Slider on Sheet Tester Development for Characterizing Galling

Venema, Jenny; Korver, Frank; Chezan, Toni

doi:10.4028/p-uii69m

Cited by 2 publications

(4 citation statements)

References 5 publications

(4 reference statements)

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“…The appearance of the scratches, ploughing tracks and particles are similar to observations on automotive pressed parts and particles collected from stamping tools [1]. The tool wear mechanism is dynamic.…”

Section: Resultssupporting

confidence: 69%

“…Such contact areas on the deformed sheet surface can be considerably large, tens of millimeters in the sliding length direction, as the sheet metal slides against the forming tools. In some contact areas unexpected galling behavior in the form of long narrow scratches, in the sheet metal sliding direction [1] was observed. Such scratches are created by tool surface local asperities.…”

Section: Introductionmentioning

confidence: 99%

“…In a previous investigation [1] the influence of pressure and tool roughness on galling was investigated with the Slider On Sheet Tester (SOST) on one material with representative roughness. In the current investigations the influence of the tool roughness is investigated for three sheet material roughness levels within the typical intervals found in automotive applications.…”

Section: Introductionmentioning

confidence: 99%

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Surface topography effects on galling of hot dip galvanized sheet metal

VENEMA¹

2023

Materials Research Proceedings

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Abstract. During manufacturing of automotive parts from hot dip galvanized sheet metal, surface asperities of the forming tools can cause breakage of small coating particles. Long and narrow scratches appear on the surface of the form part, a phenomenon known as part surface galling. Laboratory testing using a slider on sheet tests (SOST) are performed in order to investigate surface topography effects on galling. These experiments reveal the dominant effect of tool surface roughness on galling. The tool surface roughness has a large effect on the size of the detached coating particles and the distance before scratch occurrence. If the tool roughness is low enough, no surface scratch formation is observed for the investigated range of sheet surface roughness. At a high tool surface roughness scratches are observed at a very short sliding distance for all tested materials. At an intermediate tool surface roughness, the materials selected for this investigation show measurable differences but no clear trend could be identified.

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Section: Resultssupporting

confidence: 69%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface topography effects on galling of hot dip galvanized sheet metal

VENEMA¹

2023

Materials Research Proceedings

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“…They found that minor geometrical deviations in the drawbead geometry significantly affect the uplift force. Venema et al [27] observed that the coating flakes transported by the sliding sheet tend to accumulate in the area of high contact pressure (the drawbead region). Zabala et al [28] found a direct relationship between the COF, thinning and forming limit diagram.…”

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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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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Slider on Sheet Tester Development for Characterizing Galling

Cited by 2 publications

References 5 publications

Surface topography effects on galling of hot dip galvanized sheet metal

Surface topography effects on galling of hot dip galvanized sheet metal

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

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