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contributor authorMa, Qiyang
contributor authorZhong, Yuhao
contributor authorWang, Zimo
contributor authorBukkapatnam, Satish
date accessioned2024-12-24T19:10:20Z
date available2024-12-24T19:10:20Z
date copyright12/4/2023 12:00:00 AM
date issued2023
identifier issn1087-1357
identifier othermanu_146_3_031003.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303421
description abstractNatural 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 for sustainable manufacturing. However, critical issues reside in the machinability of such materials because of their multiscale 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 estimated R-squared value 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 thereafter play a significant role in increasing the cutting forces during NFRP machining. Therefore, the presented framework of the explainable machine learning approach 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleEffect of Microstructure on the Machinability of Natural Fiber Reinforced Plastic Composites: A Novel Explainable Machine Learning (XML) Approach
typeJournal Paper
journal volume146
journal issue3
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4064039
journal fristpage31003-1
journal lastpage31003-14
page14
treeJournal of Manufacturing Science and Engineering:;2023:;volume( 146 ):;issue: 003
contenttypeFulltext


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