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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690522
• 关键词: Bringing; Industry; 4.0; manufacturing; life; 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（行业4.0）旨在通过内置虚拟化，监视，自我优化和控制功能建立智能工厂，以提高整个过程链的制造效率和产品质量。每台生产机器都将具有数字模拟模型，称为数字阴影'，并平行于预测，监视，表征，优化，控制和增强其性能。但是，当前的知识状态无法以有效且在工业上可行的方式实现这一雄心勃勃的目标。拟议的发现计划旨在通过开发新技术来帮助缩小这一差距，以支持行业4.0中设想的某些关键功能。这些包括：来自进程的制造数据，轨迹优化，通过高级实时控制的即时过程改进以及新型的机电设备的动态模型估计和模拟。************除了在每个主题中追求一个主题和整体方法的深入基础研究外，都将在每个主题中进行整体和整体的方法，从而在每个主题中进行良好的想法，从而促进了良好的想法。例如，数字阴影将与轨迹优化算法和高效的3D固体建模技术集成。因此，所提出的轨迹优化将直接将零件质量结果视为优化限制，而不是生产机器移动轴的传统使用的运动学（速度，加速度和混蛋）限制。预计这种直接方法可以使生成的轨迹的保守性大幅度降低，从而减少制造周期时间并实现所需的零件准确性要求。在这个发现计划中将积极地利用所有主题领域之间的类似协同作用，以取得成果，既有科学，又是工业创新且优越的结果。由Erkorkmaz教授创立的滑铁卢大学的Precision Controls Laboratory在机电设备，建模，识别，轨迹优化，精度控制和过程模拟方面具有非常成功的研究记录。通过较早的发现计划开发的许多核心思想都导致了加拿大内外的小型，中，大型公司的协作后续研发项目，并导致了已转移到行业的技术和解决方案，以及CNC Manufacturing的新商业产品。