[1]张丰,伍占文,肖璐,等. 木塑复合材料铣削加工表面粗糙度及其预测模型的研究[J].西北林学院学报,2022,37(3):191-198.[doi:10.3969/j.issn.1001-7461.2022.03.26]
　ZHANG Feng,WU Zhan-wen,XIAO Lu,et al. Research on Milling Surface Roughness of Wood-Plastic Composites and Its Prediction Model[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2022,37(3):191-198.[doi:10.3969/j.issn.1001-7461.2022.03.26]
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 木塑复合材料铣削加工表面粗糙度及其预测模型的研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第37卷
期数:
2022年第3期
页码:
191-198
栏目:
出版日期:
2022-05-31

文章信息/Info

Title:
 Research on Milling Surface Roughness of Wood-Plastic Composites and Its Prediction Model
文章编号:
1001-7461(2022)03-0191-08
作者:
 张丰1; 伍占文1; 肖璐1; 朱兆龙2; 郭晓磊1*
 (1.南京林业大学 材料科学与工程学院，江苏 南京 210037；2.南京林业大学 家居与工业设计学院，江苏 南京 210037)
Author(s):
 ZHANG Feng1; WU Zhan-wen1; XIAO Lu1; ZHU Zhao-long2; GUO Xiao-lei1*
 （1.College of Materials Science and Engineering,Nanjing Forestry University,Nanjing 210037,Jiangsu,China; 2.College of Furniture and Industrial Design,Nanjing Forestry University,Nanjing 210037,Jiangsu,China)
关键词:
 ; 木塑复合材料; 铣削加工; 表面粗糙度; BP神经网络
Keywords:
 ; wood-plastic composite (WPC);  milling;  surface roughness;  BP neural network
分类号:
TS642
DOI:
10.3969/j.issn.1001-7461.2022.03.26
文献标志码:
A
摘要:
 在木塑复合材料（WPC）的加工过程中，加工精度低、切削表面质量差等问题时有发生，而切削力和切削温度对刀具的寿命和加工表面质量有着非常重要的影响。采用硬质合金单齿柄铣刀对木塑复合材料进行铣削试验，研究主轴转速、进给速度、切削深度等切削参数对切削力、切削温度、加工表面粗糙度的影响。在试验数据的基础上，采用BP神经网络建立了WPC加工表面粗糙度的预测模型。结果表明，随着主轴转速增大，WPC的切削力减小，切削温度增大，表面粗糙度减小；随着进给速度增大，WPC的切削力增大，切削温度减小，表面粗糙度增大；随着铣削深度增加，WPC的切削力增大，切削温度增大，表面粗糙度逐渐增大。建立的预测模型具有较高的精度，能够用于WPC铣削加工表面粗糙度的预测，为提高WPC加工表面质量、刀具使用寿命提供了理论和实践指导。
Abstract:
 Low machining accuracy,poor cutting surface quality and other problems often occur during the processing of wood-plastic composites (WPC),and cutting force and cutting temperature impose important impacts on tool life and surface quality.In this paper,a cemented carbide single tooth handle milling cutter was used for milling tests on WPC to study the effects of cutting parameters such as spindle speed,feed rate and cutting depth on cutting force,cutting temperature and machined surface roughness.Based on the experimental data,BP neural network was adopted to establish a prediction model for the surface roughness of WPC machining.The results showed that along with the increase of spindle speed,the cutting temperature increased,but the cutting force and surface roughness decreased.As the feed speed increased,the cutting temperature decreased,but the cutting force and surface roughness increased.With the increase of milling depth,the cutting force,cutting temperature and surface roughness increased.The prediction model has high accuracy and can be used to predict the surface roughness in WPC milling.It provides theoretical and practical guidance for improving WPC machining surface quality and tool service life.

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备注/Memo

备注/Memo:
 收稿日期：2021-06-15修回日期：2021-08-22
基金项目：国家自然科学基金（31971594）。
第一作者：张丰。研究方向：木材加工装备与信息化。E-mail：Njfuzhangfn@163.com
*通信作者：郭晓磊，博士，副教授。研究方向：木材切削加工技术与装备。E-mail：youngleiguo@hotmail.com
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