AdefuyePhD Students 

This project is in relation to the technical needs of slender continuum robots (length/diameter >> 20) for operations in confined environments, e.g.in-situ repair and maintenance of gas turbine engines. This will include kinematics modelling, dynamics modelling of slender continuum robots in varying loading conditions as well as the prototyping and validation of the results, which have not been studied in-depth. The project can make a step change in the field of continuum robots, pushing the technology closer to be ready for the applications in industry.

In the modern airspace industry efficiency of turbine engines strongly depends on the inlet gas temperature. This imposes high requirements on the properties of the engine component materials, forcing them to work on the edge of their capabilities. One way to support the constant turbine technology development is to design new thermal barrier coating (TBC) materials, another way is to enhance the performance of existing TBCs. My research is focused on modifications of TBC, including mechanical and microstructural ones. This involves a deep understanding of the thermal and mechanical behaviour of the TBC system under working conditions as well as metallurgical transformations, associated with applied alterations. Establishing mechanisms of modification influence on TBC performance will enable achieving desired system properties in a controlled way.

This project investigates the machining of ceramic and non-conductive materials through Electrical Discharge Machining (EDM). The main drive behind this research is the currently high demand in micro-machining parts inside industries such as automotive and aerospace, hence, the need of investigating and expanding the EDM capabilities to non-conductive aerospace materials.On a parallel side, it is also being investigated the potential use of viscous dielectrics for EDM. With this, its being carried out experimental procedure on different types of viscous dielectrics and how they contribute with the improvement of the process.

In-situ inspection and repairing are necessary tasks to the complicated machines/facilities due to their non-transferability or both high time and financial cost from the disassembling-assembling process. Based on advanced electromagnetic/electromechanics actuators, and novel mechanisms, this project aims to provide a robotic system with a potential for in-situ inspection in extreme environments. The academic valued solution is also expected to make its functionality and price more friendly to the industry with a disposable requirement.

This project aims to advance the precision of laser powder bed fusion additive manufacturing through improved in-process surface texture detection with the support of Marie-Curie Innovation Training Network. A number of methods exist for in-situ inspection of AM processes, e.g. thermal imaging, high speed imaging with cameras and co-axial melt pool imaging; however, no system to date has been able to balance the requirements of fast inspection with total inspection. For this reason, a novel vision system will be produced, which allows detection of different types of defects or significant features such as porosity, cracks, etc. via visual identification in a way that is portable and self-calibrating for easy installation in-line in the manufacturing process.

Hiding corresponds to the visual property of paint which is associated with the ability to occlude the colour or colour differences of the substrate. In the paint and coating industry, the measurement of hiding is traditionally performed in a standardised procedure with controlled lighting, using a drawdown bar to specifically apply a constant thickness of paint, and measuring the contrast ratio of this applied paint. Such methods rely on the unrealistic assumption that paint will be applied in a smooth, uniform manner outside of the laboratory. Applying paint generally results in uneven film thickness and surface texture, which greatly impacts hiding power. This project seeks to develop a new method to measure hiding of applied coatings, without the requirements for controlled lighting and that can allow for paint films of non-uniform thickness – resulting in a more accurate and realistic measure of hiding power. 

The development of this new method for measuring applied hiding will rely on the use of image capture. Images of applied waterborne paints will be analysed to obtain a metric for hiding power. This project will approach this from a visual perception perspective, rather than measuring the physical properties of the paint. The hiding metric will take into account different visual properties of the paint (as captured in the image) such as: colour, gloss, wet/dry state, illumination and surface texture. Psychophysical experiments will be conducted to determine which aspects of the images are relevant and meaningful to the visual perception of paint hide. This project will lead to an image-capture system that can accurately determine and measure the hiding power of applied waterborne paints in a realistic setting, using visual psychophysical data and images, without the need for directly measuring the physical properties of the paint itself. 

I am working in conjunction with Oerlikon Balzers to investigate coating methodologies for additive manufacturing, using Physical and Chemical Vapour Deposition (PVD, CVD) and surface modification with Electron Beams. This will improve understanding of the surface modification of AM parts before and after existing process steps. The focus will be on improving performance (thermal, mechanical, corrosion) and manufacturing processes for the tooling sector.

The significant usage of the micro components in many industries, such as automotive, aerospace and electronic gadgets makes the metrology as a bottle-neck technology in manufacturing. Coordinate metrology is the most common way to measure the geometry or form of components. With the developments in visualization (capturing and data processing) and optical technology, the optical metrology is getting more popular for its dense databases and high precision measurements from last couple of decades. So far many optical meteorology sensors have been developed and available in the current market. However,they are hard to integrate in-line with production machinery.

The main aim of the project is to develop a hybrid-metrology sensor prototype for industrial inline production measurement systems, including a vision system for fast defect detection and a focus variation system for 3D topography measurement. In practice, the final product would be an industrial inline production metrology sensor prototype, which is tested with multiple parameters such as measurement speed, system performance, resolution and accuracy.

This project is a part of the Precision Additive Metal Manufacturing (PAM^2) Marie Skłodowska-Curie Innovative Training Network (ITN) funded by the European Union under the Horizon 2020 Programme. PAM^2 has close collaboration with industry and academia to address the quality assurance and the various process stages of AM, with the aim of implementing good precision engineering practice. 

The main aims of this project are to study the effects of powder feedstock on the properties of final LPBF parts. There are some aspects that need to be considered for powder feedstock, with particle size distribution and chemical composition being the most important factors as they can influence almost all other powder properties and play a crucial role in final parts properties. Therefore, studies will be carried out to get an understanding of how particle size distribution and chemical composition affecting the powder feedstock performance and final parts performance with the full consideration of processing parameters. Finally, perfect powder feedstock for LPBF technique is expected.  

This research is related to sensor fusion in continuum robot control, aims to develop a more feasible and reliable control system including hardware and software for the current robot system.

Nowadays, Additive Manufacturing and especially Selective Laser Melting of metals are taking lots of interest from both industry and academia. Although several metals can be easily process, some others are more challenging to process. Magnesium and its alloys are part of the second category. As the melting temperature of magnesium is really close to its boiling temperature regarding how much energy bring the laser, it is intricate to maintain the melt pool between these two temperatures.My project will be to optimize the Selective Laser Melting of Magnesium with the help of modeling via Computanional Fluid Dynamics.

Powder bed fusion is a relatively new and highly promising technology in additive manufacturing. Widespread uptake of PBF is limited by a lack of confidence in the produced parts. One way to increase confidence is to introduce new standards and methods of control for improving part quality.

This product focuses on the condition of the powder layer prior to processing. A large part of this is focused on powder spreading behaviour, as this influences the geometry and density of the powder layer. The powder characteristics are also relevant, as they also affect the relevant qualities of the powder bed. 

This project concerns the measurement of relevant powder bed characteristics, utilising both in-situ and post-process measurements. This data will also be analysed using machine learning methods. The objective is to develop these measurement and analysis procedures for control of the PBF process.

Osteochondral (OC) defect refers to damage in the articular cartilage and in the underlying subchondral bone, the field of tissue engineering provides a prospective alternative strategy using biomaterials and cells in a scaffold to repair the defect. Current research explores a multiphasic graded OC scaffold that mimics the bone cartilage interface with the specific graded mechanical properties of these two tissues. The mechanical properties of the in-vivo OC tissue are used to design the geometry and Finite Element Analysis modelling is used to compare the design and materials properties used in the biofabrication.

Additive Manufacturing Technologies (AMT) is a Sheffield based manufacturer of smart post-processing systems for additive manufacturing (AM). This project aims to develop geometry measurement and machine learning integration into AMT’s DMS system. AMT require an analysis toolkit for parts at various states, including powdered, de-powdered, un-processed, post-processed, as well as various geometries and printing methods. This requires research of appropriate machine learning algorithms able to recognise and categorise the surfaces in real time while using limited computational power. Among the tasks for such machine learning is quality control of the geometry of AM part surfaces, involving internal and difficult-to-access areas. 

This PhD is the first one to look at implementing a miniaturised 3D printing technique for in-situ aero-engine repair. After a pre-selection of various metallic deposition techniques, six methods have been reviewed and analysed. By developing comparison tools, we proposed another approach to understand the weaknesses and strengths of different 3D printing techniques. Looking at a way to repair safely and efficiently aero-engines, we have decided to develop another method of depositing metallic materials.

My research will focus on new designs for heat transfer applications. The new designs will be manufactured by additive manufacturing and their performance evaluated by experiments. These new designs will be used for case studies in gas turbines. 

This project seeks to exploit the design freedoms of the Laser Powder Bed Fusion process for industrial alloys.  A method by which to simultaneously increase throughput and achieve desired local material properties is explored, thus providing a more economic process while enabling control of component failure.

Optical instruments for measuring surface topography are increasingly being used as coordinate measuring machines (CMMs) to allow them to measure complex three-dimensional components. This project will investigate the use of one such instrument based on the principle of point-autofocusing, but with a 5-axis motion system. The fundamental limitations of the techniques acting as a CMM will be determined and quantified. Specification standards that are applied in the CMM and form measurement fields (e.g. ISO 10360, 12181) will be applied, where possible, to multi-axis point-autofocussing and, where not possible, new procedures will be developed. Methods for calculating uncertainties with the instrument will be investigated, including the principle of a virtual CMM applying a Monte Carlo technique. A number of case study components will be measured to showcase the methods developed, with a focus on gear metrology. This project aims to develop measurement and calibration methodologies for the form measurement of complex engineering components.

The Fourth Industrial Revolution has revealed the importance of the application of technology as a powerful tool for the increasingly complex needs of the customer and of the value chain. It enables flexibility, enhances decision-making and improves operations.However, companies can face a number of problems because they do not necessarily have a clear vision of how to start their Digital Transformation Strategy, nor the possible impact it implies on their value chain. This research has developed a methodology based on a quality improvement approach for Digital Transformation within manufacturing processes to better inform the business case. In particular, it provides an assessment and diagnostic to define a clear vision of the Digital Transformation Strategy, proposes digital solutions to start their implementation process with clear purposes and expected benefits, and measures the efficacy and effectiveness of the application of the digital solutions. Together from a holistic perspective, these should assure the sustained success of companies. 

The development of advanced materials for high-temperature environments is essential to improve the efficiency and cut emissions in key engineering fields as nuclear or aerospace. For their outstanding mechanical properties, machining of these materials combines high process complexity with the necessity of generating surfaces with excellent microstructural integrity. In this context, my PhD research focuses on the mechanisms of microstructural deformation in machining of Ni-base superalloys. This involves investigating the physics of the cutting process, with the development of new testing methods to link macro-scale cutting phenomena with the small-scale microstructural condition generated in the machined subsurfaces.

A joint collaboration between Liberty Powder Metals and University of Nottingham motivated by a business case that suggest the powder will see a 10 fold increase in value once it has been recycled. The project is based on reducing the use of raw materials by using recycled powder. The basis of my research will focus on the mechanical properties of additively manufactured Nickel based super alloys using recycled powder. Ensuring parts have the required mechanical integrity for industrial applications after being additively manufactured such as aerospace and automotive. 

Incremental sheet forming (ISF) is a reasonably new process for manufacturing of small batch and customised non-axisymmetric sheet parts that can be used in aerospace, automotive industries and for medical applications.The double side incremental sheet forming (DSIF) is an emerging variant of the ISF process with potential benefits in improved formability, accuracy and production efficiency as compared to the conventional ISF technique. This project aims at the investigation of deformation and failure mechanisms of DSIF in order to overcome the current obstacle in the development of ISF by conducting experimental testing and finite element simulations. Research will also be carried out in developing new DSIF tool path strategies and assessing the feasibility of using DSIF as a viable route for manufacturing cranial plates for medical applications. 

The continuous advances in the sectors of advanced manufacturing and precision engineering have resulted in a demand for structures with highly complex surface specifications. However, the nature of those surfaces (high slope angles, complex geometries, diffusely reflecting surfaces) makes the use of measuring instruments for the purpose of uncertainty estimation a difficult task. This fact is evident by looking at the common issues encountered by researchers, including outliers, missing points and other unexpected topographic features.    Introduced in ISO 25178-600 (2019) surface fidelity is a metrological characteristic which encapsulates all of the aforementioned aspects that relate to the interaction of an instrument and the surface (or profile) being measured.  A number of calibration methods can be found where a material artefact of similar geometry to that of the measurand is employed to quantify surface fidelity.  However, a number of issues become apparent as a single artefact cannot hold all possible surface structures or the fact that the data from a sample topography cannot be extrapolated onto a different one. Consequently, the aim of this project is the development of a calibration framework for determining topographic fidelity and how it can be applied in the estimation of uncertainty budgets allowing for the prediction of the performance of an instrument for a number of different applications.   

The aim of my research is to investigate innovative design solutions of fixtures for conformability and passive dynamic capabilities. Fixtures must be able to locate the parts that are going to be processed in an exact and repeatable position and hold them against manufacturing loads that might be applied to them. The fixtures of this PhD are focused on holding freeform geometries, known like that due to the complexity of their shapes for improved performance, and the loss in stiffness for weight lightening, aspects that complicate the positioning and clamping, and make the part more susceptible to vibration.

Incremental sheet forming (ISF), also known as Single Point Incremental Forming (SPIF), has drawn considerable attention due to its high flexibility and low set-up cost. Substantial advances have been made in fundamental framework and practical applicability over last three decades. As an emerging ISF technology, the heat-assisted ISF can dramatically improve the formability of hard-to-form materials with poor ductility and high strength at room temperature. However, the thermal mechanism behind heat-assisted ISF is still less understood, especially for friction stir assisted incremental forming.  The aim of this project is to develop the thermal model that can describe the thermal behaviour in ISF under different process conditions. 

The project deals with intelligent product design and development for implementation in manufacturing environments. Here the intelligent product is part of a production system (equipment) used to make any product in a manufacturing setting. Much of the previous research presents models of overall facility but does not factor in the capability and design of individual components or products, which constitute the facility overall. An integral direction for the research is towards development of such production systems, their feasibility in ‘plug and produce’ systems focused on providing online and inline integrated testing and decision-making during production.The research is directed towards MALT (Micro Application Leak Testing) Use-Case development. MALT provides dry air pressure decay leak testing capability suitable for applications such as lab-on-chip, diagnostic cartridges, medical vials etc. An iterative approach will be used during the project to make the already existing product smarter, adaptable and effective. Plug and play concept will be realized for integrating the developed solution in existing manufacturing lines. In this regard, the selected approach is to develop a Multi-Agent System for Testing, Controlling and Self-configuration guided by Machine Learning Pipelines.This work is being carried out under DiManD Innovative Training Network (ITN) project funded by the European Union through the Marie Sktodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018).

Recent improvements to SLM hardware have greatly improved capability and process control. Increasingly laser/electron beam processes are being used to manufacture and repair high value assets for the aerospace and biomedical industries. A critical limiting factor here is the performance of the feedstock material - the metal powder. The reuse of this through production cycles has been shown to have some effect on part performance in typical service conditions. This project aims to overcome some of these issues through the use of material characterisation to inform powder management and performance through life.

The aim of my research is to analyse the interaction of the water jet and the aerospace materials in order to study the mechanism of their removal process. The water jet technology takes advantage of the kinematic energy of the water at high pressure for the material removal process.

Machining of the aerospace materials with waterjet will bring the advantage of high material removal rate and low surface damage, which could contribute to the safety and long lasting of the aeroengines in a more efficient and economical way.

Continuum robots have been developed aim to execute some tasks which is difficult for traditional robot, such as in-situ repairing of aero-engine. For these robot systems which expected teleoperated by human, haptic feedback is a vital impact to improve their efficiency and safety. My PhD project aim to develop the haptic interface for the continuum robot, providing haptic feedback to reflect robot or task information.  The work objectives also include teleoperating the movement of continuum robot with haptic device and employing sensors to obtain robot information in constrained environment.

Frequency scanning interferometry, based on interference beat frequency analysis allow for absolute distance measurements to multiple targets. Detectability of the reflected light from variety of surfaces inside the interferometer beat frequency spectrum makes possible the development of new family of robust sensors as well as sensor networks. The measured distances can be dependent on different physical quantities, allowing for integration of a variety of sensors within a single interferometer channel.

In frame of the project the impact of multi-reflection phenomena for different material surfaces, surface properties and intra-surfaces arrangement on the FSI measured spectrum will be investigated. As an outcome of the research, a proposal for multi-reflection based sensors and sensors network configurations is expected. 

The goal of this research is to design a novel scheduling protocol and means of application for a company moving into an Industry 4.0 environment. This involves gathering information about current state of the art technology and processes and using these to develop a representative test environment for new solutions. These solutions will then be assessed and refined further if they show promise. Once a suitable process scheduling method has been established, the project will focus on applying it to an existing factory environment.

The PhD project is focused on developing machine learning methods allowing individual and collective self-learning within a regulated environment. The learning process will utilise the status data, past experiences, and key parameters to analyse possible scenarios and propose actions to achieve individual and collective goals. Newly learned skills, combined with the context in which they are relevant, will supplement the predefined set of user-supplied skills and will be used individually or collectively to respond promptly when similar situations arise in the future. I am investigating how autonomy can support learning and adaptability within a regulated environment. I also investigate the challenges of assuring availability, flexibility, and time efficiency of modern production systems and a holistic solution for self-learning manufacturing systems.

Ceramic materials with low thermal conductivity and high melting temperature are used as a coating layer in order to increase the operating temperature of an engine and process efficiency and to protect substrate material from harsh working conditions.

Due to the high thermal expansion coefficient and severe operation temperatures, thermal stresses are induced in the coating material, leading to buckling and spallation of the protective layer.

The project aims to prolong the life of thermal barrier coatings, investigate the possible ways of thermal compliance improvement of TBCs via laser source, and understand the mechanisms of structure formation and modification.

Own the inherent safety provided by the compliant body, soft robot exerts unique functions in some industrial application such as micromanipulation and vulnerable confined space inspection compared with their rigid robot counterpart. The aim of the project is the design and control of soft robots (crawling and quadrupole walking) based on a linear DEA (dielectric elastomer actuator) unit that can conduct the inspection in open and narrow spaces. The research interests also include the development of smart material technology, intelligent control methodology, and advanced manufacturing method.

In the search for increasing electrical machine efficiencies, a complete understanding of the influences that manufacturing operations have on their performance is vital. This research seeks to investigate the mechanisms by which non-conventional machining (Abrasive Waterjet, Wire EDM, Lasers) induced defects degrade the magnetic properties of both soft and hard magnetic materials. Metallurgical analyses will include SEM, EBSD, and TEM, whilst novel methods of magnetic domain imaging will be used to study changes in the micro-magnetic structure. An understanding of the machining-induced magnetic degradation will be important for improving the efficiencies of electrical machines.

Metal matrix composites (MMCs) are widely used in industrial applications due to their superior properties like light weight, high strength, high wear resistance, etc. However, their heterogeneous structure brings more challenges in obtaining good surface integrity after machining. The main purpose of this project is to study the effects of reinforcement on material removal mechanism and surface integrity when machining MMCs. So as to improve the machinability of these materials and promote their industrial application.

Data fusion has been widely employed in surface metrology because it combines information and data from multiple sensors, providing improved accuracy and coverage compared to single-sensor measurement system. This project is aimed at developing data fusion solutions for form and surface texture metrology based on machine learning. To understand the existing algorithms and develop new solutions, test data will be collected from artefacts with various geometrical complexities via fringe projection and coherence scanning interferometry. The algorithms proposed by this research will be aimed at combining the advantages of the existing algorithms with machine learning techniques, such as clustering, grouping and regression.

The demand for manufacturing highly customized products at a reasonable cost has heightened the need for developing innovative and flexible forming processes. Incremental Sheet Forming (ISF) is one of the new forming processes that has drawn significant attention in recent years. This forming process has been mainly applied in the automotive and aerospace fields so far. This project aims to use the ISF technique to process biocompatible polymers, such as PEEK and PMMA for medical applications. Research work will concentrate on the deformation mechanism, formability and optimum processing windows affordable ISF based manufacturing of cranio-maxillofacial implants.