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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587467
• 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，依赖计算机数控 (CNC) 加工和精密运动控制的行业的制造每年创造超过 $26B 的价值，其中包括航空航天 ($7B)、汽车 ($17B)、模具 ($0.5)。 B）、生物医学设备（$1.7B）以及机器人和自动化（$0.5B），这些领域通过创新可以实现的任何生产力提高或产品质量改进。技术，将对加拿大的竞争力和财富产生产生重大影响，这是该发现研究计划的目标，其目标是开发：i）新的运动控制策略，以及2）能够提高零件质量的最佳轨迹规划算法；以及在数控机床和机器人等多轴机器上进行的制造操作的生产率。 运动控制的研究目标是通过应用并发位置和振动控制来提高生产机器的进给驱动器（即移动轴）的高速定位精度和动态刚度，从而提高可用的带宽（即响应频率范围）。用于跟踪快速运动命令并抑制加工和摩擦引起的干扰，从而提高零件精度和质量。然而，随着机器操作条件的变化，提高高移动速率下的定位精度也能提高生产率。由于姿势、零件装载/卸载或零件磨损，进给驱动器的动态响应也会发生变化，因此，在存在以下情况时，运动控制算法保持其稳定性（为了安全）和性能（为了质量保证）至关重要。拟议的研究将研究能够处理这种变化的新的稳健和自适应控制技术，同时对上述制造领域中使用的生产机器实现可靠和切实的质量改进。这些技术将建立在申请人先前已经取得的成果的基础上。进给精度和刚度提高 40-50%推动工业中使用的最先进的技术。 第二个重点是沿着 3 轴和 5 轴刀具路径的新进给率（即刀具行进）优化算法，以便在机器和制造过程的物理限制内最小化制造周期时间。这种开发是一个复杂且非线性的过程。典型的自由曲面加工刀具路径可能包含数十万个曲线段，需要动态或以高效的方式离线规划时间最佳进给率。周期时间短，但其计算复杂性阻碍了工业实施；另一方面，工业控制器应用过度简化的假设，产生次优结果和保守的周期时间。 ，目前正在加拿大制造的 CNC 中进行商业化，该发现计划将研究更新且可能更强大的算法，能够通过文献中最先进的轨迹优化技术以更低的计算成本进一步缩短周期时间。或行业。 该发现计划将支持培养2名博士生、2名硕士生和5名本科生，他们将在高科技制造业的两个重要问题上进行基础研究，这一核心研究的工业传播和应用也将有助于培养更多的高素质人才。 ，从而证明了通过这一发现研究计划在技术创造和培训方面取得的倍增效应。