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基于层合叠加理论的CFRP多向层合板铣削力建模 被引量:2

Modeling about milling force for multidirectional CFRP based on theory of superposition
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摘要 针对碳纤维增强树脂基复合材料(CFRP)多向层合板设计中常用的典型纤维铺层方向,实验研究CFRP单向层合板的铣削力随纤维方向角及加工参数的变化规律,使用BP神经网络,获取不同加工参数下的CFRP 4种典型铺层方向单向层合板铣削力数据;基于BP神经网络结果数据和切削力层合叠加理论,建立CFRP多向层合板铣削力预测模型。研究结果表明:纤维方向角是影响铣削力的最主要的因素,该CFRP多向层合板铣削力预测模型精度可以达到85%以上。 Experiments were conducted to investigate the relationship between cutting parameters and cutting force of representative unidirectional CFRP. By using the back-propagation artificial neural network, the milling forces of the representative unidirectional CFRP were obtained. Based on the time-domain graph of unidirectional CFRP milling forces' and the theory of superposition, a prediction model of multidirectional CFRP cutting forces was established and modified, and the verification experiments were conducted. The results show that the cutting angel is the most important factor to influence the milling forces. The prediction accuracy is above 85%.
作者 王福吉 朱浩杰 宿友亮 高汉卿 毕广健 WANG Fuji ZHU Haojie SU Youliang GAO Hanqing BI Guangjian(College of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China)
出处 《中南大学学报(自然科学版)》 EI CAS CSCD 北大核心 2017年第9期2352-2362,共11页 Journal of Central South University:Science and Technology
基金 国家重点基础研究发展计划(973计划)项目(2014CB046503) 国家自然科学基金资助项目(51575082 U1508207 51621064) 中央高校基本科研业务费专项资金资助项目(DUT16TD01)~~
关键词 CFRP多向层合板 切边精加工 铣削力预测 BP神经网络 层合叠加理论 multidirectional CFRP edge trimming milling force prediction BP neural network theory of superposition
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