The application of laser assisted machining/processing has shown promise in reducing tool wear in the machining of difficult-to-machine aerospace materials, such as, metal matrix composites (MMCs). On the other hand, ultrasonically assisted machining has been successfully used to demonstrate essential reductions in cutting forces with an improvement of machined surface quality. This project is a fundamental research programme that aims to comprehensively study the two techniques in combination with a clear route to implementation. Through the transition to hybrid-hybrid manufacturing processes such as the one proposed, UK industries will be able to meet the growing needs of present and future sectors/customers by efficient and sustainable resource usage in the manufacture of future aerospace materials. The research will focus on the influence of the thermal field-ultrasonic vibrations-mechanical deformation on the MMC material taking into consideration the initial underlying micro-structure of the material. Special attention will be paid to dynamic recrystallization and grain growth of the metallic matrix material due to the influence of the imposed thermal field and deformation-rates (due to machining). In parallel, a laser-ultrasonically assisted machining system will be designed, developed and installed on an existing CNC machine, with the aim of cutting without coolants, using less force and machining-induced damage. Machining studies will be conducted at industrially relevant machining conditions. Comparisons will be drawn with current practice for best machining outcomes. It is expected that the new hybrid-hybrid manufacture will lead to less machining forces with reduced tool wear and post machining (tensile) residual stresses. Finally, several case studies will be conducted with the aim of developing next generation tools for optimal manufacture.

In advanced manufacturing, there exists a rising demand for both high productivity and producing high-performance components with tighter tolerances. In order to meet these requirements, monitoring cutting tool conditions and machine tool health is needed to improve dimensional accuracy of workpiece, extend the cutting tool life, minimise machine tool down time and eliminate scrap and re-work costs. Traditionally, monitoring cutting tool conditions and machine tool health is carried out by operators who perform a manual inspection, which often causes unnecessary stoppages of machine tools and, as a result, costs incurred from lost productivity. However, without a timely inspection of both cutter status and machine tool working conditions, cutter wear or breakage and machine tool malfunction can take place during machining causing significant damage to workpieces. Some researchers have estimated that the amount of machine tool downtime due to these problems is around 6.8% while others put the figure closer to 20%. Therefore, manufacturing costs can be significantly higher than necessary when either cutters are changed before the end of their useful life or after cutter wear and breakage or machine tool malfunction have caused damage to workpieces. Consequently, a real time and automatic inspection of cutting tool status and machine tool health conditions is needed to profoundly address these problems. This project aims to propose a fundamental solution to the challenges faced by current technologies and develop innovative techniques that can autonomously detect cutting tool and machine tool anomalies in machining for advanced manufacturing. This innovative solution will be based on a novel approach known as sensor data modelling and model frequency analysis, which is uniquely developed by the PI's team at Sheffield and has recently found applications in the condition monitoring and fault diagnosis of a wide range of engineering systems and structures. The project will involve a close multi-disciplinary collaboration of ACSE academics, AMRC engineers, and industrial partners. The novel project idea and this unique research collaboration are expected to fundamentally resolve many challenges and produce urgently needed diagnostic technologies for autonomously detecting cutting tool and machine tool anomalies in machining for advanced manufacturing industry in UK.

This work will change the way we think about machining high value titanium components - for example, turning an aeroengine shaft on a lathe. Rather than apply global rules about how much metal we can remove and how fast we can rotate the part, we will develop a technique that can monitor - in real time - the "microstructure" of the part, in order to determine how much pressure we apply with the cutting tool. Microstructure in metals is analogous to the different parts of timber - heartwood, sapwood, knots, how the grain runs - but on a much smaller scale (usually fractions of a millimetre). A master carpenter will see and feel these features of the timber by sight and touch, and instinctively work the wood with their tools in such a way as to maximise strength and/or visual appeal whilst using the least amount of effort. Such finesse has not been possible in metal working as - until now - there has not been a technique available that can "see" the microstructure. A technique called spatially resolved acoustic spectroscopy (SRAS for short) uses lasers to generate and detect very high frequency ultrasonic waves that travel on the surface of the metal component. These waves interact with the microstructure, and this allows us to "see" it. By relating this information to knowledge of how machining the metal affects its performance - which is another part of the work - opens up the possibility of intelligently crafting the cutting process. Not only will this lead to faster machining processes and less damage, it will also mean that a map of the microstructure of the final part is available - this will be invaluable for confirming quality.

UK is the world's 9th largest manufacturing country [1]. Machining is one of the most used processes for producing precision parts used in aerospace and automotive industries. The demand for high performance and quality assured parts requires high precision, often over a large scale resulting in increased manufacturing costs. It has become a rule of thumb that precise machines with stiff structures and large foot prints are required for machining precision parts. As a consequence, machining costs grow exponentially as the precision increases. This has resulted in the development of expensive and non-value adding off-line verification and error compensation methods. However, these methods do not take the impact of cutting tool/workpiece geometry, cutting forces and time variable errors into account. The uptake of additive manufacturing has also resulted in generation of optimised parts often with complex geometries and thin and high walls which require finish machining with long slender tools. In these scenarios, cutting forces can bend the tool and the workpiece resulting in geometrical inaccuracies. Fluctuating cutting forces result in chatter leading to damaged surface integrity and short tool life. Using new sensors, advanced signal processing and intelligent control systems can provide the ability to detect geometrical and surface anomalies when machining, and provide data to generate strategies to prevent costly mistakes and poor quality. However, off-the-shelf sensors and data transmission devices are not necessarily suitable for monitoring and controlling machining processes. Existing high precision sensors are either too large or too expensive making them only useful for laboratory applications. Conventional statistical and process control methods cannot cope with high data sampling rates required in machining. The proposed research will realise low-cost sensors with nano scale resolution specific to machining, tools and intelligent control methods for precision machining of large parts by detecting and preventing anomalies during machining to ensure high precision part manufacture and prevent scrap production. [1] Rhodes, C., 2018, Briefing Paper No. 05809, Manufacturing: International comparisons, House of Commons Library.

Many industrial processing operations depend on feed materials that are fine powders with poor handling characteristics, which have to be rectified by granulation to form coarser granules. Generally wet granulation is employed, in which a binder is added to the powder in a mixer usually in batch processes. Continuous Twin Screw Granulation (TSG) has considerable potential, eg in the pharmaceutical sector, because of the flexibility in throughput and equipment design, reproducibility, short residence times, smaller liquid/solid ratios and also the ability to granulate difficult to process formulations. However, there remain significant technical issues that limit its widespread use and a greater understanding of the process is required to meet regulatory requirements. Moreover, encapsulated APIs (Active Pharmaceutical Ingredients) are of increasing interest and the development of a TSG process that did not damage such encapsulates would significantly extend applications. Experimental optimisation of TSG is expensive and often sub-optimal because of the high costs of APIs and does not lead to a more generic understanding of the process. Computational modelling of the behaviour of individual feed particles during the process will overcome these limitations. The Distinct Element Method (DEM) is the most widely used method but has rarely been applied to the number of particles in a TSG extruder (~ 55 million) and such examples involve simplified interparticle interactions e.g. by assuming that the particles are smooth and spherical and any liquid is present as discrete bridges rather than the greater saturation states associated with granulation. The project will be based on a multiscale strategy to develop advanced interaction laws that are more representative of real systems. The bulk and interfacial properties of a swelling particulate binder such as microcrystalline cellulose will be modelled using Coarse-grained Molecular Dynamics to derive inputs into a meso-scale Finite Discrete Element Method model of formulations that include hard particles and a viscous polymeric binder (hydroxypropylcellulose). Elastic particles (e.g. lactose and encapsulates) with viscous binder formulations will be modelled using the Fast Multi-pole Boundary Element Method. These micro- and meso-scale models will be used to provide closure for a DEM model of TSG. It will involve collaboration with the Chinese Academy of Science, which has pioneered the application of massively parallel high performance computing with GPU clusters to discrete modelling such as DEM, albeit with existing simpler interaction laws. An extensive experimental programme will be deployed to measure physical inputs and validate the models. The screw design and operating conditions of TSG for the formulations considered will be optimised using DEM and the results validated empirically. Optimisation criteria will include the granule size distribution, the quality of tablets for granules produced from the lactose formulation and the minimisation of damage to encapsulates. The primary benefit will be to provide a modelling toolbox for TSG for enabling more rapid and cost-effective optimisation, and allow encapsulated APIs to be processed. Detailed data post-processing will elucidate mechanistic information that will be used to develop regime performance maps. The multiscale modelling will have applications to a wide range of multiphase systems as exemplified by a large fraction of consumer products, catalyst pastes for extrusion processes, and agriculture products such as pesticides. The micro- and mesoscopic methods have generic applications for studying the bulk and interfacial behaviour of hard and soft particles and also droplets in emulsions. The combination of advanced modelling and implementation on massively parallel high performance GPU clusters will allow unprecedented applications to multiphase systems of enormous complexity.