Coordination Funds

新的制造工艺通常涉及实验室规模的耗时开发阶段和工业规模的进一步加速阶段，然后才能以所需的质量，生产力和成本效益进行生产。当采用新材料和新工艺时，当制造工艺及其子工艺具有大量参数和高维状态空间时，以及当开始时只有较差的动态过程模型时，情况尤其如此。从抽象的角度来看，一个巨大而复杂的优化问题必须在过程结构，参数值，控制策略和过程仪表（传感器和执行器）方面得到解决。传统上，专家通过基于他们的知识，经验和直觉的广泛实验来完成这一点。但是从制造过程中获取实验数据与大的成本相关联，假设：我们认为，人工智能的系统化使用将显著提高制造流程的成熟度，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，都将带来显著改善。然而，现实世界制造过程的高度复杂性和随机性，再加上有限数量的昂贵的现实世界样本，需要在人工智能本身的领域进行方法上的改进。科学和技术方法：首先，我们努力提高数据驱动模型识别的最新水平，优化和强化学习的目标是最大限度地减少制造过程优化所需的真实世界样本的数量。这是通过以下方式实现的：（a）将数据驱动的模型识别与先前的工艺知识和工程模型进行深度集成，以及（B）有针对性的在线实验设计，以创建具有高预期信息增益的样本。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且成熟的制造工艺。成分阿利亚是一个临时的系统过度仪表，一个过程模块化，和一个迭代的成熟过程。从工程学科的意义上讲，这是人工智能专业化的一步，其程序和工具可以扩展到大型和异质团队。相关性：将不成熟的制造流程更快地转变为成熟流程将使新产品的上市时间更短，从而为公司提供竞争优势。虽然很难预测，但我们相信，通过利用预期的方法，可以实现工艺开发时间和成本降低高达50%。

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.