Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

Abstract

In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

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

“…In the future, more use cases (other than 3D printing and assembly lines [39]) with different kinds of interfaces and human-machine interactions might be considered to expand the generality of the proposed method. In terms of the functionalities, interactive Augmented Reality (AR) training will be developed upon the proposed framework by leveraging the finger detection and text recognition algorithm through the AR goggles.…”

Research on the Evaluation System for Training Field Engineers in the Manufacturing Industry of Vocational Education Under the Background of New Industrialization

“…In the context of new industrialization, vocational colleges at the undergraduate level have more advantages in cultivating on-site engineers in the advanced manufacturing industry compared to colleges at the intermediate and vocational levels. They are more in line with the needs of the new industrialization process driven by technological innovation, and can provide strong talent support for the transformation and upgrading of the manufacturing industry towards high-end, digital, intelligent, and green directions, thereby promoting the effective improvement of quality and reasonable growth of quantity in the manufacturing industry Donovan et al (2022), Lampon & Rivo-Lopez (2022), Sarbu (2022).…”