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机器学习模型用于钻探不同类型的玻璃纤维增强聚合物复合材料

Katarzyna Biruk-Urban¹, Paul Bere², Jerzy Józwik¹

¹Department of Production Engineering, Mechanical Engineering Faculty, Lublin University of Technology, 20-618 Lublin, Poland.

|January 17, 2024

概括

这项研究模拟了玻璃纤维增强聚合物 (GFRP) 复合材料的钻井参数. 每颗牙的更高的料增加了分层,而织纤维和更低的料减少了切削力.

科学领域:

材料科学材料科学材料科学
制造业工程制造工程
聚合物复合材料聚合物复合材料

背景情况:

玻璃纤维增强聚合物 (GFRP) 是各种工业中使用的先进材料.
机械加工GFRP由于材料异质性和磨料性质而带来了挑战.
了解钻井参数对于最小化除层等损坏至关重要.

研究的目的:

模拟GFRP加工中钻孔参数 (切割速度,每个牙的料) 和切割力/分层之间的关系.
为了评估不同纤维类型 (平面 vs 织) 和重量分数对钻井结果的影响.
为了比较五种机器学习回归模型在预测这些关系方面的有效性.

主要方法:

制造了四种GFRP复合材料类型,具有不同的纤维结构和重量分数.
在垂直加工中心使用碳化石钻石涂层钻孔钻孔.
使用Kistler 9257B测试台测量了切削力.
使用一种新的油墨透方法评估分层面积.
采用五种机器学习回归模型进行模拟和分析.

主要成果:

渐变增强回归器确定每颗牙的料是影响分层的最重要因素.
每颗牙的料增加导致了更大的分层.
平面编织纤维材料表现出更高的推出分层因子.
最低的切削力组件 (F) 在每颗牙的最低料 (0.04毫米) 中被观察到,特别是在双纤维材料中.

结论:

每颗牙的料是控制GFRP钻井中分层的关键参数.
材料架构,特别是纤维类型,影响了分层的易受性.
优化钻孔参数,特别是料速度,对于高质量的GFRP加工至关重要.
机器学习模型有效地模拟了GFRP钻井中的复杂关系.

关键词:

用玻璃纤维增强的聚合物 (GFRP)切割力是指切割的力量.脱层是分层的方法.钻探钻探钻探钻探机器学习 (ML) 是指机器学习.

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