Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics

Manufacturing work cell operations are typically complex, especially when considering machine tools or industrial robot systems. The execution of these manufacturing operations requires the integration of layers of hardware and software. The integration of monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) can aid manufacturers in maintaining the performance of machine tools and robot systems by providing intelligence to enhance maintenance and control strategies. 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 system. This limitation makes it imperative that the manufacturer understand the complexity of their system. For example, a typical robot system includes a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task. Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper focuses on research to decompose a work cell into a hierarchical structure to understand the physical and functional relationships among the system’s critical elements. These relationships will be leveraged to identify areas of risk, which would drive a manufacturer to implement PHM within specific areas. INTRODUCTION Advanced technology continues to emerge at a rapid pace as manufacturers, technology developers, and technology integrators further integrate operations technology with information technology to produce their own iterations of Smart Manufacturing. Smart Manufacturing is focused on bridging and connecting hardware, software, and data to increase operational efficiency, asset availability, and quality while decreasing unscheduled downtime and scrap [1-4]. The successful implementation of these paradigms will lead to greater efficiency within manufacturing operations enabling manufacturers to be more responsive to changing consumer demand and more resilient in the face of increased competition. Robot systems play a role in many manufacturing environments including automotive [5-7], electronics [8, 9], consumer packaged goods [10], and aerospace [11-13] manufacturing. Smart Manufacturing is having a positive impact on robotic operations occurring on the factory floor. More diverse systems, sub-systems, and components are being connected together which is leading to an increase in robot work cell capabilities. The American National Standards Institute, Inc. (ANSI) defines an industrial robot system to include a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task [14]. Examples of additional equipment, devices, and sensors include vision and proximity sensors (e.g., camera, laser), safety elements (e.g., light curtain), supervisory controller (e.g., Programmable Logic Controller (PLC)), and other supporting automation (e.g., conveyor belt). Figure 1 presents an example of a robot work cell including some of its key elements. The integration of these elements and the increase of ‘moving parts’ generate greater complexity, especially when considering robot-robot and human-robot operations. More complexity leads to more sources of error which can compromise the efficiency and quality of the process. Inclusion of condition monitoring, diagnostics, and/or prognostics (collectively known as prognostics and health management (PHM)) can provide greater intelligence of equipment and process health which can minimize unscheduled downtime, increase efficiency, and improve overall productivity.