear amount predicted based on the optimized WOA-BP neural network model. The minimum wear amount of the optimized convex die was 1.02×10-6 mm. The optimal combination of process parameters was friction coefficient 0.12, stamping speed 22mm/s, die hardness 62HRC, and die clearance 0.88mm, which completes the design of the automotive rear wheel cover outer plate drawing die and optimizes its surface wear process parameters.

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Section: Optimization Of Process Parameters Based On Woa-bpmentioning

confidence: 99%

Design of Drawing Die for Rear Wheel Cover Outer Panel and Optimization of Wear Process Parameters

Gong

Wang

Yang

2023

Preprint

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“…Sheet metal forming processes have been widely used throughout history to manufacture components with thicknesses ranging from microns to centimeters. Applications where these methods are used include the manufacture of automotive parts, aerospace components or commonly used products such as cans, sinks and boxes, among others [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Study and Optimization of the Punching Process of Steel Using the Johnson–Cook Damage Model

Claver,

Acosta,

Barba

et al. 2024

Metals

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Sheet metal forming processes are widely used in applications such as those in the automotive or aerospace industries. Among them, punching is of great interest due to its high productivity and low operating cost. However, it is necessary to optimize these processes and adjust their parameters, such as clearance, shear force or tool geometry, to obtain the best finishes and minimize crack generation. Thus, the main objective of this research work is to optimize the punching process to achieve parts that do not require subsequent processes, such as deburring, by controlling the properties of the starting materials and with the help of tools such as design of experiments and simulations. In the present study, tensile tests were performed on three steels with different compositions and three sample geometries. The information obtained from these tests has allowed us to determine the parameters of the Johnson–Cook damage criteria. Moreover, punching was performed on real parts and compared with simulations to analyze the percentage of burnish surface. The results obtained show that the methodology used was correct and that it can be extrapolated to other types of die-cutting processes by reducing the percentage of surface fractures and predicting the appearance of cracks. Furthermore, it was observed that clearance has a greater influence than processing speed, while the minimum percentage of the burnish area was observed for the minimum values of clearance.

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“…In this study, we aim to build upon this body of research and present a new identification method for characterizing the elastoplastic damage behavior of S235 steel sheets [13]. Our method combines the use of bulge tests, ANOVA, multiple regression, and finite element simulations to provide a comprehensive characterization of the plastic instability of S235 steel sheets [14,15,16].…”

Section: Introductionmentioning

confidence: 99%

The Characterization of the Plastic Instability of S235 Thin Steel Sheets by Multiple Regression and Analysis of Variance Methods

GHAZOUANI,

NASRI,

ELADEB

et al. 2024

mech

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In this study, we present a new identification method of parameters characterizing the elastoplastic damage behavior of sheet metal based on analysis of variance. The analysis covered experimental and numerical simulation results using the finite element method of the sheet mild steel bulge test. The identification procedure was validated through a confrontation between numerical simulations and experimental results on several hydroforming applications, such as a free expansion and an expansion in matrix cavities. Results show consistency between experimental observations and numerical predictions of instabilities. Furthermore, the location of risk areas of instability and rupture and the damage level could be quantified. The analysis focused on a set of results combining experimental data and numerical simulations of the bulge test of steel sheets. The identified models were validated thanks to a comparison between numerical simulations and experimental results of a set of forming applications by bulge test. The results show coherence between the experimental observations and the numerical predictions of the instabilities.

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Stamping Process Parameter Optimization with Multiple Regression Analysis Approach

Cited by 16 publications

References 15 publications

Design of Drawing Die for Rear Wheel Cover Outer Panel and Optimization of Wear Process Parameters

Design of Drawing Die for Rear Wheel Cover Outer Panel and Optimization of Wear Process Parameters

Study and Optimization of the Punching Process of Steel Using the Johnson–Cook Damage Model

The Characterization of the Plastic Instability of S235 Thin Steel Sheets by Multiple Regression and Analysis of Variance Methods

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