Structural Motion Control and Optimal Trajectory Planning for High-Productivity Manufacturing

高生产率制造的结构运动控制和最佳轨迹规划

• 批准号: RGPIN-2014-03879
• 负责人: Erkorkmaz, Kaan
• 金额: $ 2.84万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=610698
• 关键词: Structural; Motion; Control; Optimal; Trajectory

In Canada, over $26B of value creation takes place annually through manufacturing in industries that rely on Computer Numerically Controlled (CNC) machining and precision motion controls. These include aerospace ($7B), automotive ($17B), dies and moulds ($0.5B), biomedical devices ($1.7B), and robotics and automation ($0.5B). Any productivity increase or product quality improvement achievable in these sectors, by innovating new technologies, would have significant impact on Canada’s competitiveness and wealth generation. This is the objective of this discovery research program, which targets the development of: i) new motion control strategies, and 2) optimal trajectory planning algorithms; capable of enhancing the part quality and productivity of manufacturing operations carried out on multi-axis machines, such as CNC machine tools and robots. The research on motion controls targets improvement of the high-speed positioning accuracy and dynamic rigidity of feed drives (i.e., moving axes) of production machines, by applying concurrent position and vibration control. This enhances the bandwidth (i.e., responsive frequency range) available for tracking rapid motion commands and rejecting disturbances due to machining and friction, leading to improved part accuracy and quality. Enhancing positioning accuracy at high traverse rates also enables higher productivity. However, as a machine’s operating conditions change due to posture, part loading/unloading, or component wear, the dynamic response of the feed drives also changes. Hence, it is vital that the motion control algorithms retain their stability (for safety) and performance (for quality assurance), in the presence of such variations. The proposed research will investigate new robust and adaptive control techniques capable of dealing with such variability, while achieving reliable and tangible quality improvement on production machines employed in the mentioned manufacturing sectors. These will build upon the applicant’s earlier work, which has achieved 40-50% accuracy and stiffness improvement on feed drives over state-of-the-art techniques used in industry. The second thrust focuses on developing new feedrate (i.e., tool progression) optimization algorithms along 3- and 5-axis toolpaths, in order to minimize the manufacturing cycle time within the physical limits of the machine and manufacturing process. This is a complex and nonlinear problem. Typical freeform machining toolpaths may contain hundreds of thousands of curved segments, for which a time-optimal feedrate needs to be planned on-the-fly, or in an efficient manner off-line. Elaborate algorithms in literature yield short cycle times, but their computational complexity prevents them from industrial implementation. Industrial controllers, on the other hand, apply over-simplifying assumptions that yield sub-optimal results and conservative cycle times. The applicant has recently developed an efficient and effective feed optimization algorithm, which is currently being commercialized inside a Canadian-built CNC. This discovery program will investigate newer and potentially more powerful algorithms, capable of achieving further cycle time reduction at lower computational cost, over state-of-the-art trajectory optimization techniques from literature or industry. This discovery program will support the training of 2 PhD, 2 MASc and 5 undergraduate students, who will conduct fundamental research in tackling two important problems in hi-tech manufacturing. Industrial dissemination and application of this core research will also help train additional highly qualified personnel, thus demonstrating a multiplying effect in terms of technology creation and training achieved through this discovery research program.

在加拿大，依靠计算机数控（CNC）加工和精密运动控制的行业每年创造的价值超过260亿美元。其中包括航空航天（70亿美元）、汽车（170亿美元）、模具（5亿美元）、生物医学设备（17亿美元）以及机器人和自动化（5亿美元）。通过创新新技术，在这些部门实现的任何生产率提高或产品质量改善都将对加拿大的竞争力和财富产生重大影响。这是本发现研究计划的目标，其目标是开发：i)新的运动控制策略，和2)最优轨迹规划算法；能够提高在多轴机床（如CNC机床和机器人）上进行的制造操作的零件质量和生产率。