Artificial Intelligence X-ray Imaging for Sustainable Metal Manufacturing (AIXISuMM)

金属制造占全球二氧化碳排放量的8%，如果要在2050年实现碳中和，我们迫切需要过渡到更可持续的工艺。在这个项目中，我们将探讨潜在的科学和理解，以允许更高地利用低嵌入碳，高杂质的回收金属作为金属制造的原料。目前的制造方法高度依赖于能源密集型的原生金属，因为它们依赖于严格控制的成分，杂质含量非常低，以提供所需的材料性能。我们相信，新的理解需要提供变革性和有效的方法来制造高等级的金属合金，使用更高比例的低嵌入碳回收材料作为原料，可以通过利用多模态X射线成像和线人工智能。我们将开发一个新的整体特征系统，包括新开发的硬件和名为Artificial的人工智能算法智能X射线成像（AIXI）作为一种智能工具，可在实验条件下研究富含杂质的合金的凝固，这些实验条件与工业过程中发现的条件相当，例如连铸、直接激冷铸造、成型铸造和增材制造，适用于各种铝和钢合金成分。AIXI将提供优于现有方法的显著优势，因为AI将嵌入数据采集系统中，并用于实时解释原始数据，大大降低数据分析所需的复杂性和时间，并显着提高系统的分析能力。这些新知识将使我们最终了解杂质和微量合金添加剂在发展凝固微观结构中的作用，并开发减轻其有害影响的方法。它还将促进转向更全面的合金设计方法，在这种方法中，凝固微观结构的设计既能提供更好的性能，又能促进随后的下游工艺，同时最大限度地减少对环境的影响。通过减少用能源密集型初级金属稀释回收废料的需要，提高金属的可回收性;鼓励更多地使用在英国广泛使用但目前出口的低等级废料;减少下游加工步骤（工艺强化）的数量，尤其是热处理实践;通过减少对严格成分规格的依赖，简化部件的可回收性;通过改善对最终微观结构的控制，提高材料性能。我们将发现并应用缺失的科学来控制相变，以创造更良性和更耐杂质的微观结构，并允许更有效地使用昂贵和潜在稀缺的合金添加剂，这将大大减少二氧化碳密集型金属行业的资源使用。此外，我们设想，开发的硬件/人工智能分析的应用可能会促进材料科学等许多领域的快速科学发展，在这些领域，高效，快速地收集和分析复杂和大型多模态数据集对于解锁必要的理解至关重要。

Metal manufacturing is responsible for 8% of global CO2 emissions and if carbon neutrality is to be achieved by 2050, we critically need to transition to more sustainable processes. In this project we address the underlying science and understanding to allow a higher utilisation of low embedded-carbon, higher impurity recycled metal as a feedstock for metal manufacturing.Current manufacturing approaches are highly dependent on energy-intensive primary metal as they rely on tightly controlled compositions with very low impurity contents to provide the required materials properties. We believe that the new understanding needed to provide transformative and efficient methods to manufacture high grade metal alloys using a much higher fraction of lower embedded-carbon recycled material as a feedstock can be delivered by leveraging the combined power of multi-modal X-ray imaging and in-line artificial intelligence.We will develop a new wholistic characterisation system comprising both newly developed hardware and AI algorithms named Artificial Intelligence X-ray Imaging (AIXI) as an intelligent tool to investigate the solidification of impurity-rich alloys in experimental conditions comparable to those found in industrial processes such as continuous casting, direct chill casting, shape casting and additive manufacturing for a wide range of aluminium and steel alloy compositions. AIXI will provide a significant advantage over existing approaches as AI will be embedded in the data acquisition system and used to interpret raw data in real-time, drastically reducing the complexity and time required for data analysis and significantly increasing the analytical power of the system. The new knowledge will allow us to finally understand the role that impurities and minor alloy additions play in the developing solidification microstructure, and to develop methodologies to mitigate their deleterious effects. It will also promote a shift to a more holistic approach for alloy design in which the solidification microstructure is engineered to both provide enhanced properties and to facilitate subsequent downstream processes with minimised environmental impact.The newly acquired knowledge will foster the development of science for `sustainable' alloys, which will: enhance metal recyclability by reducing the need for dilution of recycled scrap with energy intensive primary metal; encourage greater use of lower-grade scrap, widely available in the UK but currently exported; decrease the number of downstream processing steps (process intensification), especially heat treatment practices; simplify component recoverability by reducing the reliance on tight compositions specifications; and enhance materials properties by improving control over the final microstructure. We will uncover and apply the missing science to control phase transformations to create more benign and impurity tolerant microstructures and allow more efficient use of expensive and potentially scarce alloy additions, which will substantially cut resource use in the CO2-intensive metal industries. Furthermore, we envisage that the application of the developed hardware/AI analysis could potentially facilitate rapid scientific development in many fields of materials science and beyond where efficient, rapid collection and analysis of complex and large multi-modal datasets is critical to unlock the necessary understanding