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

Abstract

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).

Ethical perspective on AI hazards to humans: A review

“…Finally, the intelligence and automation in intelligent manufacturing may lead to a reduced understanding of the manufacturing process and product quality by humans. [ 30 ] With the continuous optimization and development of smart devices and algorithms, human intervention and supervision may decrease, making it more difficult to discover and solve product quality issues. This may affect a company reputation and consumer safety.…”

Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

“…With the current advancements in the Internet of Things, edge computing, big data, and other new-generation information technologies [ 1 , 2 , 3 , 4 ], as well as the demand for a personalized product and shorter delivery cycles, businesses are under intense competitive pressure, which necessitates the use of smart manufacturing to lower costs and boost efficiency [ 5 ]. In the machinery manufacturing sector, there is a huge number of offline equipment whose production data and processes need to be collected and monitored as traditional factories are upgraded to smart factories [ 6 ].…”