基于图形用户界面的斜交拖拉机轮胎接触面积预测人工神经网络模型的开发

IF 2.4 3区 工程技术 Q3 ENGINEERING, ENVIRONMENTAL
Rajesh Yadav, Hifjur Raheman
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引用次数: 4

摘要

建立了一个用于预测斜交拖拉机轮胎接触面积和地面压力的人工神经网络模型。使用Tkinter创建了一个图形用户界面(GUI)来部署所开发的ANN模型。从实验试验中收集了12.4–28、13.6–28、14.9–28和16.9–28轮胎的538个数据集,通过改变轮胎负载(600至1950 kg)和充气压力(69至234 kPa),并分别以70:30的比例划分用于ANN模型的训练和测试。另外50个所有轮胎的新数据集用于验证目的。将轮胎的标称宽度和总直径、正常载荷和充气压力作为所开发的ANN模型的输入参数,并将轮胎的接触面积作为输出参数。从灵敏度分析中可以明显看出,所有输入参数都会显著影响轮胎的接触面积。训练有素的人工神经网络模型与GUI相结合,预测了在任何充气压力和载荷下,轮胎在坚硬表面下的接触面积和地面压力,与实际测量值的最大偏差为2%,而Komandi模型的最大偏差分别为10%和-9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of an artificial neural network model with graphical user interface for predicting contact area of bias-ply tractor tyres on firm surface
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来源期刊
Journal of Terramechanics
Journal of Terramechanics 工程技术-工程:环境
CiteScore
5.90
自引率
8.30%
发文量
33
审稿时长
15.3 weeks
期刊介绍: The Journal of Terramechanics is primarily devoted to scientific articles concerned with research, design, and equipment utilization in the field of terramechanics. The Journal of Terramechanics is the leading international journal serving the multidisciplinary global off-road vehicle and soil working machinery industries, and related user community, governmental agencies and universities. The Journal of Terramechanics provides a forum for those involved in research, development, design, innovation, testing, application and utilization of off-road vehicles and soil working machinery, and their sub-systems and components. The Journal presents a cross-section of technical papers, reviews, comments and discussions, and serves as a medium for recording recent progress in the field.
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