With technological developments on the rise, the manufacturing industry acknowledges that it is not only important to integrate instruments, controls, and robots, but that it is also essential to connect these elements to people in order to achieve future automation. Whether in an operating room where a surgeon uses machines to save lives, or in an earthquake disaster zone where an operator is aided by robots in the rescue of affected people; interaction between machine and human is becoming increasingly central to achieving successful outcomes. Industry 4.0 trends such as digital manufacturing and the Internet of Things (IoT) are the early adopters of human-machine interfaces that support manufacturing automation and enrich our understanding of human-machine integration. Many researchers are exploring these new and emerging technologies to develop a novel knowledge model for interconnectivity between humans and machines. These knowledge models are integral in creating collaborative robots that can autonomously interact with floor engineers in a manufacturing context without human intervention. Such knowledge models must consider various aspects of knowledge such as explicit, implicit, and tacit knowledge to properly mimic a human’s performance. However, most research work in this field focused on explicit knowledge rather than tacit knowledge due to a lack of tacit knowledge modeling. Tacit knowledge is difficult to learn\\transfer if an operator’s logic is never revealed. To fill this gap, this research proposal provides a knowledge model to structure, categorize, and reuse tacit knowledge for advanced manufacturing operations. The present research uses digital manufacturing tools to examine the collaboration between humans and machines and how the connecting instruments and controls can be best designed to augment human-machine collaboration in the manufacturing industry, which is the Next Generation of Automation Systems (NGAS). For instance, the knowledge model uses patterns to obtain a deep understanding and analysis of human behavior (know-how) to capture, evaluate, and reuse a tacit knowledge against robot operation. The model will then transfer the necessary knowledge for collaborative robots to function autonomously. The proposed model demonstrates how to increase productivity to fulfill the need to adopt industry 4.0 trends in manufacturing technologies. If successful, the new manufacturing model will integrate not only people, instruments, controls and robots, but will also provide guidance in design manufacturability for advanced manufacturing systems. The main objective of this research is to develop a model to manage, and reuse tacit knowledge for NGAS. Doing so will facilitate the collaboration and increased human-machine efficiency.


Implementing a Novel Framework to Create Tacit Knowledge Models to Support Human-Robot Interactions

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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