A Review on Friction and Lubrication in Automotive Metal Forming: Experiment and Modeling

Lee, Kijung; Moon, Chang-Hyun; Lee, Myoung‐Gyu

doi:10.1007/s12239-021-0150-z

Cited by 17 publications

(3 citation statements)

References 106 publications

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“…Therefore, numerical modeling of friction and lubrication in automotive metal forming has been elaborated on using the ICME approach. [179] Karupannasamy et al developed a deterministic multiscale contact model that describes the flattening and plowing phenomena of surface asperity to investigate the frictional behavior between sheet metals and tools. [180] Results showed that the frictional behavior depended on contact pressure and microgeometry of the tool and sheet metal.…”

Section: Tribologymentioning

confidence: 99%

Integrated Computational Materials Engineering for Advanced Automotive Technology: With Focus on Life Cycle of Automotive Body Structure

Park

Min

Kim

et al. 2022

Adv Materials Technologies

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Integrated computational materials engineering (ICME) is a simulation‐driven design approach that employs multiscale‐multiphysics modeling and is based on the understanding of process–structure–property–performance relationships. The ICME approach has attracted considerable attention in the automotive industry, considering that it significantly reduces development cost and time in optimization of materials and processes through computational advancement. This study presents a comprehensive review of ICME activities in advanced automotive manufacturing technology, which focuses on the life cycle of the automotive body structure such as structural material design, manufacturing process optimization, and reliability improvement in service quality. Results show that the ICME approach has been widely implemented in automotive manufacturing processes, from development of lightweight materials to performance management. However, further advances and technological strategies should be sought to develop more robust methodologies that can replace conventional experiment‐based design approaches.

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Section: Tribologymentioning

confidence: 99%

Integrated Computational Materials Engineering for Advanced Automotive Technology: With Focus on Life Cycle of Automotive Body Structure

Park

Min

Kim

et al. 2022

Adv Materials Technologies

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“…Lubricating the friction pair is effective and is the cheapest way to reduce friction [11]. Due to their consistency, solid-based lubricants, emulsions and liquid lubricants (oils) are distinguished [12]. According to the criterion of origin, lubricants are divided into refined (obtained from crude oil), synthetic, and natural lubricants (vegetable oils and animal fats).…”

Section: Introductionmentioning

confidence: 99%

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

Trzepieciński

2024

Materials

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Friction is one of the main phenomena accompanying sheet metal forming methods, affecting the surface quality of products and the formability of the sheet metal. The most basic and cheapest way to reduce friction is to use lubricants, which should ensure the highest lubrication efficiency and at the same time be environmentally friendly. Due to the trend towards sustainable production, vegetable oils have been used in research as an alternative to petroleum-based lubricants. The analysis of friction in sheet metal forming requires an appropriate tribotester simulating the friction conditions in a specific area of the sheet metal being formed. Research has used a special strip drawing tribometer, enabling the determination the value of the coefficient of friction in the blankholder zone in the deep drawing process. Quantitative analysis of the friction phenomenon is necessary at the stage of designing the technological process and selecting technological parameters, including blankholder pressure. This article presents the results of friction testing of 1.0338 (DC04) steel sheets using a strip drawing test. The experimental tests involved pulling a strip of sheet metal between two countersamples with a rounded surface. The tests were carried out on countersamples with different levels of roughness for the range of contact pressures occurring in the blankholder zone in the deep drawing process (1.7–5 MPa). The values of the coefficient of friction determined under dry friction conditions were compared with the results for edible (corn, sunflower and rapeseed) and non-edible (Moringa, Karanja) vegetable lubricants. The tested oils are the most commonly used vegetable-based biolubricants in metal forming operations. Multi-layer artificial neural networks were used to determine the relationship between the value of the contact pressure, the roughness of the countersamples, the oil viscosity and density, and the value of the coefficient of friction. Rapeseed oil provided the best lubrication efficiency during friction testing for all of the tested samples, with an average surface roughness of Sa 0.44–1.34 μm. At the same time, as the roughness of the countersamples increased, a decrease in lubrication efficiency was observed. The lowest root mean squared error value was observed for the MLP-4-8-1 network trained with the quasi-Newton algorithm. Most of the analysed networks with different architectures trained using the various algorithms showed that the kinematic viscosity of the oil was the most important aspect in assessing the friction of the sheets tested. The influence of kinematic viscosity on the value of the coefficient of friction is strongly dependent on the surface roughness of the countersamples.

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“…However, it has the disadvantage that the pin repeatedly contacts the already worn sheet material surface. To compensate for this shortcoming, slider-on-sheet-type wear test methods, in which the tool comes into contact with the virgin sheet material, have been used [19][20][21][22][23]. However, the wear test methods described above do not reflect the deformation modes encountered in automotive sheet metal forming.…”

Section: Introductionmentioning

confidence: 99%

Efficient Wear Simulation Methodology for Predicting Nonlinear Wear Behavior of Tools in Sheet Metal Forming

et al. 2022

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In conventional wear simulation, the geometry must be updated for succeeding iterations to predict the accumulated wear. However, repeating this procedure up to the desired iteration is rather time consuming. Thus, a wear simulation process capable of reasonable quantitative wear prediction in reduced computational time is needed. This study aimed to develop an efficient wear simulation method to predict quantitative wear reasonably in reduced computational time without updating the geometry for succeeding iterations. The wear resistance of a stamping tool was quantitatively evaluated for different punch shapes (R3.0 and R5.5) and coating conditions (physical vapor deposition of CrN and AlTiCrN coatings) by using a progressive die set. To capture the nonlinear wear behavior with respect to strokes, a nonlinear equation from a modified form of Archard’s wear model was proposed. By utilizing the scale factor representing the changes in wear properties with respect to wear depth as input, the simulation can predict the behavior of rapidly increasing wear depth with respect to strokes after failure initiation. Furthermore, the proposed simulation method is efficient in terms of computational time because it does not need to perform geometry updates.

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A Review on Friction and Lubrication in Automotive Metal Forming: Experiment and Modeling

Cited by 17 publications

References 106 publications

Integrated Computational Materials Engineering for Advanced Automotive Technology: With Focus on Life Cycle of Automotive Body Structure

Integrated Computational Materials Engineering for Advanced Automotive Technology: With Focus on Life Cycle of Automotive Body Structure

The Comparison of the Multi-Layer Artificial Neural Network Training Methods in Terms of the Predictive Quality of the Coefficient of Friction of 1.0338 (DC04) Steel Sheet

Efficient Wear Simulation Methodology for Predicting Nonlinear Wear Behavior of Tools in Sheet Metal Forming

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