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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650265
• 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，每年通过依赖计算机数控（CNC）加工和精密运动控制的制造业创造的价值超过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）。这些部门通过创新新技术实现的任何生产力提高或产品质量改进，都将对加拿大的竞争力和财富产生重大影响。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）最佳轨迹规划算法;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。运动控制的研究目标是提高进给驱动器的高速定位精度和动态刚度（即，移动轴）的生产机器，通过应用并发的位置和振动控制。这增强了带宽（即，响应频率范围），可用于跟踪快速运动命令，并排除由于加工和摩擦引起的干扰，从而提高零件精度和质量。在高横移速度下提高定位精度也可以提高生产率。然而，当机器的操作条件因姿态、零件装载/卸载或部件磨损而发生变化时，进给驱动器的动态响应也会发生变化。因此，在存在这种变化的情况下，运动控制算法保持其稳定性（安全性）和性能（质量保证）至关重要。拟议的研究将探讨新的鲁棒性和自适应控制技术，能够处理这种变化，同时实现可靠的和有形的质量改进的生产机器在上述制造部门。这些将建立在申请人的早期工作的基础上，该工作已经在工业中使用的最先进技术的基础上实现了40-50%的进给驱动精度和刚度改进。第二个重点是开发新的进给速度（即，刀具行进）优化算法，以便在机床和制造过程的物理限制内最大限度地缩短制造周期时间。这是一个复杂的非线性问题。典型的自由曲面加工刀具路径可能包含数十万个弯曲段，对于这些弯曲段，需要实时规划或以有效的方式离线规划时间最优进给速率。文献中的精心设计的算法产生短的周期时间，但它们的计算复杂性使它们无法实现工业化。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。申请人最近开发了一种高效且有效的进给优化算法，该算法目前正在西班牙制造的CNC内商业化。该发现计划将研究更新的和潜在的更强大的算法，能够以更低的计算成本实现进一步的周期时间缩短，超过文献或行业中最先进的轨迹优化技术。该发现计划将支持2名博士，2名硕士和5名本科生的培训，他们将在解决高科技制造中的两个重要问题方面进行基础研究。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，从而证明通过这一发现研究计划实现的技术创造和培训方面的倍增效应。