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Knowledge-embedded machine learning and its applications in smart manufacturing

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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.

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Abbreviations

AI:

Artificial Intelligence

BPFI:

Ball Pass Frequency at Inner

BPFO:

Ball Pass Frequency at Outer

CPS:

Cyber-Physical-Systems

CRM:

Customer Relation Management

DDML:

Data-Driven Machine Learning

DT:

Digital Twin

ERP:

Enterprise Resource Planning

IIoT:

Industrial Internet of Things

KML:

Knowledge-embedded Machine Learning

MSE:

Mean Square Error

PHM:

Prognostics and Health Management

RUL:

Remaining Useful Life

SM:

Smart Manufacturing

SMAI:

Smart Manufacturing AND Artificial Intelligence

SMAIK:

Smart Manufacturing AND Artificial Intelligence AND Knowledge

WIP:

Work-in-process

WoS:

Web of science

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Acknowledgements

The authors would like to thank Mr. Eric Bechhoefer (Green Power Monitoring Systems (GPMS) International, Inc., USA) for the detailed information on the bearing dataset, data acquisition system, and the related sampling rate.

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  1. Department of Computing & Intelligence, Institute of High Performance Computing, 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Singapore

    Farzam Farbiz, Mohd Salahuddin Habibullah, Brahim Hamadicharef, Tomasz Maszczyk & Saurabh Aggarwal

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  1. Farzam Farbiz
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Farbiz, F., Habibullah, M.S., Hamadicharef, B. et al. Knowledge-embedded machine learning and its applications in smart manufacturing. J Intell Manuf 34, 2889–2906 (2023). https://doi.org/10.1007/s10845-022-01973-6

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