Effects of the cooling mode on the integrity and the multi-pass micro-scratching wear resistance of Hardox 500 ground surfaces

Bensaid, Kamel; Dhiflaoui, Hafedh; Bouzaiene, Hassen; Yahyaoui, Houda; Fredj, Nabil Ben

doi:10.1007/s00170-021-06719-x

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Li [58] 和 Hood 等 [59] 通过在石墨片上对砂轮仿形获得砂轮磨损量. Bensaid 等 [60] 通过在宽约 1 mm 的钢片上应用仿形法来评估砂轮的径向磨损. Miao 等 [61] 采用仿形法测量了砂轮的径向磨损, 比较了棕刚玉和微晶刚玉砂轮的磨损程度.…”

Section: F O R R E V I E W O N L Yunclassified

Research on grinding wheel wear measurement methods: Current status and future perspectives

LI,

BAO,

WANG

et al. 2024

Sci. Sin.-Tech.

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Section: F O R R E V I E W O N L Yunclassified

Research on grinding wheel wear measurement methods: Current status and future perspectives

LI,

BAO,

WANG

et al. 2024

Sci. Sin.-Tech.

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“…Friction in sheet-metal-forming processes is a complex function of tool-and sheetsurface roughness, friction conditions, lubricant properties, sliding velocity and pressure values [8][9][10]. In most SMF processes, the occurrence of friction is an undesirable phenomenon and causes [11][12][13][14] increased forming force, irregular deformation, and a reduction in product quality and tool life. The main parameters influencing tribological phenomena in SMF also include the dynamics of load, kinematics of tool movement, physicochemical phenomena occurring on the contact surface of the tool and workpiece and, finally, the temperature of the forming process [7,15,16].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to the Analysis of Friction Behaviour in a Drawbead Profile in Sheet Metal Forming

Trzepieciński

Najm

2022

Materials

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Drawbeads are used when forming drawpieces with complex shapes to equalise the flow resistance of a material around the perimeter of the drawpiece or to change the state of stress in certain regions of the drawpiece. This article presents a special drawbead simulator for determining the value of the coefficient of friction on the drawbead. The aim of this paper is the application of artificial neural networks (ANNs) to understand the effect of the most important parameters of the friction process (sample orientation in relation to the rolling direction of the steel sheets, surface roughness of the counter-samples and lubrication conditions) on the coefficient of friction. The intention was to build a database for training ANNs. The friction coefficient was determined for low-carbon steel sheets with various drawability indices: drawing quality DQ, deep-drawing quality DDQ and extra deep-drawing quality EDDQ. Equivalents of the sheets tested in EN standards are DC01 (DQ), DC03 (DDQ) and DC04 (EDDQ). The tests were carried out under the conditions of dry friction and the sheet surface was lubricated with machine oil LAN46 and hydraulic oil LHL32, commonly used in sheet metal forming. Moreover, various specimen orientations (0° and 90°) in relation to the rolling direction of the steel sheets were investigated. Moreover, a wide range of surface roughness values of the counter-samples (Ra = 0.32 μm, 0.63 μm, 1.25 μm and 2.5 μm) were also considered. In general, the value of the coefficient of friction increased with increasing surface roughness of the counter-samples. In the case of LAN46 machine oil, the effectiveness of lubrication decreased with increasing mean roughness of the counter-samples Ra = 0.32–1.25 μm. With increasing drawing quality of the sheet metal, the effectiveness of lubrication increased, but only in the range of surface roughness of the counter-samples in which Ra = 0.32–1.25 μm. This study investigated different transfer functions and training algorithms to develop the best artificial neural network structure. Backpropagation in an MLP structure was used to build the structure. In addition, the COF was calculated using a parameter-based analytical equation. Garson partitioning weight was used to calculate the relative importance (RI) effect on coefficient of friction. The Bayesian regularization backpropagation (BRB)—Trainbr training algorithm, together with the radial basis normalized—Radbasn transfer function, scored best in predicting the coefficient of friction with R2 values between 0.9318 and 0.9180 for the training and testing datasets, respectively.

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Improvement of Machinability of Ground Hardox 500 Steel

Bensaid,

Ben Moussa,

Ben Fredj

2024

Lecture Notes in Mechanical Engineering

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Effects of the cooling mode on the integrity and the multi-pass micro-scratching wear resistance of Hardox 500 ground surfaces

Cited by 6 publications

References 37 publications

Research on grinding wheel wear measurement methods: Current status and future perspectives

Research on grinding wheel wear measurement methods: Current status and future perspectives

Application of Artificial Neural Networks to the Analysis of Friction Behaviour in a Drawbead Profile in Sheet Metal Forming

Improvement of Machinability of Ground Hardox 500 Steel

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