TY - JOUR

T1 - Machine Learning in Manufacturing towards Industry 4.0

T2 - From ‘For Now’ to ‘Four-Know’

AU - Chen, Tingting

AU - Sampath, Vignesh

AU - May, Marvin Carl

AU - Shan, Shuo

AU - Jorg, Oliver Jonas

AU - Aguilar Martín, Juan José

AU - Stamer, Florian

AU - Fantoni, Gualtiero

AU - Tosello, Guido

AU - Calaon, Matteo

N1 - Publisher Copyright: © 2023 by the authors.

PY - 2023/2

Y1 - 2023/2

N2 - While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

AB - While attracting increasing research attention in science and technology, Machine Learning (ML) is playing a critical role in the digitalization of manufacturing operations towards Industry 4.0. Recently, ML has been applied in several fields of production engineering to solve a variety of tasks with different levels of complexity and performance. However, in spite of the enormous number of ML use cases, there is no guidance or standard for developing ML solutions from ideation to deployment. This paper aims to address this problem by proposing an ML application roadmap for the manufacturing industry based on the state-of-the-art published research on the topic. First, this paper presents two dimensions for formulating ML tasks, namely, ’Four-Know’ (Know-what, Know-why, Know-when, Know-how) and ’Four-Level’ (Product, Process, Machine, System). These are used to analyze ML development trends in manufacturing. Then, the paper provides an implementation pipeline starting from the very early stages of ML solution development and summarizes the available ML methods, including supervised learning methods, semi-supervised methods, unsupervised methods, and reinforcement methods, along with their typical applications. Finally, the paper discusses the current challenges during ML applications and provides an outline of possible directions for future developments.

KW - artificial intelligence

KW - digitization

KW - Industry 4.0

KW - machine learning

KW - manufacturing

KW - smart manufacturing

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85147912968&partnerID=8YFLogxK

U2 - 10.3390/app13031903

DO - 10.3390/app13031903

M3 - Journal articles

AN - SCOPUS:85147912968

VL - 13

JO - Applied Sciences (Switzerland)

JF - Applied Sciences (Switzerland)

SN - 2076-3417

IS - 3

M1 - 1903

ER -