Knowledge-embedded machine learning and its applications in smart manufacturing

Farzam Farbiz (), Mohd Salahuddin Habibullah (), Brahim Hamadicharef (), Tomasz Maszczyk () and Saurabh Aggarwal ()
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Farzam Farbiz: Institute of High Performance Computing
Mohd Salahuddin Habibullah: Institute of High Performance Computing
Brahim Hamadicharef: Institute of High Performance Computing
Tomasz Maszczyk: Institute of High Performance Computing
Saurabh Aggarwal: Institute of High Performance Computing

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 7, No 2, 2889-2906

Abstract: Abstract Demands for more accurate machine learning models have given rise to rethinking current modeling approaches that were deemed unsuitable, primarily due to their computational complexity and the lack of availability and accessibility to representative data. In Industry 4.0, rapid advancements in Digital Twin (DT) technologies and the pervasiveness of cost-effective sensor technologies have pushed the incorporation of artificial intelligence, particularly data-driven machine learning models, for use in smart manufacturing. However, the persistent issue with such models is their high sensitivity to the training data and the lack of interpretability in the outcomes, at times generating unrealistic results. The incorporation of knowledge into the machine learning pipeline has been earmarked as the most promising approach to address such issues. This paper aims to answer this call through a Knowledge-embedded Machine Learning (KML) framework for smart manufacturing, which embeds knowledge from experience and, or physics information into the machine learning pipeline, thus making the outcomes from these models more representative of real applications. The merits of KML were then presented through comparative studies showing its capability to outperform knowledge-based and data-driven models. This promising outcome led to the development of frameworks that can potentially incorporate KML for smart manufacturing applications such as Prognostics and Health Management (PHM) and DT, further supporting the usefulness of the proposed KML framework.

Keywords: Artificial intelligence; Smart manufacturing; Knowledge-embedded machine learning; Physics-based model; Data-driven model; Prognostics and health management; Digital twin; Inductive learning; Deductive learning; Abductive learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-022-01973-6

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