IMPLEMENTING A NOVEL FRAMEWORK TO CREATE TACIT KNOWLEDGE MODELS TO SUPPORT HUMAN- ROBOT INTERACTIONS

While it is not only important to synthesize instruments, controls, and robotics, it is also essential to connect these elements to people to achieve the future of automation. Whether in an operating room with surgical robots or in an earthquake disaster zone where an operator is aided by search and rescue drones, interaction between machines and humans is becoming central to increasing productivity. Industry 4.0 trends such as Internet of Things (IoT) and digital manufacturing are the early adopters of human-machine interfaces that support manufacturing automation. Such models must consider various aspects of process implementation such as explicit, implicit, and tacit knowledge to properly mimic a human's performance. However, most inquiries in this field use expressed information instead of tacit knowledge due to an unfulfilled need for an industrial tacit knowledge framework. Tacit knowledge is difficult to learn and transfer if an operator's logic is never revealed. In response, this research provides a knowledge model to structure, categorize, and reuse tacit knowledge for advanced manufacturing operations. The model is implemented in a human-robot interaction by capturing valuable experiences using digital tools such as Tecnomatix for further reuse in a variety of industrial applications.