补偿(心理学)
过程(计算)
机械加工
热的
人工神经网络
田口方法
计算机科学
工程类
控制理论(社会学)
控制工程
机械工程
人工智能
控制(管理)
机器学习
气象学
物理
精神分析
操作系统
心理学
作者
Abderrazak El Ouafi,Michel Guillot,Noureddine Barka
出处
期刊:Advanced Materials Research
日期:2013-02-01
卷期号:664: 907-915
被引量:17
标识
DOI:10.4028/www.scientific.net/amr.664.907
摘要
Thermally induced errors play a critical role in controlling the level of machining accuracy. They can represent a significant proportion of dimensional errors in produced parts. Since thermal errors cannot totally be eliminated at the design stage, active errors compensation appears to be the most economical and realistic solution. Accurate and efficient modeling of the thermally induced errors is an indispensable part of the error compensation process. This paper presents an integrated and comprehensive modeling approach for real-time thermal error compensation. The modeling process is based on multiple temperature measurements, Taguchi’s orthogonal arrays, artificial neural networks and various statistical tools to provide cost effective selection of appropriate temperature variables and modeling conditions as well as to achieve robust and accurate thermal error models. The experimental results on a CNC turning center confirm the feasibility and efficiency of the proposed approach and show that the resultant model can accurately predict the time-variant spindle thermal drift errors under various operating conditions. After compensation, the thermally induced spindle errors were reduced from 19m to less than 1 m. The proposed modeling optimization strategy can be effectively and advantageously used for real-time error compensation since it presents the benefit of straightforward application, reduced modeling time and uncertainty.
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