微观结构对天然纤维增强塑料复合材料可加工性的影响:一种新的可解释机器学习(XML)方法

IF 2.4 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
Qiyang Ma, Yuhao Zhong, Zimo Wang, Satish Bukkapatnam
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引用次数: 0

摘要

摘要:天然纤维增强塑料(NFRP)复合材料是一种生态友好的可生物降解材料,在保持其独特结构和性能的同时具有巨大的生态优势。在纤维增强材料中使用这些天然纤维作为传统合成纤维替代品的研究为工业应用,特别是可持续制造开辟了可能性。然而,由于这种材料的多尺度结构和增强元素分布在基体基中的随机性,关键问题在于其可加工性。本文全面研究了NFRP加工中微观结构非均匀性对切削力综合行为的影响。卷积神经网络(CNN)将微观结构增强纤维及其对切削力变化的影响联系起来(估计精度超过90%)。接下来,实现了一种模型不可知的可解释机器学习方法,通过发现与增强元件/纤维微观结构相关的潜在机制来破译这个CNN黑箱模型。提出的XML方法从加工过程中监测的显微图像中提取物理描述符,并找到纤维结构的异质性与加工合力的因果关系。结果表明,对于非均质纤维,纤维单元紧密且均匀结合(即具有较低的长径比、较低的偏心率和较高的压实度)增强了材料的强度,增加了切削力。因此,提出的可解释的机器学习框架为发现材料微观结构对合成过程动力学的因果关系以及准确预测材料去除过程中的切削行为提供了机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Effect of Microstructure on the Machinability of Natural Fiber Reinforced Plastic Composites: A Novel Explainable Machine Learning (XML) Approach
Natural fiber reinforced plastic (NFRP) composites are ecofriendly and biodegradable materials that offer tremendous ecological advantages while preserving unique structures and properties. Studies on using these natural fibers as alternatives to conventional synthetic fibers in fiber-reinforced materials have opened up possibilities for industrial applications, especially sustainable manufacturing. However, critical issues reside in the machinability of such materials because of their multi-scale structure and the randomness of the reinforcing elements distributed within the matrix basis. This paper reports a comprehensive investigation of the effect of microstructure heterogeneity on the resultant behaviors of cutting forces for NFRP machining. A convolutional neural network (CNN) links the microstructural reinforcing fibers and their impacts on changing the cutting forces (with an estimation accuracy of over 90%). Next, a model-agnostic explainable machine learning approach is implemented to decipher this CNN black-box model by discovering the underlying mechanisms of relating the reinforcing elements/fibers' microstructures. The presented XML approach extracts physical descriptors from the in-process monitoring microscopic images and finds the causality of the fibrous structures' heterogeneity to the resultant machining forces. The results suggest that, for the heterogeneous fibers, the tightly and evenly bounded fiber elements (i.e., with lower aspect ratio, lower eccentricity, and higher compactness ) strengthen the material and increase the cutting forces. Therefore, the presented explainable machine learning framework opens an opportunity to discover the causality of material microstructures on the resultant process dynamics and accurately predict the cutting behaviors during material removal processes.
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来源期刊
CiteScore
6.80
自引率
20.00%
发文量
126
审稿时长
12 months
期刊介绍: Areas of interest including, but not limited to: Additive manufacturing; Advanced materials and processing; Assembly; Biomedical manufacturing; Bulk deformation processes (e.g., extrusion, forging, wire drawing, etc.); CAD/CAM/CAE; Computer-integrated manufacturing; Control and automation; Cyber-physical systems in manufacturing; Data science-enhanced manufacturing; Design for manufacturing; Electrical and electrochemical machining; Grinding and abrasive processes; Injection molding and other polymer fabrication processes; Inspection and quality control; Laser processes; Machine tool dynamics; Machining processes; Materials handling; Metrology; Micro- and nano-machining and processing; Modeling and simulation; Nontraditional manufacturing processes; Plant engineering and maintenance; Powder processing; Precision and ultra-precision machining; Process engineering; Process planning; Production systems optimization; Rapid prototyping and solid freeform fabrication; Robotics and flexible tooling; Sensing, monitoring, and diagnostics; Sheet and tube metal forming; Sustainable manufacturing; Tribology in manufacturing; Welding and joining
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