Manufacturing Fellowship Extension in: Controlling geometrical variability of products in the digital and smart manufacturing era

这项奖学金提案是EPSRC制造业奖学金的三年延伸：控制制造产品的几何可变性(EP/I033424/1)。目前的研究金正在探索几何分解(即大小、形状和纹理)的数学基础，并创造突破性的技术来控制制造产品中的几何可变性。该方法将基础几何数学直接与来自不同工业部门(即航空航天、光学、医疗保健和弹射中心)的关键部件的设计、制造和验证联系起来。在这种情况下，不同类型的几何分解指定了几何表面要求(谱、形态和分割分解)。奖学金延期提案将采用目前奖学金的研究成果，并将其作为在制造业价值链内的新领域进行更高级基础研究的垫脚石。研究工作分为四个方面：1.制造过程的不同方面在不同的尺度(即尺寸、形状和纹理)上在曲面上留下不同的多标量几何特征。通过分解这些不同的特征特征，可以获得关于不同制造方面的信息，从而能够表征和控制制造过程的不同方面。传感器网络以不规则图像的形式提供信息，就像立体绘画一样，环境的不同视角和时间都结合在一起。这些信息的分解将提供对特征及其关系的访问，从而使敏捷的动态预测模型能够自我感知其环境，从而为开发来自传感器网络的仿生反馈控制回路奠定数学基础。智能自主制造将需要访问积累的庞大制造知识库(国家标准和国际标准、材料数据表等)。为自动创建机器可读的智能信息系统创建信息结构分解的基础，并应用这一结果为制造业价值链开发完全严格的数学基础。4.以EPSRC高级计量学未来中心(EP/P006930/1)为杠杆，将上述结果传播到解决实际工业问题中，以展示使用在先前的制造联谊会和本扩展中发展的基本分解理论相对于传统方法的优势。

This fellowship proposal is a three year extension of the current EPSRC manufacturing fellowship: Controlling Geometrical Variability of Products for Manufacturing (EP/I033424/1). The current fellowship is exploring the mathematical fundaments for the decomposition of geometry (i.e. size, shape and texture) and creating ground-breaking technology to control geometrical variability in manufactured products. The approach links fundamental geometrical mathematics direct to key component's design, manufacturing and verification from different industrial sectors (i.e. aerospace, optics, healthcare and catapult centres). In this case, the different types of geometrical decompositions specified geometrical surface requirements (spectrum, morphological and segmentation decompositions). The fellowship extension proposal will take the research results from the current fellowship and use them as a stepping stone for more advanced fundamental research in new areas within the manufacturing value chain. The research work is broken down to four aspects:1. Different aspects of the manufacturing process leave different multi-scalar geometrical features, in a surface, at different scales (i.e. size, shape and texture). By decomposing these different signature features, information regarding different manufacturing aspects can be gained enabling characterisation and control of different aspects of the manufacturing process.2. Sensor network provide information in the form of an irregular image, like a cubist painting, with different views, and times, of the environment all combined together. Decomposition of this information will provide access to features, and their relationships enabling an agile dynamic predictive model to be self-aware of its environment enabling mathematical foundations for bio-inspired feedback control loops from sensor networks to be developed.3. Smart autonomous manufacturing will require access to the huge amassed manufacturing knowledge-base (National and International Standards, Materials data-sheets, etc.). Create the foundations of decomposition of information structures for the automatic creation of smart information systems that are machine readable and to apply this result to develop the full rigorous mathematical foundations for the manufacturing value chain. 4. Using the EPSRC Future HUB in Advanced Metrology (EP/P006930/1) as leverage, disseminate the results from the above to solve real industrial problems to demonstrate the advantage of using fundamental decomposition theory, as developed in the previous manufacturing fellowship and this extension, over traditional approaches.