植物油基润滑油对Ti-6al-4v钛合金薄板润滑效果的多元建模

IF 1.5 Q4 MATERIALS SCIENCE, MULTIDISCIPLINARY Advances in Materials Science Pub Date : 2021-06-01 DOI:10.2478/adms-2021-0009
T. Trzepieciński, Marcin Szpunar
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引用次数: 0

摘要

摘要本文介绍了利用人工神经网络和方差分析对摩擦现象进行建模的结果。试验材料为0.5 mm厚的α - β 5级(Ti-6Al-4V)钛合金板材制成的条形试样。试验采用专用摩擦试验机模拟冲压成形过程中冲压件与钣金件之间的摩擦情况。在六种环保油(棕榈油、椰子油、橄榄油、葵花籽油、大豆油和亚麻籽油)润滑的条件下,进行了一项称为条形拉伸试验的试验。基于带钢拉拔试验结果,建立了工艺参数与摩擦系数的回归模型和人工神经网络模型,确定了工艺参数与摩擦系数之间的复杂相互作用。一个隐藏层和8个神经元的多层感知器对训练数据的拟合效果最好。使用Levenberg-Marquardt、back propagation和准牛顿三种算法对网络进行训练。考虑决定系数R2(0.962)和标准差比(0.272),采用Levenberg-Marquardt算法训练的网络具有最佳的回归特征。从二次回归模型的响应面可以看出,在特定压力下,润滑剂密度的增加会导致摩擦系数的减小。低密度和高运动粘度的油导致高摩擦系数。
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Multivariate Modelling of Effectiveness of Lubrication of Ti-6al-4v Titanium Alloy Sheet using Vegetable Oil-Based Lubricants
Abstract The article presents the results of modelling the friction phenomenon using artificial neural networks and analysis of variance. The test material was composed of strip specimens made of 0.5-mm-thick alpha-beta Grade 5 (Ti-6Al-4V) titanium alloy sheet. A special tribotester was used in the tests to simulate the friction conditions between the punch and the sheet metal in the sheet metal forming process. A test called the strip drawing test has been conducted in conditions in which the sheet surface is lubricated with six environmentally friendly oils (palm, coconut, olive, sunflower, soybean and linseed). Based on the results of the strip drawing test, a regression model and an artificial neural network model were built to determine the complex interactions between the process parameters and the friction coefficient. A multilayer perceptron with one hidden layer and eight neurons in this layer showed the best fit to the training data. The network training was conducted using three algorithms, i.e. Levenberg-Marquardt, back propagation and quasi-Newton. Taking into consideration both the coefficient of determination R2 (0.962) and S.D. ratio (0.272), the best regression characteristics were presented by the network trained using the Levenberg-Marquardt algorithm. From the response surfaces of the quadratic regression model it was found that an increase in the density of lubricant at a specific pressure causes a reduction in the coefficient of friction. Low density and high kinematic viscosity of the oil leads to a high coefficient of friction.
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Advances in Materials Science
Advances in Materials Science MATERIALS SCIENCE, MULTIDISCIPLINARY-
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