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Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers
With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for im...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414792/ https://www.ncbi.nlm.nih.gov/pubmed/36016006 http://dx.doi.org/10.3390/s22166246 |
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author | Li, Chen Bian, Shijie Wu, Tongzi Donovan, Richard P. Li, Bingbing |
author_facet | Li, Chen Bian, Shijie Wu, Tongzi Donovan, Richard P. Li, Bingbing |
author_sort | Li, Chen |
collection | PubMed |
description | With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through the deployment of consumer-grade electronics and a novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM) and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU). |
format | Online Article Text |
id | pubmed-9414792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94147922022-08-27 Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers Li, Chen Bian, Shijie Wu, Tongzi Donovan, Richard P. Li, Bingbing Sensors (Basel) Article With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes. The proposed approach seeks to reduce the cost and complexity of smart manufacturing deployment for SMMs through the deployment of consumer-grade electronics and a novel AI development methodology. The proposed system, AI-assisted Machine Supervision (AIMS), provides SMMs with two major subsystems: direct machine monitoring (DMM) and human-machine interaction monitoring (HIM). The AIMS system was evaluated and validated with a case study in 3D printing through the affordable AI accelerator solution of the vision processing unit (VPU). MDPI 2022-08-19 /pmc/articles/PMC9414792/ /pubmed/36016006 http://dx.doi.org/10.3390/s22166246 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Chen Bian, Shijie Wu, Tongzi Donovan, Richard P. Li, Bingbing Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers |
title | Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers |
title_full | Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers |
title_fullStr | Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers |
title_full_unstemmed | Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers |
title_short | Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers |
title_sort | affordable artificial intelligence-assisted machine supervision system for the small and medium-sized manufacturers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414792/ https://www.ncbi.nlm.nih.gov/pubmed/36016006 http://dx.doi.org/10.3390/s22166246 |
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