A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing
The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After ap...
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| Format: | Article |
| Language: | English |
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Asociación de Directivos Superiores de Administración, Negocios o Empresariales de Chile A.G. (ASFAE)
2023-12-01
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| Series: | Multidisciplinary Business Review |
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| Online Access: | https://journalmbr.net/index.php/mbr/article/view/7142 |
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| _version_ | 1825208821553823744 |
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| author | Frederico de Oliveira Santos Ivanete Schneider Hahn |
| author_facet | Frederico de Oliveira Santos Ivanete Schneider Hahn |
| author_sort | Frederico de Oliveira Santos |
| collection | DOAJ |
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The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing.
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| format | Article |
| id | doaj-art-b52d19c965a64e53b84a70d57a4303a5 |
| institution | Kabale University |
| issn | 0718-400X 0718-3992 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Asociación de Directivos Superiores de Administración, Negocios o Empresariales de Chile A.G. (ASFAE) |
| record_format | Article |
| series | Multidisciplinary Business Review |
| spelling | doaj-art-b52d19c965a64e53b84a70d57a4303a52025-02-06T21:39:37ZengAsociación de Directivos Superiores de Administración, Negocios o Empresariales de Chile A.G. (ASFAE)Multidisciplinary Business Review0718-400X0718-39922023-12-01162A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturingFrederico de Oliveira Santos0Ivanete Schneider Hahn1Instituto Federal Catarinense (IFC). VideiraUniversidade Alto Vale do Rio do Peixe The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing. https://journalmbr.net/index.php/mbr/article/view/7142Industry 4.0.Cyber-physical systemMachine learningOperationsTechnology |
| spellingShingle | Frederico de Oliveira Santos Ivanete Schneider Hahn A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing Multidisciplinary Business Review Industry 4.0. Cyber-physical system Machine learning Operations Technology |
| title | A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing |
| title_full | A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing |
| title_fullStr | A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing |
| title_full_unstemmed | A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing |
| title_short | A systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing |
| title_sort | systematic literature review and taxonomy proposition of machine learning techniques in smart manufacturing |
| topic | Industry 4.0. Cyber-physical system Machine learning Operations Technology |
| url | https://journalmbr.net/index.php/mbr/article/view/7142 |
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