HIERARCHICAL DECOMPOSITION OF A MANUFACTURING WORK CELL TO PROMOTE MONITORING, DIAGNOSTICS, AND PROGNOSTICS
Brian A. Weiss, Guixiu Qiao
Manufacturing operations are typically complex, especially when considering industrial robot systems. The execution of robot-driven tasks requires the integration of multiple layers of hardware and software. The development and integration of monitoring, diagnostic, and prognostic (collectively known as prognostics and health management (PHM)) technologies can aid manufacturers in maintaining the performance of robot systems by providing intelligence to enhance maintenance and control strategies. If appropriately designed and integrated, PHM can improve asset availability, product quality, and overall productivity. It is unlikely that a manufacturer has the capability to implement PHM in every element of their robot system. This limitation makes it imperative that the manufacturer understand the complexity of their robot system, especially the influences that each element has on one another. Typical robot systems include one or more robot arm(s), controller(s), end-effector(s), sensor(s), and safety system(s). Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper will focus on research that is aimed at hierarchically structuring the complex robot system work cell to promote an understanding of the physical and functional relationships among the system's critical elements. These relationships will be leveraged to support the identification of areas of risk, which in turn would drive a manufacturer to implement PHM within specific areas.
ASME 2017 12th International Manufacturing Science and Engineering Conference (MSEC2017)
and Qiao, G.
HIERARCHICAL DECOMPOSITION OF A MANUFACTURING WORK CELL TO PROMOTE MONITORING, DIAGNOSTICS, AND PROGNOSTICS, ASME 2017 12th International Manufacturing Science and Engineering Conference (MSEC2017), Los Angeles, CA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922582
(Accessed December 10, 2023)