An Investigation into the Friction of Cold-Rolled Low-Carbon DC06 Steel Sheets in Sheet Metal Forming Using Radial Basis Function Neural Networks

Trzepieciński, Tomasz; Szwajka, Krzysztof; Szewczyk, Marek

doi:10.3390/app13179572

Cited by 4 publications

(1 citation statement)

References 39 publications

(38 reference statements)

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“…A non-ageing DC06 steel is suitable for the most demanding deep-drawing processes [63]. DC04 and DC05 deep-drawing quality steels are suitable for deep drawing with increased requirements and stretch-draw forming.…”

Section: Conventional Low-carbon Steelsmentioning

confidence: 99%

Current Trends in Metallic Materials for Body Panels and Structural Members Used in the Automotive Industry

Trzepieciński,

Najm

2024

Materials

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The development of lightweight and durable materials for car body panels and load-bearing elements in the automotive industry results from the constant desire to reduce fuel consumption without reducing vehicle performance. The investigations mainly concern the use of these alloys in the automotive industry, which is characterised by mass production series. Increasing the share of lightweight metals in the entire structure is part of the effort to reduce fuel consumption and carbon dioxide emissions into the atmosphere. Taking into account environmental sustainability aspects, metal sheets are easier to recycle than composite materials. At the same time, the last decade has seen an increase in work related to the plastic forming of sheets made of non-ferrous metal alloys. This article provides an up-to-date systematic overview of the basic applications of metallic materials in the automotive industry. The article focuses on the four largest groups of metallic materials: steels, aluminium alloys, titanium alloys, and magnesium alloys. The work draws attention to the limitations in the development of individual material groups and potential development trends of materials used for car body panels and other structural components.

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Section: Conventional Low-carbon Steelsmentioning

confidence: 99%

Current Trends in Metallic Materials for Body Panels and Structural Members Used in the Automotive Industry

Trzepieciński,

Najm

2024

Materials

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Wear prediction model of hot rolling backup roll based on FEM + ML algorithm

Lu,

Hao,

Wang

et al. 2024

Int J Adv Manuf Technol

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The wear of backup rolls will have a great impact on the quality of hot rolled strip sheet shape. In order to overcome the limitations of the finite element method (FEM) in calculating the wear of backup rolls in terms of efficiency and accuracy, this paper proposes a FEM+ML tandem hybrid model to optimise the prediction effect of the finite element method (FEM) on the wear of backup rolls. Firstly, a backup roller wear model is established based on FEM. Secondly, in order to select the optimal machine learning (ML) algorithm as the finite element error compensation model, three types of finite element error compensation models were established based on the random forest (RF) algorithm, radial basis neural network (RBF) algorithm, and particle swarm optimisation support vector machine (PSO-SVM) algorithm. Finally, the three types of finite element error compensation models were connected in series with the FEM model to compare the prediction performance of the three types of FEM+ML models on the wear of backup rolls. The numerical experimental results show that the FEM+PSO-SVM model can predict the wear of the backup roll better, and the PSO-SVM algorithm is most suitable for establishing the finite element error compensation model. It is proved that the FEM+ML model proposed in this paper can effectively improve the accuracy and computational efficiency of the FEM model for predicting the wear of the backup rolls without adding microelements. In addition, among the hot rolling parameters, the rolling force has the greatest influence on the wear of the backup rolls, and excessive rolling force should be avoided for a single pass in order to slow down the wear of the backup rolls.

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Analysis of the friction performance of deep-drawing steel sheets using network models

Najm,

Trzepieciński,

Ibrahim

et al. 2024

Int J Adv Manuf Technol

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This article presents the results of pilot studies on the lubrication of the blankholder zone in sheet metal forming using a pressurised lubricant. The authors invented a method and built a special tribometer for pressure-assisted lubrication. This approach reduces friction in sheet metal forming processes compared to conventional lubrication. Moreover, the artificial neural network approach combined with a force-directed Fruchterman-Reingold graph algorithm and Spearman’s correlation was used for the first time to analyse the relationships between the friction process parameters and the output parameters (the coefficient of friction and the resulting surface roughness of the sheet metal). The experimental tests were conducted utilising strip drawing on four grades of steel sheets known to be outstanding for deep-drawing quality. Different oils, oil pressures and contact pressures were used. Artificial neural network models were used for the first time to determine these relationships in a strip drawing test where every parameter is represented by one node, and all nodes are connected by edges with each other. R Software version 4.2.3 was used to construct the network using the ‘qgraph’ and ‘networktools’ packages. It was found that friction conditions had a highly significant negative correlation with coefficient of friction (COF) and a moderately significant negative correlation with the final surface roughness. However, the initial surface roughness of the as-received sheets had a negative correlation with the COF and a positive one with the resulting surface roughness of the sheet metal. The parameters most related to the COF are the strength coefficient, the ultimate tensile strength and the friction conditions (dry friction or pressurised lubrication). Spearman’s correlation coefficients showed a strong correlation between the kinematic viscosity and the friction conditions.

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An Investigation into the Friction of Cold-Rolled Low-Carbon DC06 Steel Sheets in Sheet Metal Forming Using Radial Basis Function Neural Networks

Cited by 4 publications

References 39 publications

Current Trends in Metallic Materials for Body Panels and Structural Members Used in the Automotive Industry

Current Trends in Metallic Materials for Body Panels and Structural Members Used in the Automotive Industry

Wear prediction model of hot rolling backup roll based on FEM + ML algorithm

Analysis of the friction performance of deep-drawing steel sheets using network models

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