A General Approach to Electrical Vehicle Battery Remanufacturing System Design

One of the major difficulties electrical vehicle (EV) industry facing today is the production and lifetime cost of battery packs. Studies show that using remanufactured batteries can dramatically lower the cost. The major difference between remanufacturing and traditional manufacturing is the supply and demand variabilities and uncertainties differences. The returned core for remanufacturing operations (supply side) can vary considerably in terms of the time of returns and the quality of returned products. On the other hand, because different contracts can be used to regulate suppliers, it is almost always assumed zero uncertainty and variability for traditional manufacturing systems. Similarly, customers demand traditional manufacturers to sell newly produced products in constant high quality. But, remanufacturers usually sell in aftermarket, and the quality of the products demanded can vary depends on the price range, usage, customer segment and many other factors. The key is to match supply and demand side variabilities so the overlapping between them can be maximized. Because of these differences, a new framework is needed for remanufacturing system design. This research aims at developing a new approach to use remanufactured battery packs to fulfill EV warranties and customer aftermarket demands and to match supply and demand side variabilities. First, a market lifetime EV battery return (supply side) forecasting method is develop, and it is validated using Monte Carlo simulation. Second, a discrete event simulation method is developed to estimate EV battery lifetime cost for both customer and manufacturer/remanufacturer. Third, a new remanufacturing business model and a simulation framework are developed so both the quality and quantity aspects of supply and demand can be altered and the lifetime cost for both customer and manufacturer/remanufacturer can be minimized. The business models and methodologies developed in this dissertation provide managerial insights to benefit both the manufacturer/remanufacturer and customers in EV industry. Many findings and methodologies can also be readily used in other remanufacturing settings. The effectiveness of the proposed models is illustrated and validated by case studies.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143955/1/xrliang_1.pd

Integration of mahalanobis-taguchi system and activity based costing in decision making for remanufacturing

Classifying components at the end of life (EOL) into remanufacture, repair or dispose is still a major concern to automotive industries. Prior to this study, no specific approach is reported as a guide line to determine critical crankpins that justifying economical remanufacturing process. Traditional cost accounting (TCA) has been used widely by remanufacturing industries but this is not a good measure of estimating the actual manufacturing costs per unit as compared to activity based costing (ABC). However, the application of ABC method in estimating remanufactured cost is rarely reported. These issues were handled separately without a proper integration to make remanufacturing decision which frequently results into uneconomical operating cost and finally the decision becomes less accurate. The aim of this work is to develop a suitable pattern recognition method for classifying crankshaft into three different EOL groups and subsequently evaluates the critical and non-critical crankpins of the used crankshaft using Mahalanobis-Taguchi System (MTS). A remanufacturability assessment technique was developed using Microsoft Excel spreadsheet on pattern recognition and critical crankpins evaluation, and finally integrates these information into a similar spreadsheet with ABC to make decision whether the crankshaft is to be remanufactured, repaired or disposed. The developed scatter diagram was able to recognize group pattern of EOL crankshaft which later was successfully used to determine critical crankpins required for remanufacturing process. The proposed method can serve as a useful approach to the remanufacturing industries for systematically evaluate and decide EOL components for further processing. Case study on six engine models, the result shows that three engines can be securely remanufactured at above 40% profit margin while another two engines are still viable to remanufacture but with less profit margin. In contrast, only two engines can be securely remanufactured due overcharge when using TCA. This inaccuracy affects significantly the overall remanufacturing activities and revenue of the industry. In conclusion, the proposed integration on pattern recognition, parameter evaluation and costing assists the decision making process to effectively remanufacture EOL automotive components as confirmed by Head of workshop of Motor Teknologi Industri Sdn. Bhd

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

Towards cleaner production: a roadmap for predicting product end-of-life costs at early design concept

The primary objective of the research was to investigate how disposal costs were being incurred in the domain of defence electronic systems by the Original Equipment Manufacturer (OEM) and subsequently to ascertain a novel approach to prediction of their end-of-life (EOL) costs. It is intended that the OEM could utilise this method as part of a full lifecycle cost analysis at the conceptual design stage. The cost model would also serve as a useful guide to aid decision making at the conceptual design stage, so that it may lead to the design of a more sustainable product in terms of recycling, refurbishment or remanufacture with the consideration of financial impact. The novelty of this research is that it identifies the significance of disposal costs from the viewpoint of the OEM and provides a generic basis for evaluation of all the major EOL defence electronic systems. A roadmap has been proposed and developed to facilitate the prediction of disposal costs and this will be used to determine a satisfactory solution of whether the EOL parts of a defence electronic system are viable to be remanufactured, refurbished or recycled from an early stage of a design concept. A selected defence electronic system is used as a case study. Based on the findings, the proposed method offers a manageable and realistic solution so that the OEM can estimate the cost of potential EOL recovery processes at the concept design stag

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

Cost Evaluation in Design for End-of-Life of Automotive Components

The European Union implemented the End-of-Life Vehicle directive to deal with an estimated 6 million end-of-life vehicles each year. Existing literature describe the processes to deal with the waste at end-of-life of different products but there is a lack of information on the costing of these options. These costs remain a concern to automotive manufacturers. This paper therefore reports the end-of-life costs of vehicle components and also demonstrates how these costs can be predicted at the design stage. The proposed approach should help to decide whether the automotive parts are viable for remanufacture, refurbishment, recycling, or disposal from an economic perspective. Two different automotive parts have been selected as case studies to validate the approach. Assumptions were made during the development of the technique and based on the results, the proposed approach could potentially provide vehicle manufacturers a method of estimating the cost of end-of-life recovery processes of vehicle components

Application of Mahalanobis-Taguchi System in Full Blood Count of Methadone Flexi Dispensing Program

Patient under methadone flexi dispensing (MFlex) program are required to do blood tests like full blood count (FBC). A doctor assesses 3 parameters like haemoglobin, platelet count, and fasting blood sugar to ensure the patient has FBC problem. Consequently, the existing system does not have a stable ecosystem towards classification and optimization. The objective is to apply Mahalanobis-Taguchi system (MTS) in the MFlex program. The data is collected at Bandar Pekan clinic with 34 parameters. Two types of MTS methods are used like RT-Method and T-Method for classification and optimization respectively. The average Mahalanobis distance (MD) of healthy is 1.0000 and unhealthy is 187.0555. Positive degree of contribution has 19 parameters. 15 unknown samples have been diagnosed. Type 5 of 6 modifications has been selected as the best proposed solution. In conclusion, a pharmacist from Bandar Pekan clinic confirmed that MTS able to solve problem in classification and optimization of MFlex program

A Review on the Lifecycle Strategies Enhancing Remanufacturing

Remanufacturing is a domain that has increasingly been exploited during recent years due to its numerous advantages and the increasing need for society to promote a circular economy leading to sustainability. Remanufacturing is one of the main end-of-life (EoL) options that can lead to a circular economy. There is therefore a strong need to prioritize this option over other available options at the end-of-life stage of a product because it is the only recovery option that maintains the same quality as that of a new product. This review focuses on the different lifecycle strategies that can help improve remanufacturing; in other words, the various strategies prior to, during or after the end-of-life of a product that can increase the chances of that product being remanufactured rather than being recycled or disposed of after its end-of-use. The emergence of the fourth industrial revolution, also known as industry 4.0 (I4.0), will help enhance data acquisition and sharing between different stages in the supply chain, as well boost smart remanufacturing techniques. This review examines how strategies like design for remanufacturing (DfRem), remaining useful life (RUL), product service system (PSS), closed-loop supply chain (CLSC), smart remanufacturing, EoL product collection and reverse logistics (RL) can enhance remanufacturing. We should bear in mind that not all products can be remanufactured, so other options are also considered. This review mainly focuses on products that can be remanufactured. For this review, we used 181 research papers from three databases; Science Direct, Web of Science and Scopus

Dynamic lifecycle cost modeling for adaptable design optimization of additively remanufactured aeroengine components

Additive manufacturing (AM) is being used increasingly for repair and remanufacturing of aeroengine components. This enables the consideration of a design margin approach to satisfy changing requirements, in which component lifespan can be optimized for different lifecycle scenarios. This paradigm requires lifecycle cost (LCC) modeling; however, the LCC models available in the literature consider mostly the manufacturing of a component, not its repair or remanufacturing. There is thus a need for an LCC model that can consider AM for repair/remanufacturing to quantify corresponding costs and benefits. This paper presents a dynamic LCC model that estimates cumulative costs over the in-service phase and a nested design optimization problem formulation that determines the optimal component lifespan range to minimize overall cost while maximizing performance. The developed methodology is demonstrated by means of an aeroengine turbine rear structure