A survey of the advancing use and development of machine learning in smart manufacturing. (July 2018)

• Record Type: Journal Article
• Authors: Sharp, Michael; Ak, Ronay; Hedberg, Thomas
• Is Part Of: Journal of manufacturing systems. Volume 48(2018)Part C
• Page Start: 170
• Page End: 179
• Publication Date: 2018-07
• Subjects: Machine learning -- Industrie 4.0 -- Smart manufacturing; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2018.02.004
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Presents results quantifying use of machine learning across domains of the product life cycle. Used computer-aided and NLP methodologies to assess ML applications from a life cycle viewpoint. Generic solutions for applying ML to the product life cycle are absent in the literature. Abstract: Machine learning (ML) (a subset of artificial intelligence that focuses on autonomous computer knowledge gain) is actively being used across many domains, such as entertainment, commerce, and increasingly in industrial settings. The wide applicability and low barriers for development of these algorithms are allowing for innovations, once thought unattainable, to be realized in an ever more digital world. As these innovations continue across industries, the manufacturing industry has also begun to gain benefits. With the current push for Smart Manufacturing and Industrie 4.0, ML for manufacturing is experiencing unprecedented levels of interest; but how much is industry actually using these highly-publicized techniques? This paper sorts through a decade of manufacturing publications to quantify the amount of effort being put towards advancing ML in manufacturing. This work identifies both prominent areas of ML use, and popular algorithms. This also allows us to highlight any gaps, or areas where ML could play a vital role. To maximize the search space utilization of this investigation, ML based Natural Language Processing (NLP) techniques were employed to rapidly sort through a vast corpus of engineering documents to identify key areas of research and application, as well as uncover documents most pertinent to this survey. The salient outcome of this research is the presentation of current focus areas and gaps in ML applications to the manufacturing industry, with particular emphasis on cross domain knowledge utilization. A full detailing of methods and findings is presented.