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contributor authorShih, Chin-Cheng
contributor authorTai, Bruce L.
date accessioned2025-08-20T09:41:44Z
date available2025-08-20T09:41:44Z
date copyright5/22/2025 12:00:00 AM
date issued2025
identifier issn1087-1357
identifier othermanu-25-1079.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4308698
description abstractKnowing machine tool dynamics and cutting force is critical to machining process optimization, tool life prediction, and in-process monitoring. Identifying system dynamics and estimating dynamic cutting forces often require dedicated procedures and extensive execution effort. This article presents a Fourier neural operator (FNO)-inspired end-to-end architecture for the rapid estimation of dynamic forces by inferring system characteristics through interpretable operator learning in the frequency domain using system excitation and response data. This machine learning method learns to approximate the system frequency response function (FRF) as an intermediate step and subsequently produces a functional mapping from acceleration to dynamic force. For validation, both a numerical study with a theoretical two degrees-of-freedom model and a field experiment on an actual machine tool are conducted. Results demonstrate that this FNO-based method predicts dynamic forces with over 90% accuracy in terms of R2 value for both validation cases, with the approximated FRF offering insights into the underlying machine tool dynamic behavior. Model training considerations, limitations, and practicality of this method, including the approximate nature of the inferred system characteristics, are also discussed in this article.
publisherThe American Society of Mechanical Engineers (ASME)
titleDynamic Cutting Force Estimation via Fourier Neural Operator With Inferred Machine Tool Dynamics: A Proof of Concept
typeJournal Paper
journal volume147
journal issue8
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4068643
journal fristpage81003-1
journal lastpage81003-10
page10
treeJournal of Manufacturing Science and Engineering:;2025:;volume( 147 ):;issue: 008
contenttypeFulltext


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