Staff publications (SATM)

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Open Access
Exploring the transition from preventive maintenance to predictive maintenance within ERP systems by utilising digital twins
(IOS Press, 2021-10-20) Damant, Liam; Forsyth, Amy; Farcas, Ramona; Voigtländer, Melvin; Singh, Sumit; Fan, Ip-Shing; Shehab, Essam
Over the years, there has been an advancement in how manufacturing companies conduct maintenance. They have begun transitioning from Preventive Maintenance (PM) to Predictive Maintenance (PdM). With the introduction of technologies such as Digital Twin (DT), Internet of Things (IoT), and Intelligent Manufacturing (IM), the world is rapidly changing, thus allowing companies to optimise existing processes, products and reduce costs. The existing literature offers limited investigations and best practices in the end-to-end optimisation for maintenance transformation. The current paper intends to explore (a) the transition from PM to PdM and (b) the utilisation of DTs and IM for maintenance optimisation. The paper articulates the scope and features of end-to-end maintenance optimisation for asset uptime and cost benefits. The findings can help industries understand the introductions and advancements of technologies for predictive maintenance and end-to-end optimisation with the benefit of investigating and illustrating how companies can move forward.
Open Access
Digital twin-driven framework for EV batteries in automobile manufacturing
(IOS Press, 2021-10-20) Valdez Parra, Rodrigo; Pothureddy, Gaurav; Sanitas, Tom; Krishnamoorthy, Vishnuvardan; Oluwafemi, Oluwatobi; Singh, Sumit; Fan, Ip-Shing; Shehab, Essam
The successful operation of Electric-Vehicle Batteries (EVB) is paramount for the ever-continuing goal of approaching a low carbon emission future. The Lithium-ion battery (LIB) is currently the best wager to implement on Electric Vehicles (EV). Nonetheless, it comes with its fair trade of challenges. The complexity involved in the design, manufacturing and operating conditions for these batteries has made their control and monitoring paramount. Digital Twin (DT) is concretely defined as a virtual replica of a physical object, process or system. The DT can be implemented in conjunction with the EVB physical embodiment to analyse and enhance its performance. ERP is a system designed to control production and planning amongst others. This paper presents the state-of-the-art battery design, production with the combination of DT and Enterprise system. A five-dimensional DT framework has been proposed linking the physical data and virtual data with ERP. The proposed method was used to model the digital twin of EVB at the concept level and solve its challenges faced in the industry Also the potential application & benefits of the framework have been formalised with the help of a case study from Tesla EVBs.
Open Access
Return: group design project for pin-point landing demonstrator using drone technologies
(Elsevier, 2024) Felicetti, Leonard; Ignatyev, Dmitry; Grustan, Enric; Tsourdos, Antonios
In a rapidly changing world, engineers must identify societal needs, solve problems with ingenuity, and create new knowledge. While conventional taught-based courses provide a solid foundation of knowledge, they often fall short in stimulating creative thinking, translating theoretical knowledge into real-world solutions, and fostering leadership and teamwork. This paper proposes a project-based learning course designed to solidify and amplify the technical knowledge gained in preceding taught-based modules while developing essential soft skills. The course covers various stages of designing a reusable launcher-like demonstrator, aiming to simulate take-of and pinpoint landing operations using drone technology. This involves identifying system requirements and designing both software and hardware components. Throughout the course, students enhance their ability to critically formulate, solve, and evaluate engineering problems. They gain and apply technical knowledge in all aspects of drone technology, including fight dynamics, control, navigation, guidance, situational awareness, and communication. Additionally, students explore key aspects of systems engineering practices, risk management, and time management. This paper details the problem design, course timeline, outcomes and key lessons learned from the course.
Open Access
The effect of corrosion inhibitor on X-65 steel weldment in high flow rate conditions
(Elsevier, 2024-12) Wulandari, Meyliana; Nofrizal, Nofrizal; Impey, Susan; Georgarakis, Konstantinos; Raja, Pandian Bothi; Hussin, M Hazwan
This study aims to determine the performance of a commercial corrosion inhibitor (mixture of ethanediol, 2-butoxy ethanol, and fatty acid amine) in inhibiting weldment corrosion. The inhibitor's effect on parent metals (PM), heat affected zone (HAZ), and weld metal (WM) has been investigated on the corrosion behaviour of X65-welded structures in brine solution (10 m/s) using submerged jet impingement (SJI) flow loops. The results show that inhibitors can reduce the corrosion rate by 10 times to 0.36 mm/y for WM. Linear polarization resistance and electrochemical impedance spectroscopy (EIS) show that the WM exhibited the highest corrosion rate.
Open Access
Improving time transfer performance for low earth orbit satellites
(IEEE, 2024-05-20) Pal Arora, Triyan; Petrunin, Ivan; Hill-Valler, Jaz; Anyaegbu, Esther
Low earth orbit (LEO) provides closer satellites with lower transmission losses and delays while struggling with a smaller field of view (FoV) and larger drag. The related works have been developed in the past utilising LEO satellites for positioning by relaying the signal from GNSS to the ground station. The following work addresses the timing aspect along with satellite positioning through ranging and limiting the position and velocity errors to provide control over the timing bias and drift errors. Using real-time two-line element (TLE) ephemeris data from a LEO satellite, the solution incorporates a linear Kalman filter to obtain predictions by updating the algorithm with a multilateration dataset from four known ground stations. The simulation dataset for satellite orbit propagation is used as ground truth to compare with the predictions and obtain position and velocity errors, leading to timing bias and drift. An error reduction of nearly 70 % was observed for position estimation, while an error reduction of nearly 30 % was found for the timing bias. The obtained value is within 0.5 ns of the 'LEO satellite positioning through the GNSS' technique. The proposed solution contributes to the evolving landscape of LEO navigation, improving positioning and timing measurement accuracy for satellite-based services.
Open Access
Stress, strain, or energy? Which one is superior predictor of fatigue life in notched components? A novel machine learning-based framework
(Elsevier BV, 2024-10-01) Mirzaei, Amir M.
This paper introduces an efficient framework for accurately predicting the fatigue lifetime of notched components under uniaxial loading within the high-cycle fatigue regime. For this purpose, various machine learning algorithms are applied to a wide range of materials, loading conditions, notch geometries, and fatigue lives. Traditional approaches for this task have mostly relied on one of the mechanical response parameters, such as stress, strain, or energy. This study also concludes which of these parameters serves as a better measure. The key idea of the framework is to use the profile (field distribution represented by some points) of the mechanical response parameters (stress, strain, and energy release rate) to distinguish between different notch geometries. To demonstrate the accuracy and broad applicability of the framework, it is initially validated using metal materials, subsequently applied to specimens produced through additive manufacturing techniques, and ultimately tested on carbon fiber laminated composites. This research demonstrates the effective use of all three parameters in estimating fatigue lifetime, while stress-based predictions exhibit the highest accuracy. Among the machine learning algorithms investigated, Gradient Boosting and Random Forest yield the most successful results. A noteworthy finding is the significant improvement in prediction accuracy achieved by incorporating new data generated based on the Basquin equation.
Open Access
Mapping the path to decarbonised agri‐food products: a hybrid geographic information system and life cycle inventory methodology for assessing sustainable agriculture
(Wiley, 2024-09) Martindale, Wayne; Saeidan, Ali; Tahernezhad‐Javazm, Farajollah; Hollands, Tom Æ; Duong, Linh; Jagtap, Sandeep
The development of a decarbonised food industry will depend on a sustainable agricultural system where embodied food product greenhouse gas emissions (GHG) can be associated with agricultural production. The method presented demonstrates how mapping agri‐production can be used to calculate regional carbon footprints so GHG emission reduction is geographically strategic. Different agronomic and husbandry outcomes are mapped using Geographic Information Systems (GIS's) and carbon footprints are calculated using Life Cycle Inventory (LCI) libraries. The hybridised GIS‐LCI approach reports unique insights for decarbonisation, demonstrating how farming practices can be further integrated to best deliver food security. We use the GIS‐LCI method to show; (1), geography limits crop and livestock production types; (2), agri‐product density data can be used to calculate a food system carbon footprint; and (3), GIS's can be used to focus food policy for sustainability.
Open Access
Manufacturing thick laminates using a layer by layer curing approach
(Elsevier BV, 2024-12-01) Sun, Xiaochuan; Cook, Lawrence; Belnoue, Jonathan P-H.; Tifkitsis, Kostas I.; Kratz, James; Skordos, Alex A.
The work presented in this paper puts forward a manufacturing strategy for the processing of thermosetting composites based on Layer by Layer (LbL) curing. The process operates additively with sublaminates placed in a heated press, partially cured while consolidating, followed by loading of the next sublaminate and repeating the cycle until part completion. Coupled consolidation-cure simulation was utilised to design the process and establish its capabilities showing that halving the cure time is possible for thick parts. Mechanical testing showed that for pre-cure of placed layers below the gelation degree of cure, interlaminar properties are equivalent to those of conventionally manufactured material. A trial was carried out demonstrating successfully the LbL process. On-line measurements of temperature and compaction matched the predictions of the simulation, whilst the quality of the material produced is equivalent to that of conventionally produced composites.
Open Access
Landing gear health assessment: synergising flight data analysis with theoretical prognostics in a hybrid assessment approach
(PHM Society, 2024-06-27) El Mir, Haroun; King, Stephen; Skote, Martin; Alam, Mushfiqul; Place, Simon
This study addresses a critical shortfall in aircraft landing gear (LG) maintenance: the challenge of detecting degradation that necessitates intervention between scheduled maintenance intervals, particularly in the absence of hard landings. To address this issue, we introduce a Performance Degradation Metric (PDM) utilising Flight Data Recorder (FDR) output during the touchdown and initial roll phases of landing. This metric correlates time-series accelerometer data from a Saab 340B aircraft’s onboard sensors with non-linear response dynamic models that predict expected LG travel and reaction profiles across a set of ground contact cycles within a single landing. This facilitates the early detection of deviations from standard LG response behaviour, pinpointing potential performance abnormalities. The initiator of this approach is the Landing Sequence Typology, which systematically decomposes each aircraft landing into successive dynamic periods defined by their representative boundary conditions. What follows is the setting of initial parameters for the ordinary differential equations (ODE)s of motion that determine the orientation and impact responses of the most critical components of the LG assembly. Solving these ODEs with the integration of a non-linear representation of an oleo-pneumatic shock absorber model compliant with CS25 aircraft standards produces anticipated profiles of LG travel based on factors such as aircraft weight and speed at touchdown, which are subsequently cross-referenced with real accelerometer data, enhanced by video footage analysis. This footage is crucial for verifying the sequence of LG touchdowns and corresponding accelerometer outputs, thereby bolstering the precision of our analysis. Upon the conclusion of this study, by facilitating the early identification of LG performance deviations in specific landing scenarios, this diagnostic tool shall enable timely maintenance interventions. This proactive approach not only mitigates the risk of damage escalation to other components but also transitions main LG maintenance practices from reactive to proactive.
Open Access
Developing a stackable programme based on the advanced air mobility systems MSc course
(Elsevier BV, 2024-09-05) Zhao, Junjie; Gong, Tingyu; Nnamani, Christantus; Conrad, Christopher; Fremond, Rodolphe; Tang, Yiwen; Xu, Yan; Tsourdos, Antonios
This study proposes the development of content and materials for a stackable programme that aligns with the existing Cranfield University Advanced Air Mobility Systems (AAMS) MSc Course and integrates with ongoing Future Flight Challenge (FFC) projects, emerging research and development (R&D) capacities, and the growing demand for skilled professionals in the sector. The programme is structured into four phases: enhancement of taught modules through technology-enhanced teaching (TET), enrichment of project-based learning, bolstering of student experience and career development, and a stackable approach adaptable to various educational levels. This approach was evaluated using courses from the 2022/23 and 2023/24 academic years.
Open Access
Development of multi aluminium foam-filled crash box systems to improve crashworthiness performance of road Service vehicle
(Elsevier, 2025-01) De Biasio, Antony; Ghasemnejad, Hessam; Srimanosaowapak, S; Watson, JW
Honeycomb crash absorbers are known as mechanical energy-absorbing systems in both automotive and aerospace industries. However, the gap of knowledge in the transverse impacts of multi-foam-filled or stiffener-reinforced honeycombs is still unfilled. This paper investigates the energy absorption process in large crash boxes applied onto a road maintenance vehicle, exploring four aluminium honeycomb absorbers with design factors like added aluminium foam, corrugated sheet thicknesses, and stiffener reinforcements. The optimised foam-filled honeycomb structures are analysed for four crash scenarios in two different directions; frontal impact (T-direction) and lateral impact (L-direction) subjected to 50 km/h crash speed. The objective of this research is to identify the most efficient design that achieves a maximum acceleration of up to 20g while absorbing a specific energy of 145 kJ. The FE models were developed in ABAQUS to explore various scenarios related to damage zones, impact energy capabilities, and multi-foam-filled crash boxes. Finally, the lightest design of honeycomb absorbers which can maximise energy absorption while maintaining acceleration below the specified threshold of 20g will be recommended.
Open Access
Fundamental challenges and complexities of damage identification from dynamic response in plate structures
(MDPI AG, 2024-09-12) Alshammari, Yousef Lafi A.; He, Feiyang; Alrwili, Abdullah Ayed; Khan, Muhammad
For many years, structural health monitoring (SHM) has held significant importance across diverse engineering sectors. The main aim of SHM is to assess the health status and understand distinct features of structures by analyzing real-time data from physical measurements. The dynamic response (DR) is a significant tool in SHM studies. This response is used primarily to detect variations or damage by examining the vibration signals of DR. Numerous scholarly articles and reviews have discussed the phenomenon and importance of using DR to predict damages in uniform thickness (UT) plate structures. However, previous reviews have predominantly focused on the UT plates, neglecting the equally important varying thickness (VT) plate structures. Given the significance of VT plates, especially for academic researchers, it is essential to compile a comprehensive review that covers the vibration of both the UT and VT cracked plate structures and their identification methods, with a special emphasis on VT plates. VT plates are particularly significant due to their application in critical components of various applications where optimizing the weight, aerodynamics, and dimensions is crucial to meet specific design specifications. Furthermore, this review critically evaluates the damage identification methods, focusing on their accuracy and applicability in real-world applications. This review revealed that current research studies are inadequate in describing crack path identification; they have primarily focused on predicting the quantification of cracks in terms of size or possible location. Identifying the crack path is crucial to avoid catastrophic failures, especially in scenarios where the crack may propagate in critical dimensions of the plate. Therefore, it can be concluded that an accurate analytical and empirical study of crack path and damage identification in these plates would be a novel and significant contribution to the academic field.
Open Access
Potential of non-contact dynamic response measurements for predicting small size or hidden damages in highly damped structures
(MDPI, 2024-09-10) Azouz, Zakrya; Honarvar Shakibaei Asli, Barmak; Khan, Muhammad
Vibration-based structural health monitoring (SHM) is essential for evaluating structural integrity. Traditional methods using contact vibration sensors like accelerometers have limitations in accessibility, coverage, and impact on structural dynamics. Recent digital advancements offer new solutions through high-speed camera-based measurements. This study explores how camera settings (speed and resolution) influence the accuracy of dynamic response measurements for detecting small cracks in damped cantilever beams. Different beam thicknesses affect damping, altering dynamic response parameters such as frequency and amplitude, which are crucial for damage quantification. Experiments were conducted on 3D-printed Acrylonitrile Butadiene Styrene (ABS) cantilever beams with varying crack depth ratios from 0% to 60% of the beam thickness. The study utilised the Canny edge detection technique and Fast Fourier Transform to analyse vibration behaviour captured by cameras at different settings. The results show an optimal set of camera resolutions and frame rates for accurately capturing dynamic responses. Empirical models based on four image resolutions were validated against experimental data, achieving over 98% accuracy for predicting the natural frequency and around 90% for resonance amplitude. The optimal frame rate for measuring natural frequency and amplitude was found to be 2.4 times the beam’s natural frequency. The findings provide a method for damage assessment by establishing a relationship between crack depth, beam thickness, and damping ratio.
Open Access
Advancements in 3D x-ray imaging: development and application of a twin robot system
(Brunel University, 2024-08-31) Asif, Seemal; Hryshchenko Sumina, Yuliya; Holden, Martin; Contino, Matteo; Adiuku, Ndidiamaka; Hughes, Bryn; Plastropoulos, Angelos; Avdelidis, Nico; Webb, Phil
development of a novel twin robot system for 3D X-ray imaging integrates advanced robotic control with mobile X-ray technology to significantly enhance diagnostic accuracy and efficiency in both medical and industrial applications. Key technical aspects, including innovative design specifications and system architecture, are discussed in detail. The twin robots operate in tandem, providing comprehensive imaging capabilities with high precision. This novel approach offers potential applications ranging from medical diagnostics to industrial inspections, significantly improving over traditional imaging methods. Preliminary results demonstrate the system's effectiveness in producing detailed 3D images, underscoring its potential for wide-ranging uses. Future research will focus on optimizing image quality and automating the imaging process to increase utility and efficiency. This development signifies a step forward in integrating robotics and imaging technology, promising enhanced outcomes in various fields.
Open Access
Human facial emotion recognition for adaptive human robot collaboration in manufacturing
(Brunel University, 2024-08-31) Khan, Fahad; Asif, Seemal; Webb, Phil
The integration of robots into various industries, including manufacturing, has introduced new challenges in achieving efficient human-robot collaboration. A crucial aspect of successful collaboration is the ability of robots to understand and respond to human emotions. In the context of human-robot collaboration in manufacturing, accurately predicting human emotions is essential for enhancing efficiency and safety. This paper presents a setup for human emotion detection, focusing on facial emotion recognition. The proposed model and descriptive summary involve the utilising state-of-the-art algorithms such as AlexNet, HaarCascade (HCC), MTCNN (Multi-Task Cascaded Convolutional Neural Networks), and SVM (Support Vector Machine), applied to datasets like CK+, JAFFE, and AffectNet. The performance of each facial recognition model is evaluated in real-time scenarios, resulting in significant progress with an accuracy improvement from 40% to 78.1%. These results demonstrate the effectiveness of the approach in enabling adaptive robot control based on human emotions and enhancing collaboration quality. This research uniquely integrates facial emotion recognition and robot control to enable adaptive responses during human-robot collaboration in manufacturing settings. By understanding and responding to human emotions, robots can improve their interactions with humans, leading to increased productivity and improved overall collaboration efficiency.
Open Access
A risk assessment method for mid-air collisions in urban air mobility operations
(Institute of Electrical and Electronics Engineers (IEEE), 2024-12-31) Su, Yu; Xu, Yan
This paper proposes a method to systematically assess the risk of mid-air collisions in Urban Air Mobility (UAM) operations, considering unique flight characteristics, mission requirements, and the evolving airspace dynamics. The method encompasses three pivotal phases: the encounter leading to collision, the loss of control post-collision, and the resulting harm to third parties on the ground or in the air. Instead of focusing solely on the collision risk, this method quantifies potential harms, introducing the metric of “fatalities per flight hour” akin to conventional aviation. Three main barriers, strategic mitigation, tactical mitigation, and collision avoidance, are modelled to calculate the probability of mid-air collisions. The gas model evaluates the probability of strategic mitigation failure, while an encounter timeline concept determines the probability of tactical mitigation failure. This paper concludes with Monte Carlo simulations validating the proposed model and a real-world case study demonstrating its applicability for regulators, operators, and stakeholders in ensuring the safety and efficiency of future UAM operations.
Open Access
Knowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing
(Informa UK Limited, 2024-09-05) Qin, Jian; Taraphdar, Pradeeptta; Sun, Yongle; Wainwright, James; Lai, Wai Jun; Feng, Shuo; Ding, Jialuo; Williams, Stewart
Directed energy deposition additive manufacturing (DED-AM) has gained significant interest in producing large-scale metallic structural components. In this paper, a knowledge-based machine learning (ML) approach, combining both physics-based simulation and data-driven modelling, is proposed for a study on thermal variables of DED-AM. This approach enables both forward and backward predictions, which breaks down the barriers between the basic process parameters and key process attributes. Process knowledge plays a critical role to enable the prediction and enhance the accuracy in both prediction directions. The proposed ML approach successfully predicted the thermal variables of wire arc based DED-AM for forward modelling and the process parameters for backward modelling, typically within 7% errors. This approach can be further generalised as a powerful modelling tool for design, control, and evaluation of DED-AM processes regarding build geometry and properties, as well as an essential constituent element in a digital twin of a DED-AM system.
Open Access
Organizational culture enabler and inhibitor factors for ambidextrous innovation
(MDPI AG, 2024-09-06) Al Saied, Mohammad; McLaughlin, Patrick
Ambidextrous innovation is considered to be a key framework for innovation that offers organizations the ability to maintain their current level of competitiveness and develop and sustain a long-term competitive advantage. However, the implementation of ambidextrous innovation is constrained by an organization’s culture. Thus, the aim and objective of the present research are to explore the literature deeply and attempt to understand both organizational culture and ambidextrous innovation, along with key cultural aspects with regard to ambidexterity. The present research deeply dived into the model of organizational culture and attempted to build synergy between each model with respect to ambidexterity. The results of the present research suggest that Cameron and Quinn’s competing value framework, once amalgamated with the Schein model, creates an organizational culture framework that can be used to develop a culture that is best suited to the implementation of ambidextrous innovation. The Schein model provides a comprehensive guideline for each value of the competing value framework. Further, the present research also extracted key insights with regard to the role culture can play in innovation in general and ambidextrous innovation in particular. Finally, the present research also attempted to build a list of culture enablers and inhibitors that can facilitate and impede the process of ambidextrous innovation.
Open Access
Transformation of the product lifecycle value chain towards industry 5.0
(Elsevier BV, 2024-06) Xia, Hanbing; Li, Jiahong; Milisavljevic-Syed, Jelena; Salonitis, Konstantinos
The transition from Industry 4.0 to Industry 5.0 has broadened the focus of enterprises, moving beyond their organisational boundaries limits to embrace the interconnected structure within the product lifecycle value chain. Despite this shift, there remains a significant gap in research on the framework for transforming the product lifecycle value chain from Industry 4.0 to Industry 5.0. Thus, building upon the achievements of Industry 4.0, the proposed value chain transformation frameworks are proposed, which can improve sustainability and resilience while considering human needs. The frameworks offer enterprises a comprehensive understanding of the structure and strategic approach required for the Industry 5.0 product lifecycle value chain transformation. Finally, the authors summarise six research challenges and opportunities and eleven research questions to advance the transformation of the Industry 5.0 product lifecycle value chain.
Open Access
Efficient and near-optimal global path planning for AGVs: a DNN-based double closed-loop approach with guarantee mechanism
(Institute of Electrical and Electronics Engineers (IEEE), 2024) Zhang, Runda; Chai, Runqi; Chen, Kaiyuan; Zhang, Jinning; Chai, Senchun; Xia, Yuanqing; Tsourdos, Antonios
In this article, a novel global path planning approach with rapid convergence properties for autonomous ground vehicles (AGVs) named neural sampling rapidly exploring random tree (NS-RRT*) is proposed. This approach has a three-layer structure to obtain a feasible and near-optimal path. The first layer is the data collection stage. Utilizing the target area adaptive rapidly exploring random tree (TAA-RRT*) algorithm to establish a collection of paths considering the initial noise disturbance. To enhance network generalization, an optimal path backward generation (OPBG) strategy is introduced to augment the dataset size. In the second layer, the deep neural network (DNN) is trained to learn the relationships between the states and the sampling strategies. In the third layer, the trained model is used to guide RRT* sampling, and an efficient guarantee mechanism is also designed to ensure the feasibility of the planning task. The proposed algorithm can assist the RRT* algorithm in efficiently obtaining optimal or near-optimal strategies, significantly enhancing search efficiency. Numerical results and experiments are executed to demonstrate the feasibility and efficiency of the proposed method.