In investment casting, the removal of wax from the shell is a critical step which may cause shell failure. It would be advantageous to predict the stress development during de-waxing process with computer simulation. The process was simulated with the consideration of two aspects: (i) The thermo-physical data required to model the shell and wax behaviour in the autoclave environment and (ii) A simulation capable of capturing the interaction between shell, wax and the autoclave environment. Data on mechanical properties, thermal properties, permeability, rheology, thermal expansion and density was gathered for wax and shell as appropriate. Flow-3D was used to simulate the de-wax process such that the shell and wax can be simultaneously modelled. It was shown that the Von misses stress exceeded the expected critical failure stress at certain nodes after steam was introduced to the system. Waxes with higher viscosity were predicted to reach the critical stress sooner. The simulation showed that for the selected drainage orifice sizes that was no or little difference in the time taken to reach the critical stress. Wax compressibility which was considered to represent shell permeability was predicted to have a large effect on shell cracking prediction. In general, the statistics of failure in validation test limited the conclusions that could be drawn. Waxes predicted to show differences in cracking and drainage with increasing orifice size did so in the experiment. The simulated drainage times were greater than determined experimentally by around 380s and this requires further investigation.

An investigation has been performed into the formulation of silica-based ceramic core materials for investment casting. The formulation of 3 materials that are currently used by Ross Ceramics was investigated in detail and the data from this used to determine the elements of the formulation that should be investigated in more detail. The techniques used included a variety of mechanical tests at both room and high temperatures, dilatometry, phase analysis by XRD and chemical analysis by XRF, pore analysis by simple Archimedes tests and mercury porosimetry and both scanning electron and transmitted light microscopy. A large number of materials were created with different formulations and the various characterisation techniques employed were used to determine the function of the different constituent. It was determined that the function of zircon was to prevent grain boundary movement at elevated temperatures via the Zener effect, by acting as a non-reactive secondary phase. The zircon also had an additional consequence from the inherent contamination, with alumina, from the ball-mill procedure performed by the supplier. This had the effect of significantly affecting several high temperature properties. The effect of several dopant materials: TiO2, Al2O3, MgO, cristobalite seed and Molochite addition was also investigated. The latter two were determined to function mainly by their inherent contamination. The other additives affected the materials in different ways depending on the amount used and the presence of any phase eutectics that had detrimental effects on the high temperature material properties. The particle size distribution of the materials was also investigated. It was determined from the experiments that the D20 was an important factor for deterring final material properties and that most materials obeyed a modified Griffith’s crack theory relation, with the exception of materials with a larger zircon particle size than that of the main silica constituent. From this work new materials could be created using the knowledge gained and material properties could be optimised to meet specific requirements by changing zircon quantity, dopant levels and particle size.

Two additive processing scenarios were considered covering manufacturing and repair applications. One scenario required evaluation of three processes for the addition of shaped blocks to a casing. The other involved infilling a shallow hemispherical indent with one of three polycrystalline alloys (alloy 718, waspaloy™ and RR1000) using different deposition strategies. Processinduced discontinuities were characterised and controlled using a design of experiments approach. As distinct from welding, process variables of overlap, Toolpath, bead shape,sequencing and incremental height were established as influential variables Toolpath in particular can give markedly different textures and grain alignment which would be expected to influence mechanical anisotropy. Fine pool processes seem less prone to cracking and more likely to yield fine microstructures.

Two-stage roll milling of chocolate pastes was studied to investigate the relationships between roll speeds and pressing force with mass processing rate, product particle size and roll torque. A three-roll mill was used, operating in two and three-roll mode for each stage, respectively. A strong correlation (R2=0.97) was found between the size of the largest particles and the surface coverage of the discharge roll. No evidence was found of shear rate affecting the size of the largest particles (p=0.13). Negative torque values were measured on the slower rolls under higher forces and/or speed ratios. The minimum specific mechanical energy input (S\(M\(E\(I) was observed to occur for the lowest pressing force that still resulted in satisfactory material transfer. Theoretical models based on the lubrication approximation (LAT), with and without pressure-dependent viscosity, resulted in lower apparent viscosities found at faster roll speeds, higher speed ratios and/or greater forces. The observed tendency for the material to detach from the slow roll suggests that higher shearing action in the slow boundary causes a greater reduction in paste-roll adhesion, provided that material cohesion exceeds adhesion. A comparison with full 2D FEM solutions revealed differences no greater than 0.02% in the pressure and velocity profiles.

The research involved the optimisation of various machining operations for the manufacture of automotive turbocharger impellers. Phase 1 work comprised a capability study of the Al-Si C355 impeller machining process within Doncasters Sterling. The results highlighted that both C\(_p\) and C\(_{pk}\) values were generally above the automotive industry standard of 1.66 and that current processes were capable of producing parts to specification. Benchmark testing of TiN coated WC and PCD tooling against uncoated WC was undertaken in Phase 2 experiments. While the PCD tools showed significant improvement, the TiN coated tools were inferior in terms of thrust force, torque and workpiece surface roughness when compared against the uncoated WC tools. Phase 3 research evaluated the performance of advanced diamond like carbon (DLC) coatings against WC when blind-hole drilling C355 and reaming 6082-T6 aluminium alloys. Benefits in terms of extended tool life and reduced material adhesion/BUE were observed when drilling and reaming using the commercial Graphit-iC™ coating. The performance of CVD diamond coated and PCD tools were also assessed with the latter showing lower wear rates with negligible workpiece adhesion/BUE compared to WC in both drilling and reaming experiments. The former however failed catastrophically at high cutting speeds during drilling.

Manufacturing is currently experiencing a paradigm shift in the way that products are designed, produced and serviced. Such changes are brought about mainly by the extensive use of the Internet and digital technologies. As a result of this shift, a new industrial revolution is emerging, termed “Industry 4.0” (i4), which promises to accommodate mass customisation at a mass production cost. For i4 to become a reality, however, multiple challenges need to be addressed, highlighting the need for design for agile manufacturing and, for this, a framework capable of integrating big data analytics arising from the service end, business informatics through the manufacturing process, and artificial intelligence (AI) for the entire manufacturing value chain. This thesis attempts to address these issues, with a focus on the need for design for agile manufacturing. First, the state of the art in this field of research is reviewed on combining cutting-edge technologies in digital manufacturing with big data analysed to support agile manufacturing. Then, the work is focused on developing an AI-based framework to address one of the customisation issues in smart design and agile manufacturing, that is, prediction of potential customer needs and wants. With this framework, an AI-based approach is developed to predict design attributes that would help manufacturers to decide the best virtual designs to meet emerging customer needs and wants predictively. In particular, various machine learning approaches are developed to help explain at least 85% of the design variance when building a model to predict potential customer needs and wants. These approaches include k-means clustering, self-organizing maps, fuzzy k-means clustering, and decision trees, all supporting a vector machine to evaluate and extract conscious and subconscious customer needs and wants. A model capable of accurately predicting customer needs and wants for at least 85% of classified design attributes is thus obtained. Further, an analysis capable of determining the best design attributes and features for predicting customer needs and wants is also achieved. As the information analysed can be utilized to advise the selection of desired attributes, it is fed back in a closed-loop of the manufacturing value chain: design → manufacture → management/service → → → design ... For this, a total of 4 case studies are undertaken to test and demonstrate the efficacy and effectiveness of the framework developed. These case studies include: 1) an evaluation model of consumer cars with multiple attributes including categorical and numerical ones; 2) specifications of automotive vehicles in terms of various characteristics including categorical and numerical instances; 3) fuel consumptions of various car models and makes, taking into account a desire for low fuel costs and low CO2 emissions; and 4) computer parts design for recommending the best design attributes when buying a computer. The results show that the decision trees, as a machine learning approach, work best in predicting customer needs and wants for smart design. With the tested framework and methodology, this thesis overall presents a holistic attempt to addressing the missing gap between manufacture and customisation, that is meeting customer needs and wants. Effective ways of achieving customization for i4 and smart manufacturing are identified. This is achieved through predicting potential customer needs and wants and applying the prediction at the product design stage for agile manufacturing to meet individual requirements at a mass production cost. Such agility is one key element in realising Industry 4.0. At the end, this thesis contributes to improving the process of analysing the data to predict potential customer needs and wants to be used as inputs to customizing product designs agilely.

Linear friction welding (LFW) is known as an advanced technique to substitute fusion welding of joining Ni-based superalloys. However, due to the complex microstructural development caused by LFW, some unexplained issues still need to be solved, such as: influence of weld parameters (weld pressure, amplitude of oscillation, and frequency of oscillation) on the development of microstructural, microhardness, texture and residual stresses in LFWed IN718-IN713LC; impact of post-weld heat treatment (PWHT) on the microstructural and microhardness development; influence of rapid thermal cycle on the precipitate dissolution of IN718 and IN713LC; formation mechanisms of alumina inclusions (Al-oxides) on the weld interface. The present work aims to investigate these issues.

A major factor limiting the use and longevity of rare earth based magnetic materials is their susceptibility to aqueous corrosion and associated detrimental effects upon the magnetic properties of the material. This process was investigated through a combination of exposure to simulated environmental conditions and hydrogen absorption/desorption studies (HADS) in conjunction with magnetic characterisation. This study utilises NdFeB MQP-B melt-spun ribbon manufactured by Magnequench, in the form of MQI bonded magnets and also in its unbonded state as MQ powder. Specifically, it was concerned with how effective a variety of bonding media (epoxy resin,PTFE, zinc) and surface coatings (PTFE, Qsil, zinc LPPS, Dex-Cool) were at limiting the impact of aqueous corrosion in MQI bonded magnets. To characterise the effect of hydrogen absorption upon the magnetic properties of the MQP-B, hydrogen uptake was induced followed by a series of outgassing heat treatments with subsequent magnetic characterisation accompanied by HADS techniques performed after each outgas. This allowed comparisons to be made between the effects of aqueous corrosion process and hydrogen absorption upon the magnetic properties of the alloy. This study has clearly demonstrated the link between the abundance of environmental moisture and rate of Hci losses in MQI bonded magnets. In addition to this the key mechanism responsible for the degradation of magnetic properties has been identified. These losses have been attributed to the absorption of hydrogen generated by the dissociation of water in the presence of NdFeB during the aqueous corrosion process. It has been shown that the use of a bonding media that is impermeable to water can limit the effects of aqueous corrosion by limiting water access to the Magnequench particles (MQP) and also the positive effects of the use of suitable surface coatings has been shown to be effective for the same reason.

Titanium aluminide alloys are prone to extensive shrinkage porosity, particularly interdendritic layer porosity. This research aims to develop a better understanding of shrinkage porosity in Ti-46Al-8Nb alloy and how to minimize it. Practical measurements in conjunction with computer simulations were carried out in order to study the effects of cooling rate and temperature gradient on shrinkage porosity in Ti-46Al-8Nb investment castings, through investigating the influence of casting geometry, specifically mould taper on cylindrical bars, and preheating mould temperature on the macrostructure and the formation of shrinkage porosity. The interface heat-transfer coefficient of the ceramic shell/surroundings was determined using measured cooling curves obtained in 200 mbar of Argon and in vacuum. Furthermore, the alloy/mould interfacial heat transfer coefficient was estimated and was verified by comparing measured and predicted cooling curves of Ti-46Al-8Nb. The Niyama criterion function was validated to predict shrinkage-related porosity in Ti-46Al-8Nb castings, through either thermal or tilt-filling ProCastTM simulation models. Positions in a casting with Niyama values above 4 (°C. min)0.5cm-1; (3 (K. s)0.5mm-1); could be considered completely sound. The critical value of the Niyama function was 2 (°C. min)0.5cm-1; (1.5 (K. s)0.5mm-1); below which the position tested was susceptible to macroshrinkage porosity.

This research presents a new sensor structure, the coupled micro resonator array (CMRA) as an approach to reduce the complexity of large artificial nose sensing system. The aim is to exploit multiple resonant sensors with a simplified readout. The CMRA working principle is based on mass loading frequency response effect; the frequency response of the coupled resonators is a signature for the multiple sensors. The key research outputs are balanced effective mass of the coupled resonators for measurable response and broke the structure symmetry for unique frequency response pattern and stable structure eigenvectors to enhance the system odour discrimination. To develop the CMRA, the structure is modelled and analysed using finite element and lumped mass analysis. Using silicon-on-insulator material, the CMRA is fabricated in order to evaluate the performance. The effect of mass loading is tested by platinum mass deposition using focused ion beam technology (FIB). The inverse eigenvalue analysis was used to estimate the mass change pattern of the CMRA structure. The research also investigates effect of the manufacturing variations on the CMRA structure performance. With the finger print of the coupled frequency response, the output signal of N multiple resonant sensors is monitored by a single processor; hence, reducing the complexity of readout and signal processing system.