Article
Green & Sustainable Science & Technology
Jiewu Leng, Man Zhou, Yuxuan Xiao, Hu Zhang, Qiang Liu, Weiming Shen, Qianyi Su, Longzhang Li
Summary: Digital twins technology enables semi-physical simulation to reduce the commissioning cost of manufacturing systems. This paper proposes a digital twins-based remote semi-physical commissioning approach, validated through a case study, to make the commissioning of smart manufacturing systems more sustainable by combining open architecture design paradigm with digital twins-based approach.
JOURNAL OF CLEANER PRODUCTION
(2021)
Review
Chemistry, Physical
S. Divya, Swati Panda, Sugato Hajra, Rathinaraja Jeyaraj, Anand Paul, Sang Hyun Park, Hoe Joon Kim, Tae Hwan Oh
Summary: Recent advancements in AI and ML have increased the demand for self-powered devices. To address the energy issue, energy-harvesting technologies like PENG and TENG are being explored. This article discusses the use of AI technologies for data processing in PENG and TENG, and the potential applications in robotics, security systems, and healthcare. The challenges and alternatives in these technologies are also explored.
Article
Computer Science, Interdisciplinary Applications
Ziqi Huang, Marcel Fey, Chao Liu, Ege Beysel, Xun Xu, Christian Brecher
Summary: Digital twin (DT) and artificial intelligence (AI) technologies are essential for achieving sustainable resilient manufacturing in Industry 4.0. A novel modeling framework is proposed in this article to address the limitations of digital twins at the shopfloor level. The framework integrates AI techniques and machine tool expertise using aggregated data, contextualizes metadata sources from different stages of production, and incorporates prior knowledge to enhance modeling reliability in dynamic industrial circumstances. A hybrid learning-based digital twin is developed and tested, which enables learning uncertainties in real industrial environments and improves modeling reliability based on data quality and accessibility.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Editorial Material
Agricultural Engineering
Samir Kumar Khanal, Ayon Tarafdar, Siming You
Summary: In recent years, there has been significant attention towards the digital transformation of bioprocesses, focusing on interconnectivity, online monitoring, process automation, AI and ML, and real-time data acquisition. AI can analyze and forecast high-dimensional data from bioprocess dynamics, allowing for precise control and synchronization to improve performance and efficiency. Data-driven bioprocessing is a promising technology for addressing challenges in bioprocesses. The MLSB-2022 special issue incorporates recent advances in ML and AI applications in bioprocesses, providing valuable resources for researchers to learn major developments.
BIORESOURCE TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Kyu Tae Park, Sang Ho Lee, Sang Do Noh
Summary: The study proposed information fusion and systematic logic library (SLL) generation methods to facilitate the self-configuration of an autonomous digital twin (DT). These methods were proposed from available smart manufacturing standards, helping to automatically implement DT applications on physical assets.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Review
Computer Science, Interdisciplinary Applications
Zhihao Liu, Quan Liu, Wenjun Xu, Lihui Wang, Zude Zhou
Summary: This article reviews the background of smart robotic manufacturing and various definitions and categories of robot learning. The focus is on leading technologies and applications of robot learning, as well as open problems and future research directions.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Review
Computer Science, Artificial Intelligence
Chao Zhang, Zenghui Wang, Guanghui Zhou, Fengtian Chang, Dongxu Ma, Yanzhen Jing, Wei Cheng, Kai Ding, Dan Zhao
Summary: Industry 5.0 complements Industry 4.0 by emphasizing research and innovation as drivers towards a sustainable and human-centric industry. Human-centric smart manufacturing (HSM) utilizes human flexibility, machine precision, and new-generation information technologies to construct a super smart and resilient manufacturing system. This paper conducts a systematic literature review to identify promising research topics and highlights the key enablers, applications, and challenges of HSM.
ADVANCED ENGINEERING INFORMATICS
(2023)
Review
Computer Science, Artificial Intelligence
Ching-Hung Lee, Chang Wang, Xiaojing Fan, Fan Li, Chun-Hsien Chen
Summary: As the ageing population grows continuously, traditional healthcare providers are facing difficulties in meeting the changing and unpredictable demands as well as the rising customer expectations. Artificial intelligence (AI) technology is rapidly becoming a powerful tool to accelerate the digital transformation in aged healthcare, addressing the high cost, dynamic nature, and unpredictability of the user environment. This study examined the advancements brought about by AI in elderly healthcare through a thorough literature analysis. The findings revealed the development and implementation of AI-enabled systems and scenarios in several elderly healthcare fields, showing a substantial positive impact and significant improvements in the field.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Tran Hong Van Nguyen, Pei-Min Huang, Chen-Fu Chien, Chung-Kai Chang
Summary: This study aims to develop a digital cost estimation system by integrating data-driven methodologies, search engines, and rule-based decision mechanism to improve the accuracy of cost estimation and the effectiveness of quotation. The empirical study results have shown the practical viability of the proposed solution, surpassing conventional approaches.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Review
Chemistry, Multidisciplinary
Jiawen Xu, Matthias Kovatsch, Denny Mattern, Filippo Mazza, Marko Harasic, Adrian Paschke, Sergio Lucia
Summary: This paper provides an executive summary on AI techniques for non-experts, focusing on deep learning, and discusses the important issues of data quality, data secrecy, and AI safety in industrial AI systems. The paper presents state-of-the-art techniques and industrial use cases for each challenge, providing readers with a concrete view of these techniques.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Constantin Cronrath, Bengt Lennartson
Summary: In the control of complex systems, there are two main trends: model-based control from digital twins and model-free control through AI. Attempts have been made to bridge the gap between these two by incorporating learning-based AI algorithms into digital twins. However, evaluation results show that blackbox optimization algorithms generally outperform generic learning algorithms.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Review
Chemistry, Analytical
Ziqi Huang, Yang Shen, Jiayi Li, Marcel Fey, Christian Brecher
Summary: Digital twin and artificial intelligence technologies play essential roles in Industry 4.0, with a focus on the integration of infrastructure, algorithms, and applications. AI-driven digital twin technologies are widely used in smart manufacturing and advanced robotics, offering advantages for sustainable development.
Article
Engineering, Electrical & Electronic
Sergey Konstantinov, Fadi Assad, Bilal Ahmad, Daniel A. Vera, Robert Harrison
Summary: This article introduces a virtual engineering method in the development of industrial automation systems. By using virtual engineering and virtual commissioning, the challenges in the construction process can be effectively addressed, providing assistance in the subsequent stages of the system lifecycle.
Article
Chemistry, Multidisciplinary
Joze M. Rozanec, Blaz Kazic, Maja Skrjanc, Blaz Fortuna, Dunja Mladenic
Summary: The outcomes of this study can be applied to B2B discrete demand forecasting in the automotive industry and potentially to other demand forecasting domains. Global machine learning models outperformed local models and were able to constrain forecast errors by pooling product data based on past demand magnitude. Metrics and criteria were also proposed for a comprehensive understanding of demand forecasting model performance.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Analytical
Marek Noga, Martin Juhas, Martin Gulan
Summary: Digital twin (DT) is an emerging key technology with applications in various engineering fields. Despite lacking a unanimously adopted definition, it can be used to improve existing processes or create new devices, and support device commissioning. This article presents the concept of hybrid virtual commissioning through a practical case study, highlighting its benefits and suitable scenarios.
Article
Mechanics
Christopher Sacco, Anis Baz Radwan, Andrew Anderson, Ramy Harik, Elizabeth Gregory
COMPOSITE STRUCTURES
(2020)
Article
Chemistry, Analytical
Kaishu Xia, Clint Saidy, Max Kirkpatrick, Noble Anumbe, Amit Sheth, Ramy Harik
Summary: The manufacturing industry is currently undergoing a paradigm shift from traditional control pyramids to decentralized, service-oriented, and cyber-physical systems in the Fourth Industrial Revolution. The authors aim to develop a novel CPS-enabled control architecture that includes intelligent information systems, fast and secure industrial communication networks, cognitive automation, and interoperability between machines and humans.
Article
Engineering, Multidisciplinary
Christopher Sacco, Alex Brasington, Clint Saidy, Max Kirkpatrick, Joshua Halbritter, Roudy Wehbe, Ramy Harik
Summary: Manual rework is considered necessary in the production of aerospace composite structures manufactured through automated fiber placement. Limited information is available on the quality control outcomes obtained through manual rework. This study investigates how rework may improve part quality, shift defect distributions, or possibly make the resulting structure worse. Through analysis of inspection data, new insights and trends related to manual rework can be observed.
COMPOSITES PART B-ENGINEERING
(2021)
Article
Engineering, Industrial
Xiaoliang Yan, Reed Williams, Elena Arvanitis, Shreyes Melkote
Summary: This paper extends prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, providing manufacturability feedback and labels of candidate machining operations for query 3D parts.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)
Article
Engineering, Industrial
Jing Huang, Zhifen Zhang, Rui Qin, Yanlong Yu, Guangrui Wen, Wei Cheng, Xuefeng Chen
Summary: In this study, a deep learning framework that combines interpretability and feature fusion is proposed for real-time monitoring of pipeline leaks. The proposed method extracts abstract feature details of leak acoustic emission signals through multi-level dynamic receptive fields and optimizes the learning process of the network using a feature fusion module. Experimental results show that the proposed method can effectively extract distinguishing features of leak acoustic emission signals, achieving higher recognition accuracy compared to typical deep learning methods. Additionally, feature map visualization demonstrates the physical interpretability of the proposed method in abstract feature extraction.
JOURNAL OF MANUFACTURING SYSTEMS
(2024)