Maintaining manufacturing process performance is becoming more challenging as 1) existing equipment becomes older and worn; 2) process complexity increases with the integration of advanced and emerging technologies; 3) manufacturers build in-process reconfigurability to support greater variability and product customization; and 4) manufacturers require their processes to maintain higher accuracy and precision. Manufacturers require innovative measurement science to assess and trust the data infrastructure and analytics critical for health monitoring capabilities of existing and new manufacturing equipment and processes. Manufacturers need advanced sensing, data infrastructure, and analytics to optimize production, to know when and why production performance thresholds are exceeded (diagnostics) or will be exceeded (prognostics). Ultimately, manufacturers want to make informed decisions to both minimize downtime and maintain or maximize production quality. Making informed decisions to enhance factory floor-level operations is promoted through the development of standards and guidelines to effectively design, implement, verify, and validate monitoring, diagnostic, and prognostic technologies. The requirement for standards and guidelines becomes more critical as manufacturers collect larger, more diverse volumes of data, the health of their equipment changes, and their processes evolve to meet changing consumer demand. NIST recognizes these challenges and needs within the manufacturing community and seeks to address them in the MDP4MO project. Specifically, this project continues the existing research in developing measurement science to promote monitoring, diagnostic, and prognostic technologies within manufacturing robot workcells and machine tools; promotes greater technology transfer of the NIST-developed methods and tools; furthers NIST’s critical role in building up standards and guidelines to support the manufacturing community; and explores new research for developing on-machine measurement science to enhance knowledge and trust in the performance of sensing and analytics in support of manufacturing maintenance.
Objective - Develop and deploy measurement science to promote the implementation, verification, and validation of advanced monitoring, diagnostic, and prognostic technologies to minimize unplanned downtime and optimize planned downtime in manufacturing operations.
What is the technical idea?
Manufacturers need standards and guidelines to effectively enable, integrate, verify, and validate monitoring, diagnostic, and prognostic technologies to enhance factory floor-level decision-making. NIST research will contribute to the manufacturing community’s ability to advance the state-of-the-art in these specific technologies including a focus on four key activities throughout the life of the project. The continued execution of each activity should both enhance NIST research efforts and increase the manufacturing community’s awareness of emergent verification and validation techniques.
Enhance Diagnostics and Prognostics: Develop new diagnostic and prognostic measurement technologies (both hardware and software), methods, and metrics for manufacturing work cell systems, particularly robotics and machine tools. Presently, manufacturers have limited options when it comes to integrating reliable diagnostic and prognostic technologies to assess production performance and accuracy, because many current methods are typically offline and expensive in equipment and time. The options become smaller in terms of those technologies that have been independently verified and validated. Manufacturers desire smart machine tools and robotics with online abilities to assess their own health, so that production isn’t halted but enhanced. Enhancing diagnostic and prognostic measurement capabilities will a) support industry’s development of emergent manufacturing capabilities, b) offer end-users the necessary means to verify and validate the chosen technologies, and c) provide greater completeness of NIST’s measurement science contributions.
Perform Industry Pilots: Emerging test methods, performance metrics, and measurement techniques will be conducted with key industry partners to further verify and validate NIST-developed measurement science. The impact of these pilot activities should a) enhance the quality and relevance of NIST research efforts, b) offer insight as to the current PHM-capability levels of industry technologies, and c) implementation of NIST-developed measurement science to generate datasets and support ongoing research. Project personnel are also looking to consortia, including building up their own, to focus on larger collaborative industry pilots for online monitoring, diagnostics, and prognostics.
Convene Industry-focused Forums: Building upon the success of the 2019 Measurement and Evaluation for Prognostics and Health Management for Manufacturing Operations (ME4PHM) Workshop, additional public events and meetings will be organized and executed. It is the hope to continue the ME4PHM Workshop to bring industry together with academia and government to discuss the latest trends and needs in terms of measuring the performance of monitoring, diagnostic, and prognostic technologies.
Dissemination through Standards and Guidelines: In addition to publicly-available documents and reference datasets, NIST is aiming to disseminate its measurement science in the development of standards and guidelines. NIST personnel have spurred the formation of ASME subcommittee focused on Advanced Monitoring, Diagnostics, and Prognostics for Manufacturing Operations. Several leadership positions are held by NIST researchers and the subcommittee is actively developing guidance documentation to support the manufacturing community’s ability to design and deploy monitoring, diagnostic, and prognostic technologies across their enterprises.
What is the research plan?
Workcells are becoming a greater focal point as manufacturing takes a more distributed and decentralized approach. Workcells are being asked to perform a wide range of operations – robotic arms are becoming more prolific, especially in small to medium-sized manufacturing, while machine tools are also being pushed to their limits for higher accuracies with near 24/7 production. Workcells are becoming more susceptible to faults and failures as 1) existing equipment and processes become older and worn; 2) process complexity increases as advances in existing technologies and emerging technologies (sensors, actuators, etc.) are integrated; and 3) manufacturers build in process reconfigurability to support greater variability and product customization. Changing operational profiles (e.g., a robot moving a 5-kg box and then being asked to move a 15-kg box) will affect the degradation of the workcell and its constituent elements. Through identification of current health and early signs of problems in robotic arms and machine tools, manufacturers will have the information they need to trust their maintenance plans and optimize production.
This project continues the existing research in developing measurement science to promote monitoring, diagnostic, and prognostic technologies within manufacturing workcells, specifically for robotic arms and machine tools; promotes greater technology transfer of the NIST-developed methods and tools; furthers NIST’s critical role in building up standards and guidelines to support the manufacturing community; and explores new research in terms of verifying and validating emerging, industry-driven monitoring, diagnostic, and prognostic capabilities.
The output of this effort will enable manufacturers to make more strategic and cost-effective decisions to enhance their maintenance and control strategies. This will be accomplished by providing manufacturers with measurement science to support the verification and validation of advanced monitoring, diagnostic, and prognostic capabilities; and determine the appropriate performance metrics to enhance their ability to diagnose and predict faults and failures. Activities will include new research, continuation of existing research, and technology transfer through interactions with industry partners and strong collaboration with standards development organizations (e.g., ASME).