12th International Conference on Through-life Engineering Services 2024
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Welcome to the TESConf2024
We are delighted to announce the 12th International Conference on Through-life Engineering Services 2024 (TESConf2024), to be held 6-7 June 2024 at Cranfield University, UK.Browse
Recent Submissions
Item Open Access A sustainability-based framework for predicting the remaining useful life of a complex engineering asset(Cranfield University, 2024-06-07) Venkata Subhadua, Vaishnav; Ali, Zain; Farsi, Maryam; Norton, AndyAs climate change became recognised as a major global challenge, the ability to define and account for the environmental performance of an asset became an important attribute aiding the sustainable development strategies towards net-zero. Remaining Useful Life (RUL) indicator allows for optimised maintenance scheduling and the life extension of an asset. However, the existing RUL prediction methods do not fully consider the environmental performance (EP) of an asset. This paper aims to develop a sustainability-based framework for complex engineering assets’ RUL prediction based on a systematic review of key literature. The proposed framework introduces a new concept, so-called ‘sustainable-RUL’ (SRUL), which refers to the estimated remaining lifetime that an item is able to function reliably and be environmentally sustainable. The Scopus database is used to develop the PRISMA framework. Finally, a generic S-RUL framework is introduced which incorporates the environmental sustainability aspect into the RUL prediction. Hence, the decision-maker is provided with a single predictive indicator, that accounts for the asset reliability and EP at the same level of granularity, thus facilitating the selection of maintenance policies that establishes a condition for ecological and economic stability.Item Open Access Knowledge-graph based approach for automated selection of spare parts suitable for additive manufacturing: a railway use-case(Cranfield University, 2024-06-07) Madreiter, Theresa; Besinger, Philipp; Archila, Sebastian; Kohl, Linus; Ansari, FazelSpare part inventory management (SPIM) in the railway sector highly demands reliability and transparency for decentralized inventory control. Optimal SPIM should ensure the availability of needed spare parts for a service request, considering the frequency of use and criticality criterion for effective maintenance. Additive manufacturing (AM) technologies enable costeffective production of small batch sizes often required for spare parts. However, critical component-specific information is often unstructured within engineering drawings (ED), making digital processing, and linking to existing data from enterprise resource planning (ERP) and maintenance management systems difficult. To ensure effective maintenance logistics, this paper introduces a knowledge graph (KG) that can facilitate i) interlinking multiple sources through data integration and ii) establishing a semantic data hub, thus serving as a backbone for automated assessment of component's suitability for AM. The proposed KG-based approach merges relevant (existing) ontologies, multi-structured data from ED, ERP system information, and external data sources. The approach is developed and evaluated in real-world use-cases in cooperation with the Austrian railway and public transit industry.Item Open Access Integrating models for calculating component sustainability metrics(Cranfield University, 2024-06-07) Kirkwood, Leigh; Abu-Monshar, Anees; Norman, Beth; Crook, Robert; Barlow, Ed; Hayes, Cai; Rybicka, JustynaThis paper reports on the development of models for calculating sustainability metrics at a per part level, developed for the LEAD factory project. While many organisations collect site level data for sustainability, there is a notable lack of support to calculate at a per part level. This scope difference requires different methods, but part level information can aid organisations in making production changes to achieve sustainability KPI’s. A cradle-to-gate approach was used that links raw-material, the transportation of materials and details on the production processes. To achieve this, a toolset of different models was designed and built to address key activities in the value-chain, to both support potentially independent analysis of just that value-chain link or the more complete cradle-to-gate analysis. Integration of model outputs and planning of information flow within the toolset is the primary focus of this paper. This is part 1 of a 2 part paper: this paper focuses on how the models were integrated and the design of the wider toolset. Part 2 focuses on the benchmarking using the model-set and comparing the “JENI” system developed as part of the LEAD factory project.Item Open Access Infrared thermography- a study into its current capabilities and future prospects(Cranfield University, 2024-06-07) Ali, Zain; Peng, Shaoyang; Addepalli, Sri; Zhao, YifanThe fourth industrial revolution has brought forward a paradigm shift in the analyses and interpretation of data. Emerging technologies such as artificial intelligence, Internet of Things (IoT), and cyber physical systems have accelerated the process of concepts such as decentralised decision making, automation, and digitalisation. In the context of Non-destructive Evaluation (NDE), adopting these technologies has substantially improved the efficiency of existing techniques. Currently, Ultrasound Testing (UT) has been dubbed as the “gold standard” for Non-destructive Testing (NDT). However, a major drawback to this technique is its contact-based inspection. Infrared Thermography (IRT) on the other hand offers a non-contact non-intrusive inspection and is a growing area of interest to researchers. This paper explores the impact of infrared thermography within the maintenance industry. Firstly, the current state-of-the-art in IRT is presented followed by the limitations of the technique, the current research and knowledge gap that exists in thermographic testing. Potential solutions that can overcome the limitations are proposed. These cover specific aspects of the technique such as the working principle, mathematical modelling, data interpretation and processing, automation, and digitalisation. Finally, future prospects of the technique are briefly presented.Item Open Access Implementation and demonstration of autonomous ultrasonic track inspection using cloud-based AI rail flaw analyzer(Cranfield University, 2024-06-07) He, Feiyang; Durazo Cardenas, Isidro; Li, Jian; Ruiz Carcel, Cristobal; Ishola, Ademayowa; Starr, Andrew; Anderson, Robert; Price, RichardThis research successfully demonstrated autonomous rail inspection feasibility up to Technology Readiness Level (TRL) 7. A prototype integrating an autonomous rail vehicle and Sperry's Ultrasound Testing (UT) system was developed at Cranfield University. It was first tested at Cranfield’s Railways Innovation Test Area (RITA) at TRL 5 and tested at heritage operational railway, in Idridgehay, Derbyshire, UK achieving TRL 7. Experimental works included a 15-meter track test at RITA and nine rounds demonstration of a 250-meter track inspection at Idridgehay, showcasing inspection, localization, navigation accuracy, and defect location precision. The prototype successfully detected artificial rail defect during the demonstration and promptly communicated to command centre via email. We characterised the vehicle performance by measuring the positional error and detection rate. The positional accuracy measurements, verified through GPS and odometry, revealed an odometry-based error of 0.27-3.2 metres and an 8-metre GPS-associated error. The absence of differential GPS and a data fusion approach contributing to these errors. In addition, Weak 4G signal coverage in the fields impacted operator-vehicle communication and data uploading. Future iterations should address these limitations, exploring alternatives for enhanced accuracy and advancing defect-sizing technology.Item Open Access A survey framework for setting an Augmented Reality (AR) roadmap in rolling stock organizations(Cranfield University, 2024-06-07) Scheffer, Sara; Abbas YawarIn anticipation of Industry 4.0’s significant impact on future maintenance systems, Augmented Reality (AR) emerges as a key technology. Even though AR has proven to be mature and applicable in maintenance practices, its practical implementation and challenges faced by organizations remain understudied. The integration of AR into organizational systems poses a crucial challenge, necessitating novel maintenance strategies, interdepartmental collaboration, and clearly defined goals. This research addresses the impact of AR on organizations, emphasizing the need for organizational readiness and adaption in maintenance processes, AR technology adoption, and IT infrastructure integration. This research demonstrates the development of an AR roadmap for rolling stock organizations through the application of a comprehensive survey framework. The study is based on a dual-pronged approach, incorporating a maturity and readiness assessment to ascertain the current state of AR adoption and organizational readiness. The survey framework systematically evaluates critical aspects such as existing technology infrastructure, operator skills, challenges faced, and desired organizational outcomes. The survey findings contribute to developing a tailored AR roadmap that addresses the specific needs and challenges of rolling stock maintenance operations. This research aims to enhance AR integration in rolling stock organizations and provides a practical guide for effective implementation.Item Open Access A review of simulation modelling approaches in aviation spare parts inventory optimisation(Cranfield University, 2024-06-07) bin Mohammud, Zaki; Fan, Ip-Shing; Avdelidis, Nicolas P.Aviation spare parts are expensive and are being kept as a buffer for unscheduled and scheduled maintenance activities. Apart from cash flow being locked in the inventory, spare parts for aircraft or helicopters are also critical in the continuous operations of air assets. In addition, the holding cost is roughly 20 per cent of the total inventory value. Holding costs are costs such as insurance, utilities and manpower. Minimising the total inventory value could be done by adopting a lower inventory count through various methods, such as the provision of spare parts, which can be done either by forecasting the failure of components or by using new maintenance methodologies, such as predictive maintenance. The methods have been used widely in the aviation industry for a long time. The upward trend of papers published from 1963 to 2023 shows that aviation spare parts optimisation is still being discussed. This paper reviews the simulation modelling approaches to optimise aviation spare inventory. 221 papers were reviewed from Scopus and Web of Science (WoS) literature databases, and 17 papers from 1982 to 2023 were chosen based on the simulation modelling approach, such as System Dynamics and Discrete-Event Simulation. The papers were classified according to simulation modelling techniques, spare parts and operations classification, and challenges and opportunities.Item Open Access Medium entropy alloys for biomedical applications(Cranfield University, 2024-06-07) Zhang, Jiacheng; Stiehler, Martin; Syed, Adnan; Jorge Jr, Alberto Moreira; Jolly, Mark; Georgarakis, KonstantinosHigh entropy alloys (HEAs) is a rapidly emerging class of metallic materials consisting of four or more elements in equimolar or quasi-equimolar compositions. These alloys often have simple crystal structures and tailorable properties attracting significant interest for different applications. Common metallic materials for orthopaedic and dental implants include stainless steel, Co-Cr and Ti- alloys. Although these materials are widely in use, issues relevant to biocompatibility and suspected toxicity and the elastic modulus mismatch compared to that of hard tissue have been raised in recent years. High entropy alloys specifically designed for bio-medical applications can offer solutions to overcome these limitations. Bio-HEAs have emerged in the last couple of years and currently receive increasing scientific attention. In this work, we discuss on the design of new entropic alloys using only non-toxic elements such as Ti, Zr, Nb, Ta and Mo. We use a systematic approach to investigate the effect of additional elements on the microstructure and properties of the alloys starting from the binary Ti-Nb and extending to the ternary Ti-Zr-Nb, the quaternary Ti-Zr-Nb-Ta and the Ti-Zr-Nb-Ta-Mo alloy. The alloy design is building on previous work on beta Ti- alloys which has shown promising trends for reducing the elastic modulus of implant materials. The alloys were produced by arc-melting and suction casting under Ar inert atmosphere. X-ray diffraction, and scanning electron microscopy were employed to reveal their crystal structure and microstructure. respectively. The developed alloys exhibit BCC crystal structure and a dendritic microstructure in their as-cast condition. The addition of Zr and Mo was found to increase the hardness of the alloys.Item Open Access Simulation of a Cyber-Physical Lifecycle System based on a blockchain platform(Cranfield University, 2024-06-07) Izumida, Yuto; Hada, Chihiro; Fukushige, ShinichiProduct lifecycle management plays a crucial role in establishing resource circulation systems based on artifact stocks accumulated in society. However, given the involvement of diverse stakeholders, managing the complex circular systems of a product lifecycle requires advanced digital technologies. The concept of a cyber-physical lifecycle system (CPLS) is proposed in this paper. CPLS constructs the digital twin of a product lifecycle to be managed. The digital model in the cyberspace is used for analysis, simulation, and reconfiguration of the lifecycle based on real data acquired from the physical space of the CPLS. In this research, we introduce blockchain technology in the CPLS to aggregate information on target products and processes from each stakeholder involved in the lifecycle. To support decision-making in lifecycle management, a life cycle simulation (LCS) is conducted in the cyberspace of the CPLS based on the acquired data through blockchain. Subsequently, decisions are made based on the simulation results to increase the profit of each stakeholder and minimize the environmental loads of the entire lifecycle. In a case study focusing on the life cycle management of electric motors, we conducted a simulation-based analysis on the prototype of CPLS. A twin-experiment for the case showed the effectiveness of production management using the CPLS from economic and environmental perspectives.Item Open Access Causal AI-powered Digital Product Passports for enabling a circular and sustainable manufacturing ecosystem(Cranfield University, 2024-06-07) Ompusunggua, Agusmian P.; Tjahjowidodo, Tegoeh; Wicaksano, H.Digital product passport (DPP) has been recently introduced by policymakers (e.g., the European Commission) to advance sustainable business practices towards a circular economy (CE). As a newly introduced concept, DPP is still relatively high-level and vague. Therefore, its definition, information flow architecture, what relevant information needs to be stored, and how to use such information in the context of a circular and sustainable manufacturing ecosystem, etc., are still open research questions. This paper addresses these research questions by proposing a novel conceptual framework for DPP, facilitating seamless information exchanges among CE stakeholders, and providing a transparent and trustworthy basis for decision-making. Causal AI utilisation is proposed to extract causal relationships among sustainability/circularity KPIs comprehensively, encompassing raw material supply chain, circularity-compliant product design, manufacturing optimisation on the shop floor, and after-sale product usage optimisation. Seamless information exchange will be achieved through semantic interoperability and a comprehensive model of the whole supply chain by employing an ontology model. The causal AI approach is proposed to identify causalities among KPIs and other factors to predict environmental impacts. This way, a causal model integrating domain expert knowledge and causality discovered from measured data will increase the transparency/explainability of prediction/decision made by machine learning algorithms.