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

Abstract

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.

Reuse and Mechanochemical Processing of Ore Dressing Tailings Used for Extracting Pb and Zn

“…The analysis of the scattered data remains a rather complex task that is solved in different ways. The algorithms based on machine learning or deep learning are mainly used [85][86][87], including ANN in combination with the Levenberg-Marquardt Scheme having backpropagation [88,89], Shapley Additive exPlanations (SHAP) in combination with CatBoost (an AI model was used for increasing the gradients on decision trees) [90], two-dimensional regression models [91][92][93], Shapley Value Regression [94], Nearest Neighbor Method [95], etc. The main disadvantages of ANN, as well as of other "stochastic" interpolation methods, are provided in [96][97][98].…”

Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining

“…Additionally, it is important for the dataset size to be sufficiently large, which has not been consistently observed in the literature. Furthermore, in the domain of grinding, proficient neural network implementation should encompass various sizes and transfer functions [32][33][34]. Addressing the gaps in the current literature, this study utilizes ANNs with a substantial dataset [15][16][17][18][19]35,36], varying sizes, and transfer functions.…”

A data-driven approach for studying tribology based on experimentation and artificial intelligence coupling tools

“…Some approaches are based on the Fuzzy-neuro system which is the combination of the two materials on the contact effect and the characteristics of the sliding system [11]. Therefore, successful case studies using these approaches in a tribological context demonstrate their ability to accurately and efficiently predict tribological features [12] in the design of materials composition [13], lubricant formulations [14], lubrication and fluid film establishment [15], and interaction first bodies-environment [16]. However, certain models had certain limitations like they cannot be applicable for estimating the tribological behavior of wider varieties of materials that contain rigid structures with high wear resistance levels [17].…”