A new experimental test apparatus for angle binder draw bead simulations

Smith, L. M.; Zhou, Yadong; Zhou, Dajun; Du, Changqing; Wanintrudal, C.

doi:10.1016/j.jmatprotec.2009.01.017

Cited by 17 publications

(10 citation statements)

References 4 publications

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“…It was found that the main factor determining the value of the restraining force is the height of the drawbead. Smith et al [17] developed a device for determining the pulling force of the sheet metal in a stamping die with a drawbead. When the sheet metal is pulled through the height-adjustable drawbead, the pulling force, the restraining force, and the displacement of the sheet are measured.…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that it is difficult to achieve a straight wall on both the 'length' and 'width' sides without the use of a drawbead. Smith et al [17] developed a device for obtaining the pulling forces for drawbead tooling on inclined binder surfaces in the Oakland University Angle Draw Bead Simulation (OU-ADBS) test. For the deep-drawing steel sheets, bead penetrations, and bead geometries studied, the pulling force at a 20 • binder angle ranged from 55% to 96% of that corresponding to a 0 • binder angle.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

et al. 2023

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This paper is devoted to the determination of the coefficient of friction (COF) in the drawbead region in metal forming processes. As the test material, AW-5251 aluminium alloys sheets fabricated under various hardening conditions (AW-5251-O, AW-5251-H14, AW-5251-H16 and AW-5251H22) were used. The sheets were tested using a drawbead simulator with different countersample roughness and different orientations of the specimens in relation to the sheet rolling direction. A drawbead simulator was designed to model the friction conditions when the sheet metal passed through the drawbead in sheet metal forming. The experimental tests were carried out under conditions of dry friction and lubrication of the sheet metal surfaces with three lubricants: machine oil, hydraulic oil, and engine oil. Based on the results of the experimental tests, the value of the COF was determined. The Random Forest (RF) machine learning algorithm and artificial neural networks (ANNs) were used to identify the parameters affecting the COF. The R statistical package software version 4.1.0 was used for running the RF model and neural network. The relative importance of the inputs was analysed using 12 different activation functions in ANNs and nine different loss functions in the RF. Based on the experimental tests, it was concluded that the COF for samples cut along the sheet rolling direction was greater than for samples cut in the transverse direction. However, the COF’s most relevant input was oil viscosity (0.59), followed by the average counter sample roughness Ra (0.30) and the yield stress Rp0.2 and strength coefficient K (0.05 and 0.06, respectively). The hard sigmoid activation function had the poorest R2 (0.25) and nRMSE (0.30). The ideal run was found after training and testing the RF model (R2 = 0.90 ± 0.028). Ra values greater than 1.1 and Rp0.2 values between 105 and 190 resulted in a decreased COF. The COF values dropped to 9–35 for viscosity and 105–190 for Rp0.2, with a gap between 110 and 130 when the oil viscosity was added. The COF was low when the oil viscosity was 9–35, and the Ra was 0.95–1.25. The interaction between K and the other inputs, which produces a relatively limited range of reduced COF values, was the least relevant. The COF was reduced by setting the Rp0.2 between 105 and 190, the Ra between 0.95 and 1.25, and the oil viscosity between 9 and 35.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the Frictional Performance of AW-5251 Aluminium Alloy Sheets Using the Random Forest Machine Learning Algorithm and Multilayer Perceptron

et al. 2023

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“…The idea of Nine's simulator construction is to separate the deformation resistance of the sheet from the frictional resistances. The change in friction conditions in the drawbead simulator is obtained by changing the angle of the roll wrapping [10,11], the lubrication conditions [10,12], the shape and dimensions of the drawbead model [13] and the drawing speed [12,13].…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of value of friction coefficient in the drawbead region

Fejkiel¹,

Trzepieciński²

2018

ZN PRz Mechanika

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Drawbeads are supposed to change the stress state of sheets in specific areas of the sheet material, or compensate for material flow at the periphery of the drawpiece, especially when forming complex shaped thin-walled products. The article presents the results of frictional resistance testing of a DC04 steel sheet using a drawbead simulator test. Experimental investigations were carried out using a special device that allows a change in the degree of deformation of the sheet metal on the drawbead. Specimens were tested for: (i) three degrees of sheet deformation; (ii) three countersamples' surface roughness; and (iii) three lubrication conditions: dry friction, machine oil lubrication and paint lubrication. The test specimens were cut out along, and transversely to, the direction of rolling of the sheet metal. The basic relations between technological parameters of friction test and coefficient of friction were determined.

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“…Bae et al [1] carried out investigations using a simulation-based prediction model of the drawbead restraining force, aiming to obtain the ideal drawbead shape to be used in the tool design stage. Smith et al [12] developed a device for obtaining the restraining force of drawbeads oriented in angles and used it to calibrate a simulation model. Using drawbeads, strong local restraining forces can be created as the sheet metal is bent and unbent several times and undergoes surface friction against the forming die.…”

Section: Introductionmentioning

confidence: 99%