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Industry 4.0 system concepts and machine learning for intelligent environment design


Advances in communication technology, computational modeling, and control algorithms have enabled the transformation of data into knowledge and control of machines and systems in real-time with high accuracy via smart sensory devices and wireless networks. Such systems are known as cyber-physical systems (CPS). Industry 4.0 seamlessly connects designers, manufacturers, and consumers to increase productivity, reliability and customer satisfaction through the integration of Internet of Things (IoT), Internet of Services (IoS), or cloud computing with cyber-physical systems. The proposed joint project will enable Dr. Sheng-Jen ("Tony") Hsieh from Texas A&M University and Dr. Roseli A.F. Romero from the University of São Paulo to exchange ideas and findings related to intelligent environment platform design. Both are currently working on projects related to Industry 4.0 concepts and architecture: Dr. Hsieh's project on cyber-enabled assembly system monitoring and intelligent prognostics, funded by Texas A&M and CONACyT, focuses on manufacturing environments; while Dr. Romero's project on architecture and applications for robotics in intelligent environments, funded by FAPESP, focuses on robotics in home environments. The methodologies resulting from Dr. Hsieh's research can be applied to Dr. Romero's research on intelligent integration of robotics within home environments. Similarly, Dr. Romero's research findings can illuminate Dr. Hsieh's efforts to use machine learning (ML) algorithms to construct cyber models of assembly system environments based on sensory data-specifically using multiple sensors with multi-attribute data. The goals of the proposed collaboration are to: 1) jointly develop ML algorithms and CPS that can be used to model and solve robot, human and machine system problems within an Industry 4.0 architecture; 2) disseminate research findings; 3) identify collaborators who can contribute to future research efforts; 4) gain synergy and support from industry; and 5) develop a joint proposal on ML and CPS for human-robot collaboration in smart home environments and within smart assembly systems. (AU)

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