Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 16; p. 6246
• Main Authors: Li, Chen, Bian, Shijie, Wu, Tongzi, Donovan, Richard P., Li, Bingbing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 19.08.2022; MDPI
• Subjects: 3D printing; Accuracy; affordable AI accelerator; Artificial intelligence; Automation; Complexity; computer vision; Computers; Data acquisition systems; Human error; Industrial production; Internet of Things; IoT; Machine learning; machine supervision; Machinery; Magneto-electric machines; Manufacturers; Manufacturing; Manufacturing processes; Monitoring; Productivity; Semantics; Small & medium sized enterprises-SME; smart manufacturing; Subsystems; Supervision; Supervisors; Technology application; Upgrading; Workers; Workflow; United States
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22166246

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).