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ردیف | عنوان | نوع |
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1 |
The applications of Internet of Things in the automotive industry: A review of the batteries, fuel cells, and engines
کاربردهای اینترنت اشیا در صنعت خودرو: مروری بر باتری ها، سلول های سوختی و موتورها-2022 The current advances in the integration of devices through the internet of things (IoT) have
encouraged researchers to focus on the applications of IoT in the automotive industry. Although
different achievements in the in-vehicle network analysis and traffic management have been
already reviewed, a comprehensive study to bring together the main applications of the IoT in the
automotive industry is required. Internal combustion engines (ICEs) are established as the most
common prime-mover for cars, however, with the depleting fossil-fuel resources, the interest in
the usage of fuel cells and batteries has increased. In this regard, the main goal of the current
study is to evaluate the application of IoT in batteries, fuel cells, and ICEs. This paper is also
centralized on different types of IoT applications and combines them with empirical articles such
as Random Location Detection, Vehicle Theft Prevention, Observation of vehicle performance,
and industrial management of vehicles. As an output of this comprehensive review, different
usages of the IoT in the automotive sector will be clarified. Also, this article can be considered as a
basis for advancing the recent implementation of the IoT in the fuel cell, battery, and ICE
domains.
keywords: اینترنت اشیا (IoT) | باتری | سلول سوختی | موتور احتراق داخلی (ICE) | Internet of Things (IoT) | Battery | Fuel cell | Internal combustion engine (ICE) |
مقاله انگلیسی |
2 |
A review and perspectives on predicting the performance and durability of electrical contacts in automotive applications
بررسی و دیدگاههایی در مورد پیشبینی عملکرد و دوام کنتاکتهای الکتریکی در کاربردهای خودرو-2021 This review reports the recent progress in predicting the performance and long-term durability of
electrical connectors in the automotive industry. The review features a short introduction to
electrical contacts as well as the validation process before product launch, followed by a study of
fretting wear and the latest mathematical models describing this phenomenon. We discuss approaches to numerical modeling in the micro- and macro-scale, including the identification of the
most promising research approaches to allow durability prediction of an electrical connector.
Finally, we address some gaps in the research which require further investigation. This would
allow further development of numerical models enabling the prediction of automotive connector
durability with regard to its electrical and mechanical performance, and hence, the performance
of the entire wire harness.
Keywords: Fretting | Modeling and simulation | Numerical modeling | Mechanics of materials | Electrical and electronic engineering | Modeling of degradation | LSR aging |
مقاله انگلیسی |
3 |
Exploring the feasibility of introducing electric freight vehicles in the short food supply chain: A multi-stakeholder approach
بررسی امکان معرفی وسایل نقلیه باری الکتریکی در زنجیره تأمین مواد غذایی کوتاه: یک رویکرد چند ذی نفع-2021 The transition towards more sustainable approaches in the Food Supply Chain was concretely visible in the implementation of alternative models, like the Short Food Supply Chain. Some authors raise doubts on the environmental impact of this model, in particular for the externalities caused by the transport system, suggesting the adoption of Electric Freight vehicles. By adopting a multi-stakeholder approach, the objective of this study is to explore both the barriers and potentialities involved in the adoption of Electric Freight vehicles in the Short Food Supply Chain, and the existence of a shared strategy at the system level able to foster their adoption. Results suggest that, for entrepreneurs, Electric Freight vehicles appear as a viable option, although more efforts are still needed at a governmental level, through the promotion of public measures in the form of support for purchasing costs or rental rate and offering technical expertise services. In terms of infrastructures, as is clear from interviews, improving the charging infrastructure efficiency to ensure EFVs shift optimization and increasing the number of charging points are today a priority. On the whole, more collaborative methods should be inaugurated, contributing to a shared vision of urban mobility which takes into account all supply chain actors (charging point operators, automotive industry, rental car services, farmers, and local authorities) to ensure the system works in a more efficient way. Keywords: Electric Fright Transport | Short Food Supply Chain | Last Miles food | System Innovation | Case study | Sicily |
مقاله انگلیسی |
4 |
Deep learning computer vision for the separation of Cast- and Wrought-Aluminum scrap
یادگیری عمیق بینایی ماشین برای جداسازی ضایعات آلومینیوم ریخته گری و فرفورژه-2021 In consequence of the electrification and the increased adoption of lightweight structures in the automotive industry, global demand for wrought Aluminum (Al) is expected to rise while demand for cast Al will stagnate. Since cast alloys can only be converted to wrought alloys by energy-intensive processes, the most promising strategy to avoid the emergence of excess Al cast alloys scrap is to sort cast from wrought alloys. To date, the separation of complex mixes of non-ferrous metals often implies the use of either or both sink-float techniques and/or X-ray fluorescence (XRF) based sorting. Therefore, the presented research develops an efficient method to classify cast and wrought (C&W) alloys in a real-time system with a conveyor belt using transfer learning methods, such as fine-tuning and feature extraction. Five CNNs are evaluated to classify C&W alloys using colour and depth images and transfer learning methods. In addition, the early fusion and late fusion of colour and depth images of C&W Al are investigated. For early fusion, data is added as an extra input channel to the first convolution layer of the CNN, and for later fusion, the images are fed in two separate subnetworks with the same architecture, where the parameters of the fully-connected layers are concatenated in both subnetworks. Our approach shows that late fusion CNN DenseNet allows obtaining the best performances and can achieve up to 98% accuracy. Keywords: Artificial intelligence | Automatic sorting | Scrap recycling | Cast and wrought Aluminum | Deep learning computer vision | Object detection and recognition |
مقاله انگلیسی |
5 |
An ontology-based knowledge management approach supporting simulation-aided design for car crash simulation in the development phase
یک رویکرد مدیریت دانش مبتنی بر هستی شناسی حمایت از طراحی شبیه سازی کمک برای شبیه سازی تصادف اتومبیل در مرحله توسعه-2021 In the automotive industry, the design process is both costly and time-consuming. This research focuses
on improving the design process by mainly reducing time while producing more robust vehicles. Vehicle
development is based on simulation; thus, the design process is referred to as simulation-aided design.
Engineering design is highly collaborative and knowledge intensive. Therefore, knowledge management
plays a crucial role in today’s global economy and is essential for the competitiveness of companies.
However, current research on engineering knowledge management focuses on either the codification or
the personalisation approaches of knowledge management. Thus, this paper addresses an integrated and
collaborative approach. This paper aims to develop an ontology-based knowledge management approach
to support simulation-aided design, specifically car crash simulation. The knowledge management support system is designed to ensure the capture and retrieval of engineering knowledge and to enable
collaboration between different stakeholders. An evaluation of the models and technologies used is also
undertaken, based on use case scenarios.
keywords: مدیریت دانش | هستی شناسی | مدل دانش | بازیابی دانش | همکاری | طراحی مهندسی | شبیه سازی سقوط | Knowledge management | Ontology | Knowledge model | Knowledge retrieval | Collaboration | Engineering design | Crash simulation |
مقاله انگلیسی |
6 |
Improving supply chain resilience through industry 4:0: A systematic literature review under the impressions of the COVID-19 pandemic
بهبود انعطاف پذیری زنجیره تأمین از طریق صنعت 4:0: بررسی ادبیات سیستماتیک تحت تأثیر همه گیری COVID-19-2021 The COVID-19 pandemic is one of the most severe supply chain disruptions in history and has challenged practitioners and scholars to improve the resilience of supply chains. Recent technological progress, especially industry 4.0, indicates promising possibilities to mitigate supply chain risks such as the COVID-19 pandemic. However, the literature lacks a comprehensive analysis of the link between industry 4.0 and supply chain resilience. To close this research gap, we present evidence from a systematic literature review, including 62 papers from high-quality journals. Based on a categorization of industry 4.0 enabler technologies and supply chain resilience antecedents, we introduce a holistic framework depicting the relationship between both areas while exploring the current state-of-the-art. To verify industry 4.0’s resilience opportunities in a severe supply chain disruption, we apply our framework to a use case, the COVID-19-affected automotive industry. Overall, our results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness. Moreover, we demonstrate that visibility and velocity are the resilience antecedents that benefit most from industry 4.0 implementation. We also establish that industry 4.0 holistically supports pre-disruption resilience measures, enabling more effective proactive risk management. Both research and practice can benefit from this study. While scholars may analyze resilience potentials of under-explored enabler technologies, practitioners can use our findings to guide industry 4.0 investment decisions. Keywords: Industry 4.0 | Supply chain risk management | Supply chain resilience | Supply chain disruption | Digital supply chain | Literature review |
مقاله انگلیسی |
7 |
Dynamic capabilities in sustainable supply chain management: An inter-temporal comparison of the food and automotive industries
قابلیت های پویا در مدیریت پایدار زنجیره تأمین: مقایسه بین زمانی صنایع غذایی و خودرو-2021 This paper seeks to enrich the theoretical debate on dynamic capabilities (DCs) in sustainable supply chain management (SSCM). By extending Beske et al.’s (2014) study, a systematic literature review was conducted, and articles matching our inclusion criteria were analyzed from 2002 to 2018. Yet, two major additions are made. For the first time, two distinctive sectors, i.e., the food and automotive industry, are compared. Furthermore, a temporal perspective is provided by comparing two time periods (2002–2013 and 2014–2018) based on content as well as quantitative contingency analyses.The results for the food sector indicate a shift from “standards and certifications,” a central construct within the 2002–2013 sample, to proactive strategies aiming for the integration of stakeholders in the 2014–2018 sample. Similarly, the findings of the automotive industry indicate a shift from monitoring to joint development and knowledge management. Based on our comparison, the SSCM debate in the food industry appears more diverse in terms of practices and capabilities employed. In contrast, the analysis for the automotive industry indicates a focus on SCM elements instead of a comprehensive SSCM view. To the best of our knowledge, this is the first attempt to apply an intra- and inter-sectoral analysis combined with a temporal analysis within the SSCM domain. This provides evidence that the methodological approach taken allows distinguishing among both time periods and industries. Keywords: Sustainable supply chain management | Dynamic capabilities | Automotive industry | Food industry |
مقاله انگلیسی |
8 |
A process-based automotive troubleshooting service and knowledge management system in collaborative environment
یک سرویس عیب یابی خودرو مبتنی بر فرآیند و سیستم مدیریت دانش در یک محیط مشترک-2020 In automotive industry, manufacturers expand their duties in the products service life by means of providing the car troubleshooting services to end users. Moreover, product service system (PSS) has turned into a significant study issue in the recent decade, it focuses on stressing the specific requirements in the new service model. Hence, generation of a PSS-oriented automotive troubleshooting mode not only enhances the service quality, but promotes thoroughly the values of business and its products. Besides, under the support of network communication mechanism, it becomes easily to manage the knowledge and process of automotive troubleshooting in collaborative environment. This study explores the different relationships among car enterprises, focusing on the decision-making processes as well as communication and management of knowledge in automotive trouble- shooting. This study also investigates the potential utilization of improved content management architectures in the automotive engineering field that is controlled by conventional engineering information modes. A frame- work of collaborative troubleshooting procedure is presented, constructed and evaluated by using an instance automobile problem, which reveals the meaningful efficiency could be reached and the module of content management has several benefits than the conventional engineering information modes in conducting the in- formation of automotive inspection, maintenance, repairing and service involving unorganized and dynamic knowledge. Keywords: Automotive troubleshooting | Product service architecture | Collaboration of service | Process and knowledge management |
مقاله انگلیسی |
9 |
Deep reinforcement learning for a color-batching resequencing problem
یادگیری تقویتی عمیق برای یک مسئله سنجش مجدد دسته ای رنگ-2020 In automotive paint shops, changes of colors between consecutive production orders cause costs for cleaning the
painting robots. It is a significant task to re-sequence orders and group orders with identical color as a color
batch to minimize the color changeover costs. In this paper, a Color-batching Resequencing Problem (CRP) with
mix bank buffer systems is considered. We propose a Color-Histogram (CH) model to describe the CRP as a
Markov decision process and a Deep Q-Network (DQN) algorithm to solve the CRP integrated with the virtual car
resequencing technique. The CH model significantly reduces the number of possible actions of the DQN agent, so
that the DQN algorithm can be applied to the CRP at a practical scale. A DQN agent is trained in a deep
reinforcement learning environment to minimize the costs of color changeovers for the CRP. Two experiments
with different assumptions on the order attribute distributions and cost metrics were conducted and evaluated.
Experimental results show that the proposed approach outperformed conventional algorithms under both conditions.
The proposed agent can run in real time on a regular personal computer with a GPU. Hence, the proposed
approach can be readily applied in the production control of automotive paint shops to resolve orderresequencing
problems. Keywords: Deep reinforcement learning | Color-Batching problem | Virtual car resequencing | Production control | Automotive industry |
مقاله انگلیسی |
10 |
Managing workflow of customer requirements using machine learning
مدیریت گردش کار نیاز مشتری با استفاده از یادگیری ماشین-2019 Customer requirements – product specifications issued by the customer – organize the dialog between
suppliers and customers and, hence, affect the dynamics of supply networks. These large and complex
documents are frequently updated over time, while changes are seldom marked by the customers who
issue the requirements. The lack of structure and defined responsibilities, thus, demands an expert to
manually process the requirements. Here, the possibility to improve the usual workflow with machine
learning algorithms is explored.
The whole requirements management process has two major bottlenecks, which can be automatized.
The first one, detecting changes, can be accomplished via a document comparison tool. The second one,
recognizing the responsibilities and assigning them to the right department, can be solved with standard
machine learning algorithms. Here, such algorithms are applied to a dataset obtained from a global
automotive industry supplier.
The proposed method improves the requirements management process by reducing an expert’s
workload and thus decreasing the time for processing one document was reduced from 2 weeks to 1 h.
Moreover, the method gives a high accuracy of department assignment and can self-improve once
implemented into a requirements management system.
Although the machine learning methods are very popular nowadays, they are seldom used to improve
business processes in real companies, especially in the case of processes that did not require
digitalization in the past. Here we show, how such methods can solve some of the management problems
and improve their workflow. Keywords: Documents management | Automation | Classification | Machine learning |
مقاله انگلیسی |