Coordination Funds

协调基金

• 批准号: 498948117
• 负责人: Professor Dr.-Ing. Jürgen Beyerer
• 金额: --
• 依托单位: Institut für Anthropomatik und Robotik (IAR)
• 依托单位国家: 德国
• 项目类别: Research Units
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/498948117?language=en
• 关键词: Coordination; Funds

New manufacturing processes typically involve a time-consuming development phase at the laboratory scale and a further ramp-up phase at an industrial scale before they are capable of producing with the required quality, productivity and cost-effectiveness. This is especially the case when new materials and new processes are employed, when the manufacturing process and its sub-processes have a vast number of parameters and high-dimensional state-spaces, and when only poor dynamical process models are available at the outset. From an abstract point of view, a huge and complex optimisation problem must be solved in terms of the process structure, parameter values, control strategies, and process instrumentation (sensors and actuators). Traditionally, experts accomplish this via extensive experiments based on their knowledge, experience, and intuition. But acquiring experimental data from manufacturing processes is associated with large costs due to, e.g., downtimes of expensive infrastructure, resource consumption for non-marketable output and costs for destructive testing of output products in dedicated laboratories.Hypothesis: We believe that the systematic use of AI leads to significant improvements in the maturation of manufacturing processes, both in terms of the solution quality and the resource investment needed to arrive at a pursued maturity. However, the high complexity and stochastic nature of real-world manufacturing processes, coupled with a limited number of expensive real-world samples, requires methodological improvements also in the field of artificial intelligence itself.Scientific and technical approach: First, we strive to improve the state-of-the-art in data-driven model identification, optimisation and reinforcement learning with the goal of minimizing the number of real-world samples required for manufacturing process optimisation. This is achieved (a) by the deep integration of data-driven model identification with prior process knowledge and engineering models, as well as (b) the targeted online design of experiments to create samples with high expected information gain.Secondly, we create a broadly applicable methodology for the AI-assisted transformation of an initially immature manufacturing process towards an applicable and mature one. Ingredients are inter alia a temporary systematic over-instrumentation, a process modularisation, and an iterative maturation procedure. This is a step towards a professionalization of AI in the sense of an engineering discipline with procedures and tools that can scale to large and heterogeneous teams.Relevance: A much faster transformation of an immature manufacturing process into a mature process would allow for a much shorter time to market for new products, and would therefore provide competitive advantages for companies. Although difficult to predict, we believe that by utilising the intended methodology, a time and cost reduction in process development up to 50% can be achieved.

新的制造工艺通常包括实验室规模的耗时开发阶段和工业规模的进一步提升阶段，然后才能以所需的质量、生产力和成本效益生产。当采用新材料和新工艺时，当制造过程及其子过程具有大量参数和高维状态空间时，以及当一开始只有较差的动态过程模型可用时，情况尤其如此。从抽象的角度来看，在过程结构、参数值、控制策略和过程仪表（传感器和执行器）方面，必须解决一个巨大而复杂的优化问题。传统上，专家们通过基于他们的知识、经验和直觉的大量实验来实现这一目标。但是，从制造过程中获取实验数据伴随着巨大的成本，例如，昂贵的基础设施的停机时间，非市场产品的资源消耗以及在专用实验室对输出产品进行破坏性检测的成本。假设：我们认为，系统地使用人工智能可以显著改善制造过程的成熟度，无论是在解决方案质量方面，还是在达到所追求的成熟度所需的资源投资方面。然而，现实世界制造过程的高度复杂性和随机性，加上有限数量的昂贵的现实世界样本，也需要人工智能领域本身的方法改进。科学和技术方法：首先，我们努力提高数据驱动模型识别、优化和强化学习方面的最新技术，目标是最大限度地减少制造过程优化所需的实际样本数量。这是通过(a)数据驱动模型识别与先验过程知识和工程模型的深度集成，以及(b)有针对性的在线实验设计来创建具有高预期信息增益的样本来实现的。其次，我们创建了一种广泛适用的方法，用于人工智能辅助将最初不成熟的制造工艺转变为适用和成熟的制造工艺。除其他外，成分是临时的系统过度仪表化、过程模块化和迭代成熟过程。这是朝着人工智能专业化迈出的一步，在工程学科的意义上，程序和工具可以扩展到大型和异构的团队。相关性：将不成熟的制造过程更快地转变为成熟的过程将允许更短的时间将新产品推向市场，从而为公司提供竞争优势。虽然很难预测，但我们相信，通过使用预期的方法，可以实现过程开发的时间和成本减少高达50%。