Decision support for assessing the feasibility of a product for remanufacture

Remanufacturing is the process of restoring old, damaged and failed products to a condition as good as new . Whilst the practice of remanufacture has been conducted for almost a century, the attention it receives within mainstream business is increasing due to potential benefits associated with economic savings and reduced environmental impact. There are several challenges in operating a successful remanufacturing business, one of which is how to assess the feasibility of remanufacturing. Remanufacturing does not lend itself towards every product due to factors related to the product, process, market and business capabilities, therefore careful assessment should be conducted before taking on a remanufacturing endeavour. This thesis reports the research undertaken to aid decision makers assessing the feasibility of a product for remanufacture. The aim has therefore been to determine the requirements of assessing remanufacturing feasibility, then to develop a tool to support this activity. Requirements of the decision making process were established through a detailed review of the literature supplemented with additional interviews from remanufacturing businesses, whilst research gaps for support tools were identified through a systematic review of existing tools presented within academia. Through these reviews it was determined that current methods do not provide enough support in determining the impact of uncertainties found within remanufacturing against key assessment criteria, such as economic cost. Focus upon the tool development was therefore directed at estimating remanufacturing cost of a product under uncertain conditions. The tool was designed, utilising techniques such as Monte Carlo analysis, fuzzy sets and case based reasoning. A prototype of the tool was then implemented within an object oriented structure and deployed as web service. Testing and validation were conducted by demonstrating the functionality of the tool against a set of specification requirements, through two contrasting remanufacturing case studies identified within industry. In summary this research has developed a tool to support the assessment of remanufacturing viability through cost estimation under uncertain conditions, identifying requirements through a detailed literature review and interviews with industry and providing validation through two detailed case studies. The tool is novel in its ability to calculate both cost and the risk associated with the uncertainties present within the remanufacturing domain

Industry 4.0: product digital twins for remanufacturing decision-making

Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a product’s life cycle. The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The model’s architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an asset’s through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored

Factories of the Future

Engineering; Industrial engineering; Production engineerin

A Framework and Baseline for the Integration of a Sustainable Circular Economy in Offshore Wind

Circular economy and renewable energy infrastructure such as offshore wind farms are often assumed to be developed in synergy as part of sustainable transitions. Offshore wind is among the preferred technologies for low-carbon energy. Deployment is forecast to accelerate over ten times faster than onshore wind between 2021 and 2025, while the first generation of offshore wind turbines is about to be decommissioned. However, the growing scale of offshore wind brings new sustainability challenges. Many of the challenges are circular economy-related, such as increasing resource exploitation and competition and underdeveloped end-of-use solutions for decommissioned components and materials. However, circular economy is not yet commonly and systematically applied to offshore wind. Circular economy is a whole system approach aiming to make better use of products, components and materials throughout their consecutive lifecycles. The purpose of this study is to enable the integration of a sustainable circular economy into the design, development, operation and end-of-use management of offshore wind infrastructure. This will require a holistic overview of potential circular economy strategies that apply to offshore wind, because focus on no, or a subset of, circular solutions would open the sector to the risk of unintended consequences, such as replacing carbon impacts with water pollution, and short-term private cost savings with long-term bills for taxpayers. This study starts with a systematic review of circular economy and wind literature as a basis for the coproduction of a framework to embed a sustainable circular economy throughout the lifecycle of offshore wind energy infrastructure, resulting in eighteen strategies: design for circular economy, data and information, recertification, dematerialisation, waste prevention, modularisation, maintenance and repair, reuse and repurpose, refurbish and remanufacturing, lifetime extension, repowering, decommissioning, site recovery, disassembly, recycling, energy recovery, landfill and re-mining. An initial baseline review for each strategy is included. The application and transferability of the framework to other energy sectors, such as oil and gas and onshore wind, are discussed. This article concludes with an agenda for research and innovation and actions to take by industry and government

Enhancing remanufacturing automation using deep learning approach

In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces