Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’

Abstract

first_pagesettingsOrder Article Reprints Open AccessArticle Machine Learning in Manufacturing towards Industry 4.0: From ‘For Now’ to ‘Four-Know’ by Tingting Chen 1,*ORCID,Vignesh Sampath 2ORCID,Marvin Carl May 3ORCID,Shuo Shan 1ORCID,Oliver Jonas Jorg 4ORCID,Juan José Aguilar Martín 5ORCID,Florian Stamer 3ORCID,Gualtiero Fantoni 4ORCID,Guido Tosello 1ORCID andMatteo Calaon 1ORCID 1 Department of Civil and Mechanical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark 2 Autonomous and Intelligent Systems Unit, Tekniker, Member of Basque Research and Technology Alliance, 20600 Eibar, Spain 3 wbk Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany 4 Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy 5 Department of Design and Manufacturing Engineering, School of Engineering and Architecture, University of Zarazoga, 50009 Zaragoza, Spain * Author to whom correspondence should be addressed. Appl. Sci. 2023, 13(3), 1903; https://doi.org/10.3390/app13031903 (registering DOI) Received: 23 November 2022 / Revised: 18 January 2023 / Accepted: 27 January 2023 / Published: 1 February 2023 (This article belongs to the Special Issue Advances in Sustainable and Digitalized Factories: Manufacturing, Measuring Technologies and Systems) Download Browse Figures Versions Notes Abstract 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