基于多元回归分析和人工神经网络的立铣削表面粗糙度建模与预测

Q3 Engineering

Tribology in Industry Pub Date : 2022-09-15 DOI:10.24874/ti.1368.07.22.09

Strahinja Ðurovic, Jelena Stanojković, D. Lazarević, Bogdan Ćirković, Aleksa Lazarvic, D. Džunić, Ž. Šarkočević

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引用次数: 0

摘要

近年来，趋势是使用人工智能对机器加工进行建模。人工神经网络(ANN)和多元回归分析是用于制造技术性能建模和优化的方法。人工神经网络和多元回归分析对加工过程的预测和优化具有较高的可靠性。在立铣削加工过程中，采用主轴转速、进给速度和切削深度等加工参数，使表面粗糙度最小化。采用人工神经网络和多元回归分析方法研究了各参数对表面粗糙度的影响，并与实测结果进行了比较

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Modeling and Prediction of Surface Roughness in the End Milling Process using Multiple Regression Analysis and Artificial Neural Network

In recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high reliability in the prediction and optimization of machining processes. In this paper, machining parameters such as spindle speed, feed rate and depth of cut were used in end milling process to minimize surface roughness. The influence of the parameters on the surface roughness was investigated using an artificial neural network and multiple regression analysis, and results are compared with the measured results

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来源期刊

Tribology in Industry Engineering-Mechanical Engineering

CiteScore

2.80

自引率

0.00%

发文量

审稿时长

8 weeks

期刊介绍： he aim of Tribology in Industry journal is to publish quality experimental and theoretical research papers in fields of the science of friction, wear and lubrication and any closely related fields. The scope includes all aspects of materials science, surface science, applied physics and mechanical engineering which relate directly to the subjects of wear and friction. Topical areas include, but are not limited to: Friction, Wear, Lubricants, Surface characterization, Surface engineering, Nanotribology, Contact mechanics, Coatings, Alloys, Composites, Tribological design, Biotribology, Green Tribology.