A study on die wear prediction for automobile panels stamping based on dynamic model

Qiao, Xiujuan; Cheng, Aiguo; Nie, Xiaodong; Ning, Minqing

doi:10.1007/s00170-018-1811-6

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…This equation will be implied into the finite element simulation model through DEFORM3D. When W is the wear volume, P is the interface pressure, L is the sliding distance, V is the sliding velocity, H refers to the hardness of the tool material, and dt is the time increment, The variables a, b, c, and K are the wear constants [18][19][20][21].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Tool Wear in Fine Blanking Punch with PVD Coating with TiCN for JIS.SKH51 High Speed Tool Steel Using Archard Wear Model

Phabsimma,

Panich

2023

KEM

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The aim of this study is predict tool wear in the fine-blanking process using a tribometer tester with ASTM G99-17 standard. Then, the result of wear volume and tool life were confirmed with finite element simulation and experiment. The punch material used in fine blanking process was selected to be hight speed steel JIS.SKH51 with surface coating thickness 4 µm of TiCN, the work piece material was used as hot-rolled steel JIS.SPHD P/O, with a thickness of 8 mm. The experimented and simulated results were found that the pressure, and sliding velocity were increase in term of linear curve with slope K = 2.09x10-6 and K = 2.07x10-6 ,respectively, and the hardness of material was increase in term of power at 1.983 and slope of K = 2.14 x10-6. These three factors were effect to the wear volume and tool life, simultaneously, the summation of three factors will average as K = 2.10 x10-6. From comparison of the applied wear equation model with tribology tester the simulated and experimented results were similarly equal with 0.05 by significance at 95% reliability confident. The resulted were precisely confirmed according to the previous research.

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Section: Methodsmentioning

confidence: 99%

Prediction of Tool Wear in Fine Blanking Punch with PVD Coating with TiCN for JIS.SKH51 High Speed Tool Steel Using Archard Wear Model

Phabsimma,

Panich

2023

KEM

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“…LQ is achieved by turning the martensitic phase into fine martensite, which improves the material surface hardness and wear resistance [ 34 ] and reduces the roughness of the wear surface. The wear surface roughness after LQ-LSP3 strengthening is the smallest, mainly because LQ-LSP3 promotes plastic deformation, grain refinement, and improved microstructure morphology and distribution of the material, which leads to the hardness and wear resistance of the material [ 35 ].…”

Section: Analysis and Conclusionmentioning

confidence: 99%

Effect of Laser Quenching-Shock Peening Strengthening on the Microstructure and Mechanical Properties of Cr12MoV Steel

et al. 2022

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The automobile covering parts mold is a key piece of equipment in the automobile industry, and its drawbead is the core element that affects the life of the mold and the quality of the parts made. Due to the complex structure of the mold cavity for covering parts, there exist differences between material flow characteristics, load conditions, stress strain, failure forms and so on in the surface of different parts of its drawbead and the different directions of the same part of the drawbead, thus putting forward new requirements for material strengthening. For the differentiated lose efficacy forms of the dangerous end faces of the tension bars, this study carried out research into the effect of laser quenching–shock peening strengthening (LQ-LSP) on the organization, plastic deformation resistance and wear resistance of Cr12MoV steel. It was shown that the microhardness (722.30 HV) and residual stress (−383.84 MPa) of the specimens were further enhanced after laser quenching–shock peening composite strengthening. The residual austenite content of the specimen was reduced to 0.8%, and the eutectic carbide distribution morphology was improved. After three rounds of laser composite peening, the specimens had the smallest displacement of the nanoindentation load–depth curve, which exhibited the greatest nanohardness (20.0 Pa) and modulus of elasticity (565.25 Pa), while reducing the coefficient of friction (0.61) and surface roughness (0.152 Ra). The smooth and flat surface of the specimen with shallow and narrow plow grooves improved the resistance of Cr12MoV steel to plastic deformation and wear.

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“…According to the actual stamping results, the DP780 high-strength steel plate has good forming, good quality and no defects, and the life of the stamping die is close to the predicted results. Literature [4] Xiaoyong Qiao et al according to the wear characteristics of automobile panel stamping die, a die wear prediction method is established, a new friction and wear device is designed, which can simulate the wear environment of blank die interface in actual stamping. Based on the finite element software, the prediction of die wear is realized by using the wear surface updated constantly by dynamic coefficient and node movement.…”

Section: Introductionmentioning

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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A study on die wear prediction for automobile panels stamping based on dynamic model

Cited by 7 publications

References 17 publications

Prediction of Tool Wear in Fine Blanking Punch with PVD Coating with TiCN for JIS.SKH51 High Speed Tool Steel Using Archard Wear Model

Prediction of Tool Wear in Fine Blanking Punch with PVD Coating with TiCN for JIS.SKH51 High Speed Tool Steel Using Archard Wear Model

Effect of Laser Quenching-Shock Peening Strengthening on the Microstructure and Mechanical Properties of Cr12MoV Steel

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

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