Despite the importance of product repairability, current methods for assessing and grading repairability are limited, which hampers the efforts of designers, remanufacturers, original equipment manufacturers (OEMs), and repair shops. To improve the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning architectures such as ConvNeXt, GoogLeNet, ResNet50, and VGG16 to evaluate repairability scores. The second approach is an unsupervised learning framework that combines feature extraction and cluster learning to identify product design features and group devices with similar designs. It utilizes an oriented FAST and rotated BRIEF feature extractor (ORB) along with k-means clustering to extract features from teardown images and categorize products with similar designs. To demonstrate the application of these assessment approaches, smartphones are used as a case study. The results highlight the potential of artificial intelligence in developing an automated system for assessing and rating product repairability.
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The objective of this study is to provide an overview of Blockchain technology and Industry 4.0 for advancing supply chains towards sustainability. First, extracted from the existing literature, we evaluate the capabilities of Industry 4.0 for sustainability under three main topics of (1) Internet of things (IoT)-enabled energy management in smart factories; (2) smart logistics and transportation; and (3) smart business models. We expand beyond Industry 4.0 with unfolding the capabilities that Blockchain offers for increasing sustainability, under four main areas: (1) design of incentive mechanisms and tokenization to promote consumer green behavior; (2) enhance visibility across the entire product lifecycle; (3) increase systems efficiency while decreasing development and operational costs; and (4) foster sustainability monitoring and reporting performance across supply chain networks. Furthermore, Blockchain technology capabilities for contributing to social and environmental sustainability, research gaps, adversary effects of Blockchain, and future research directions are discussed.more » « less
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Prior studies have already predicted that enforcement of IP on the additive manufacturing industry will not be successful due to the widespread use of file-sharing technologies, similar to the entertainment and music industry. This paper discusses the capabilities of Blockchain technology for protecting IP in the design and manufacturing area. A conceptual framework for a digital platform is defined in this paper and further, a survey study of engineering design and manufacturing students has been conducted to identify the main motivation behind developing these platforms and the types of features that should be included in Blockchain-based IP platforms for asset protection, particularly for product design. In addition, respondents provided their opinions about the type of industry that might be affected more by the threat of counterfeiting products and the role of Blockchain-based IP systems on the growth and development of innovation.more » « less
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The potential of smart cities in remediating environmental problems in general and waste management, in particular, is an important question that needs to be investigated in academic research. Built on an integrative review of the literature, this study offers insights into the potential of smart cities and connected communities in facilitating waste management efforts. Shortcomings of existing waste management practices are highlighted and a conceptual framework for a centralized waste management system is proposed, where three interconnected elements are discussed: (1) an infrastructure for proper collection of product lifecycle data to facilitate full visibility throughout the entire lifespan of a product, (2) a set of new business models relied on product lifecycle data to prevent waste generation, and (3) an intelligent sensor-based infrastructure for proper upstream waste separation and on-time collection. The proposed framework highlights the value of product lifecycle data in reducing waste and enhancing waste recovery and the need for connecting waste management practices to the whole product lifecycle. An example of the use of tracking and data sharing technologies for investigating the waste management issues has been discussed. Finally, the success factors for implementing the proposed framework and some thoughts on future research directions have been discussed.more » « less
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This study used the unrealized potential of citizen science as an innovative educational tool with the aim of enhancing research and learning experience of students in several engineering design and manufacturing courses with a particular focus on sustainability-related topics. Citizen science has been employed as a data collection and educational tool in two engineering courses at the University at Buffalo in which students were tasked with reporting examples of good and bad designs they observe in their everyday life. The results revealed the significant potential of citizen scientists to report innovative and informative design and manufacturing ideas.more » « less
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Efficient disassembly operation is considered a promising approach toward waste reduction and End-of-Use (EOU) product recovery. However, many kinds of uncertainty exist during the product lifecycle which make disassembly decision a complicated process. The optimum disassembly sequence may vary at different milestones depending on the purpose of disassembly (repair, maintenance, reuse and recovery), product quality conditions and external factors such as consumer preference, and the market value of EOU components. A disassembly sequence which is optimum for one purpose may not be optimum in future life cycles or other purposes. Therefore, there is a need for incorporating the requirements of the entire product life-cycle when obtaining the optimum disassembly sequence. This paper applies a fuzzy method to quantify the probability that each feasible disassembly transition will be needed during the entire product lifecycle. Further, the probability values have been used in an optimization model to find the disassembly sequence with maximum likelihood. An example of vacuum cleaner is used to show how the proposed method can be applied to quantify different users’ evaluation on the relative importance of disassembly selection criteria as well as the probability of each disassembly operation.more » « less
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As electronic waste (e-waste) becomes one of the fastest growing environmental concerns, remanufacturing is considered as a promising solution. However, the profitability of take back systems is hampered by several factors including the lack of information on the quantity and timing of to-be-returned used products to a remanufacturing facility. Product design features, consumers’ awareness of recycling opportunities, socio-demographic information, peer pressure, and the tendency of customer to keep used items in storage are among contributing factors in increasing uncertainties in the waste stream. Predicting customer choice decisions on returning back used products, including both the time in which the customer will stop using the product and the end-of-use decisions (e.g. storage, resell, through away, and return to the waste stream) could help manufacturers have a better estimation of the return trend. The objective of this paper is to develop an Agent Based Simulation (ABS) model integrated with Discrete Choice Analysis (DCA) technique to predict consumer decisions on the End-of-Use (EOU) products. The proposed simulation tool aims at investigating the impact of design features, interaction among individual consumers and socio-demographic characteristics of end users on the number of returns. A numerical example of cellphone take-back system has been provided to show the application of the model.more » « less