Bringing Industry 4.0 manufacturing to life: Digital shadows, optimized trajectories, structural controls, and advanced mechatronics

将工业 4.0 制造带入生活：数字阴影、优化轨迹、结构控制和先进机电一体化

• 批准号: RGPIN-2019-05334
• 负责人: Erkorkmaz, Kaan
• 金额: $ 4.01万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715470
• 关键词: Bringing; Industry; 4.0; manufacturing; life

The 4th Industrial Revolution (Industry 4.0) aims to establish smart factories with built-in virtualization, monitoring, self-optimization, and control functionalities, to achieve improved manufacturing efficiency and product quality across complete process chains. Each production machine would have a digital simulation model, called a digital shadow', running in parallel to predict, monitor, characterize, optimize, control, and enhance its performance. The current state of knowledge, however, falls short of being able to achieve such an ambitious set of goals in an effective and industrially viable manner. The proposed Discovery program aims to help close this gap, by developing new technologies in support of certain critical functionalities envisioned in Industry 4.0. These include: dynamic model estimation and simulation from in-process manufacturing data, trajectory optimization, on-the-fly process improvement through advanced real-time controls, and novel mechatronic devices to boost manufacturing productivity. In addition to pursuing deep fundamental research in each of the themes, a holistic and integrative approach is proposed in which the progress and results from every theme will enable far-reaching new ideas to be pursued in the complementary themes. For example, digital shadows will be integrated with trajectory optimization algorithms and with highly efficient 3D solid modeling techniques. Thus, the proposed trajectory optimization will directly consider part quality outcomes as the optimization constraints, rather than the traditionally used kinematic (velocity, acceleration, and jerk) limits of the production machine's moving axes. This direct approach is expected to enable a dramatic reduction in the conservativeness of the generated trajectories, thereby reducing the manufacturing cycle time and enabling the desired part accuracy requirements to be retained. Similar synergies between all theme areas will be actively capitalized upon within this Discovery program, in order to achieve results that are both scientifically new and also industrially innovative and superior. Overall, this Discovery program targets bold and ambitious new steps towards transforming the state of knowledge in virtual and intelligent manufacturing to the next level, while simultaneously training new highly qualified personnel at postdoctoral, PhD, master's, and undergraduate levels. The University of Waterloo's Precision Controls Laboratory, founded by Prof. Erkorkmaz, has a highly successful record of research in mechatronic devices, modeling, identification, trajectory optimization, precision controls, and process simulation. Many core ideas developed through earlier Discovery programs have led to collaborative follow-up R&D projects with small, medium, and large-scale companies within and outside Canada, and have resulted in technologies and solutions that have been transferred to industry, as well as new commercial products in CNC manufacturing.

第四次工业革命（工业4.0）旨在建立具有内置虚拟化、监控、自优化和控制功能的智能工厂，以提高整个过程链的制造效率和产品质量。每台生产机器都有一个数字仿真模型，称为“数字阴影”，并行运行，以预测，监控，表征，优化，控制和提高其性能。然而，目前的知识水平福尔斯不足以以有效和工业上可行的方式实现这样一组雄心勃勃的目标。拟议的Discovery计划旨在通过开发新技术来支持工业4.0中设想的某些关键功能，从而帮助缩小这一差距。其中包括：通过先进的实时控制和新颖的机电一体化设备，从过程中的制造数据进行动态模型估计和仿真、轨迹优化、动态过程改进，以提高制造生产率。 除了在每个主题中进行深入的基础研究外，还提出了一种整体和综合的方法，其中每个主题的进展和结果将使互补主题中具有深远意义的新思想得以实现。例如，数字阴影将与轨迹优化算法和高效的3D实体建模技术相结合。因此，所提出的轨迹优化将直接考虑零件质量结果作为优化约束，而不是传统上使用的生产机器的移动轴的运动学（速度、加速度和加加速度）限制。这种直接的方法预计能够大大减少所生成的轨迹的保守性，从而减少制造周期时间，并能够保持所需的零件精度要求。所有主题领域之间的类似协同作用将在本发现计划中积极利用，以实现科学上的新成果，以及工业上的创新和上级成果。 总的来说，这个发现计划的目标是大胆而雄心勃勃的新步骤，将虚拟和智能制造的知识状态转变到一个新的水平，同时培养博士后，博士，硕士和本科水平的新的高素质人才。由Erkorkmaz教授创立的滑铁卢大学精密控制实验室在机电设备、建模、识别、轨迹优化、精密控制和过程仿真方面有着非常成功的研究记录。通过早期的发现计划开发的许多核心想法已经导致与加拿大国内外的小型，中型和大型公司合作的后续研发项目，并导致技术和解决方案已经转移到工业，以及CNC制造中的新商业产品。