Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design

Abstract

The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet-of-things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low-cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon-based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.

Observational process data analytics using causal inference

“…Even though, this example is from a steel manufacturing plant, the challenge of extracting causal information from observational data are universal 64 . Due to recipe designs, feedforward adjustment, and human intervention, noncausal correlations are very common in process data 6,25,65 . Without the scrutiny of causal inference, any found statistical association may lead to a faulty attempt at process improvement.…”

“…64 Due to recipe designs, feedforward adjustment, and human intervention, noncausal correlations are very common in process data. 6,25,65 Without the scrutiny of causal inference, any found statistical association may lead to a faulty attempt at process improvement.…”

“…With the rapid advancement of the Industrial Internet of Things, artificial intelligence, and cloud computing, the manufacturing industry is moving toward a more integrated and data‐driven paradigm known as smart manufacturing 3–5 . In smart manufacturing systems, a massive amount of process data are generated, providing a great opportunity for process improvement 6,7 . As a result, process data analytics plays an increasingly important role in smart manufacturing, and many successful applications have been reported 8–10 …”