Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization

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Studies on manufacturing cost prediction based on deep learning have begun in recent years, but the cost prediction rationale cannot be explained because the models are still used as a black box. This study aims to propose a manufacturing cost prediction process for 3D computer-aided design (CAD) models using explainable artificial intelligence. The proposed process can visualize the machining features of the 3D CAD model that are influencing the increase in manufacturing costs. The proposed process consists of (1) data collection and pre-processing, (2) 3D deep learning architecture exploration, and (3) visualization to explain the prediction results. The proposed deep learning model shows high predictability of manufacturing cost for the computer numerical control (CNC) machined parts. In particular, using 3D gradient-weighted class activation mapping proves that the proposed model not only can detect the CNC machining features but also can differentiate the machining difficulty for the same feature. Using the proposed process, we can provide a design guidance to engineering designers in reducing manufacturing costs during the conceptual design phase. We can also provide real-time quotations and redesign proposals to online manufacturing platform customers.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2021-11
Language
English
Article Type
Article
Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.183

ISSN
0957-4174
DOI
10.1016/j.eswa.2021.115430
URI
http://hdl.handle.net/10203/287747
Appears in Collection
GT-Journal Papers(저널논문)
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