Traditional smart manufacturing emphasizes data-driven decision making for individual systems. In a smart manufacturing network, computation services are widely used to transform data streams to decisions in heterogeneous manufacturing conditions and integrate human expertise for cyber-human collaborative tasks in various contexts. These computation services can adaptively improve productivity, quality, and flexibility of manufacturing to personalization. Such computation services in a smart manufacturing network poses significant challenges in selection of computation pipelines with limited communication and computation resources. Motivated by these challenges, this presentation will mainly discuss how to recommend contextualized computation services and pipelines in a smart manufacturing network with different manufacturing processes. A recommender system-based framework is proposed to support efficient computation pipeline selection, considering the reliability and responsiveness of the ubiquitous communication and computation resources. Manufacturing case studies were performed to validate the proposed methodology.
Dr. Ran Jin is an associate professor and the Director of Laboratory of Data Science and Visualization at the Grado Department of Industrial and Systems Engineering at Virginia Tech. He received his Ph.D. degree in Industrial Engineering from Georgia Tech, Atlanta, two Master’s degrees in Industrial Engineering and in Statistics, both from the University of Michigan, Ann Arbor, and a bachelor degree in Electronic Engineering from Tsinghua University, Beijing. His research focuses on computation services in smart manufacturing, such as data fusion methods for synergistically modeling, monitoring and control of manufacturing processes and systems, and cognitive-based visualization for human-machine collaboration. He is currently serving as President of the Engineering Faculty Organization of Virginia Tech, Chair of the Quality, Statistics and Reliability Subdivision in INFORMS, and an Associate Editor for IISE Transactions, Focus Issue on Design and Manufacturing.