Our use of metals is so important that it defines periods of human civilisation - from the Bronze Age (c. 3600 BC) to the Iron Age (c. 1100BC). With our present-day mastery of metals and alloys, the mounting emphasis is now on resources and the environment. The metals industry is looking at new ways to produce lighter, stronger materials in a sustainable, economical and pollution-free manner. Ultrasonic cavitation treatment offers a route to meet these goals. Ultrasonic treatment of the second commonest structural metal, aluminium, causes degassing through the evacuation of dissolved gases that lead to porosity, grain refinement to assist formability, dispersion and distribution of solid or immiscible phases to improve mechanical properties during recycling etc. In spite of the benefits, transfer of this promising technology to industry has been plagued by difficulties, especially in treating large volumes of liquid metal typical in processes such as 'Direct Chill' continuous casting for ingot production. Fundamental research is needed to answer the following practical questions: what is the optimum melt flow rate that maximises treatment efficiency whilst minimizing input power, cost, and plant complexity? What is the optimum operating frequency and acoustic power that accelerates the treatment effects? What is the optimum location of an ultrasonic power source in the melt transfer system in relation to the melt pool geometry? Answering these questions will pave the way for widespread industrial use of ultrasonic melt processing with the benefit of improving the properties of lightweight structural alloys, simultaneously alleviating the present use of polluting (Cl, F) for degassing or expensive (Zr, Ti, B, Ar) grain refinement additives. Capitalising on the unique expertise gained by the proposers during the highly successful UltraMelt project (22 publications), this research aims to answer the challenge of efficiently treating large liquid volumes by developing a comprehensive numerical model that couples all the physics involved: fluid flow, heat transfer, solidification, acoustics and bubble dynamics. Greenwich will lead the development of an improved cavitation model, based on the wave equation and conservation laws, and applied to the two-phase problem of bubble breakup and transport in the melt, and its interaction with solid inclusions (e.g. the solidification front of an aluminium alloy or of any intermetallic impurities present). To improve the efficiency of the ultrasonic cavitation treatment in flowing metal, a launder conduit will be used. The sensitivity of the process with respect to different adjustable parameters (source power, frequency, time in the cavitation zone, baffle location ...) will be examined with parallel computations in a 3D model of melt flow in the launder. This computer model will be validated by experiments in both transparent liquids and aluminium. Water and transparent organic alloy experiments will use a PIV technique by Oxford Brookes University to measure the size, number and positions of bubbles and compared these with the numerical predictions. Mechanisms of intermetallic fragmentation and particle cluster breakup will be observed in real time using a high speed camera at Brunel University and X-ray radiography at the Diamond Light Source facility. Mechanical properties of intermetallic impurities at temperatures relevant to melt processing will be measured using unique nano-indentation technique in collaboration with Anton Paar Ltd. Cavitation pressure measurements in launder conduits will be conducted at Brunel University and the empirical observations will be compared with model predictions. The fully-developed model will be used to optimise the ultrasonic melt treatment in melt flow during direct-chill casting and verified using pilot-scale facilities at AMCC (Brunel, with support of Constellium) and industrial-scale facilities at Kaiser Aluminum.

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

Additive manufacturing (AM) makes net-shaped, highly precise, and cost-effective components of intricate design with minimum waste. However, the AM industry faces many technical challenges in the production of high-quality parts due to intrinsic defects, e.g. pores, cracks, distortions and anisotropy. These microstructural discontinuities are related to the material properties and solidification behaviour upon the AM processing conditions, i.e. rapid melting and cooling. The current developments of AM focus mostly on the printing processing, mitigating intrinsic material's deficiencies by process control, such as laser power and scan speed, and much less on the material side, with a majority of the alloys being originally designed and tailored to suit other manufacturing routes, e.g. casting. The quality of AM parts is dominated by the properties and characteristics of the alloy feedstocks - vital aspects that are currently largely overlooked. As a consequence, there is a limited number of materials that are designed specifically for manufacturing high-quality AM components. The synergetic approach in this project is three-fold and aims to (a) develop a new class of hierarchically structured Al-based alloys with fine-tuned structures and compositions at both the nano- and micro-scale, which satisfy the requirements for cracking resistance, structure uniformity, reduced residual stresses and porosity, enabling a unique combination of properties and dimensional precision for AM; (b) test and optimise their performance upon AM using in situ and ex situ high precision characterisation methods; (c) validate the approach by manufacturing AM test parts with enhanced product quality and, hence, with improved properties and performance. Combining these three advances, we will deliver a new class of high-quality AM materials with lightweight, uniform structure and properties, high rigidity, thermal stability, and designed functionality; combining the best processing features of existing diverse alloy groups. While addressing the challenges of AM through dedicated material development, this proposal has a strong and credible pathway to impact other manufacturing processes, e.g. casting and powder metallurgy using the same alloy design paradigm.

The DigitalMetal CDT is born out to meet a national, strategic need for training a new generation of technical leaders able to lead digital transformation of metals industry & its supply chain with the objective of increasing agility, productivity & international competitiveness of the metals industry in the UK. The metals industry is a vital component of the UK's manufacturing economy and makes a significant contribution to key strategic sectors such as construction, aerospace, automotive, energy, defence and medical, directly contributing £20bn to UK GDP, and underpins over £190bn manufacturing GDP. Without a new cadre of leaders in digital technologies, equipped to transform discoveries and breakthroughs in metals and manufacturing (M&M) technologies into products, the UK risks entering another cycle of world-leading innovation but losing the benefits arising from exploitation to more capable and better prepared global competitors. The evolution to Industry 4.0 and Materials 4.0 coupled with unprecedented opportunities of "big data" enable the uptake of artificial intelligence/deep learning (AI/DL) based solutions, making it feasible to implement zero-defects, right first-time manufacturing/zero-waste (ZDM/ZW) concepts and meet the environmental-, sustainable- and societal- challenges. However, to fully take advantage of these opportunities, two critical challenges must be addressed. First, as user-identified problems in the metals industry that spans domains (from discoveries in M&M to their up-scaling and deployment in high volume/value production), urgently needed a new breed of engineers with skills to traverse these domains by going beyond the classical PhD training, i.e., T-model signifying transferable skills and in-depth knowledge in a single domain, to a new Pi-model raining that is underpinned by transferable skills and in-depth knowledge that transverse across domains i.e.,: AI/DL and engineering (M&M) to enable rapid exploitation of discoveries in M&M. Second, while AI/DL domain provides data-driven correlation analysis critical for product performance and defect identification, it is insufficient for root cause analysis (causality). This necessitates training on integrating data-driven with physics-based models of product & production, which is currently lacking in the metals industry. The Midlands region, as the top contributor to UK Gross Value Added through metals and metal products, with world-leading companies, such as Rolls-Royce and Constellium, LEAR and their customers, underpinned through collaborations with the five Midlands universities: Birmingham, Leicester, Loughborough, Nottingham & Warwick, is uniquely positioned to integrate research and industry resources and train a new cadre of engineers & researchers on the Pi-model to address user-needs. Our vision is to train future leaders able to accelerate the exploitation of M&M discoveries using digital technology to enable defect-free, right first-time manufacturing at reduced costs, digitise to decarbonise, and implement fuel switching in metals manufacturing industry.

The Materials Made Smarter Centre has been co-created by Academia and Industry as a response to the pressing need to revolutionise the way we manufacture and value materials in our economy. The UK's ability to manufacture advanced materials underpins our ambitions to move towards cleaner growth and a more resource efficient economy. Innovation towards a net zero-carbon economy needs new materials with enhanced properties, performance and functionality and new processing technologies, with enhanced manufacturing capability, to make and deliver economic and societal benefit to the UK. However, significant technological challenges must still be overcome before we can benefit fully from the transformative technical and environmental benefits that new materials and manufacturing processes may bring. Our capacity to monitor and control material properties both during manufacture and through into service affect our ability to deliver a tailored and guaranteed performance that is 'right-first-time' and limit capacity to manage materials as assets through their lifetime. This reduces materials to the status of a commodity - a status which is both undeserved and unsustainable. Future materials intensive manufacturing needs to add greater value to the materials we use, be that through reduction of environmental impact, extension of product life or via enhanced functionality. Digitalisation of the materials thread will help to enhance their value by developing the tools and means to certify, monitor and control materials in-process and in-service improving productivity and stimulating new business models. Our vision is to put the UK's materials intensive manufacturing industries at the forefront of the UK's technological advancement and green recovery from the dual impacts of COVID and rapid environmental change. We will develop the advanced digital technologies and tools to enable the verification, validation, certification and traceability of materials manufacturing and work with partners to address the challenges of digital adoption. Digitisation of the materials thread will drive productivity improvements in materials intensive industries, realise new business models and change the way we value and use materials.