We will develop technology for the real-time detection of defects in metal additive manufacturing processes. We envision a future where every part made will come with a digital copy of itself containing a 3D map of defects. This will enable manufacturers to accelerate certification and quality assurance of high-integrity parts through virtual testing and also provide online feedback for the rapid optimisation of process parameters. This project addresses multiple digital manufacturing research challenges across data analytics, real-time optimisation, virtual testing, and model verification. Additive manufacture (AM), also known as 3D printing, of metallic materials is transforming manufacturing supply chains across the energy, transport, healthcare, and defence sectors. It stimulates design innovation and through lighter, better performing and more reliable products, it can help us meet our future net zero and sustainability goals. However, use of AM parts in safety critical industries is limited by concerns around material property consistency. These concerns present a considerable challenge for quality assurance, slowing further adoption of AM processes and constraining much needed innovation. We aim to solve this challenge using in-process sensing, where cameras and other sensor types observe the manufacturing process in real-time, in combination with data-driven machine learning models to predict when defects occur. To do this we will design and build part geometries representative of common industrial designs and collect in-processing monitoring data across several sensor modalities (i.e. co-axial melt pool imaging, surface temperature, melt track morphology sensor systems) from our unique in-process monitoring platform. The parts will then be micro-CT scanned post-build to establish porosity truth data, creating a suite of pristine, spatially registered, data sets. The builds will cover various industrially relevant manufacturing parameters and common machine issues such as dirty lens, clogged filter, contaminated powder, worn wiper blade, etc. These data sets will be used for the training and validation of data-driven machine learning models to predict part porosity. Robust non-destructive evaluation methodologies will be used to characterise model performance. We will then implement online layer-wise feedback to dynamically adjust processing parameters and repair defects through selective remelting. This approach will address fundamental challenges in model robustness, data reduction, real-time processing, optimisation, and feedback. Ultimately, this project will enhance metal additive manufacturing part quality and enable accelerated virtual certification. Combined, these outputs will reduce the risk involved in developing innovative new products, removing a significant barrier to the widespread adoption of metal additive manufacturing technology.

Imagine you are responsible for the digitisation journey of your company's manufacturing. You know by embedding digitisation throughout the whole manufacturing value chain will bring success. However, your manufacturing portfolio is diverse e.g. high volume fast moving white goods, novel pharmaceutical biological drugs, automotive components, chemical synthesis and aircraft avionics. You also design, manufacture and operate nuclear facilities. Each of the sectors claim they are unique; however, your experience evidences the underlying challenges are common - although often articulated in different ways! You have learnt lessons from the 1980's where companies adopted automation because 'it was the new shiny technology" but productivity savings were not always realised, and the Made Smarter Review evidenced that in 2017 productivity challenges, still remain - even though the technology is available. You know that people are the critical element. You have seen manufacturing systems fail to deliver because of employee pushback, lack of engagement/skills/leadership as well as poor change management (Made Smarter Review 2017, Vander Luis Da Silva, et al. 2020). You recognise to create value in manufacturing through digitalisation needs investment in people. It is your view that the right combination of current technology, data and people can deliver SMART manufacturing today i.e. if we have the right people, we can be responsive, reactive, make smart decisions to maximise manufacturing value using live data and information. However, to achieve digitally engaged people, especially in manufacturing you believe there needs to be a process for manufacturing companies to follow, regardless of sector and size. The vision of this centre is to enable 'UK Manufacturing to improve their productivity year on year by investing in their biggest assets - people"". This investment will lead to the uptake of digitalisation. As part of our ambitious centre, Theme 4 - "Societal and cultural change: managing the disruptive impact of digital technologies." i.e. achieving digitally engaged people is core and will account for up-to 65% of our activities. Around 56% of UK manufacturing (Q2, 2019) are SMEs which are critical to UK Manufacturing. To meet our goal of digitally engaged people our research will engage the whole manufacturing value chain through Theme 3 - "connected and versatile supply chain" our second core theme However, we recognise investing in people will 'touch' all of the themes and our networking activities will be crucial in leveraging value from the Made Smarter investments. In summary, our hypothesis is that regardless of manufacturing sector and company size a common process leading to digitally engaged people is achievable in practice i.e. in industry. Impacts from embedding our research into aerospace manufacturing demonstrated the data/information engineers believed they needed, was not the data/information they used to make decisions. We were able to increase their productivity by 47% through a combination of manufacturing digitalisation and human factors. This was achieved through a step-by-step process, using data analytics, human factors and observing people in action. Our centre will build on this expertise and create a generic process for use across UK manufacturing - leading to increased productivity.