Article
Engineering, Industrial
Xinlai Liu, Yishuo Jiang, Zicheng Wang, Ray Y. Zhong, H. H. Cheung, George Q. Huang
Summary: This paper proposes a unified five-layer blockchain-enabled secure digital twin platform architecture for small and middle enterprises (SMEs) in the manufacturing industry to overcome the limitations of traditional manufacturing patterns. The experimental results show that the proposed platform, named imseStudio, effectively digitizes manufacturing resources and promotes the transformation towards service manufacturing.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, Prabadevi, N. Deepa, Kapal Dev, Thippa Reddy Gadekallu, Rukhsana Ruby, Madhusanka Liyanage
Summary: Industry 5.0 is the future industrial revolution that aims to leverage human expertise in collaboration with intelligent machines to achieve more efficient manufacturing solutions than Industry 4.0. This article introduces the potential applications and supporting technologies of Industry 5.0, and highlights the research challenges and open issues that need to be further developed.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Lianhui Li, Bingbing Lei, Chunlei Mao
Summary: Digital twin is a technology that creates a virtual model of a physical entity in a digital way, enabling interaction and integration between the physical and information worlds, and providing a reliable bridge for industrial information integration. With the advancement of digital twin, its application in smart manufacturing has become increasingly widespread. This paper proposes a quantitative green performance evaluation framework for smart manufacturing, driven by a digital twin-based industrial information integration system.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2022)
Article
Engineering, Industrial
Ehsan Elahi, Zainab Khalid, Muhammad Zubair Tauni, Hongxia Zhang, Xing Lirong
Summary: Evaluation of climate-induced crop damages is important for developing innovative technologies and management strategies to reduce vulnerability in agriculture. This study analyzed survey data from 1232 wheat growers in Pakistan to estimate the production risk of weather shocks on wheat farms and the effectiveness of different management strategies. The results showed that extreme weather events had significant adverse effects on wheat crop damages, especially when occurring close to harvest time. The adoption of adaptive measures significantly reduced wheat losses, and factors such as education, farming experience, family size, cropping area, and access to weather forecast information also influenced the adoption of innovative management strategies.
Article
Engineering, Industrial
Yasanur Kayikci, Nachiappan Subramanian, Manoj Dora, Manjot Singh Bhatia
Summary: This paper presents a blockchain-enabled food supply chain framework developed through a systematic literature review and semi-structured case interviews in the context of emerging economies. It investigates the suitability of blockchain technology in resolving major challenges, such as traceability, trust, and accountability in the food industry. The study provides empirical evidence of blockchain technology implementation in the Industry 4.0 era and offers insights for future researchers to address technological and people-related challenges.
PRODUCTION PLANNING & CONTROL
(2022)
Article
Automation & Control Systems
Weizheng Wang, Hao Xu, Mamoun Alazab, Thippa Reddy Gadekallu, Zhaoyang Han, Chunhua Su
Summary: The Industrial Internet of Things (IIoT) has significantly transformed personal lifestyles and society operations, sparking interest in areas such as intelligent logistics, smart grids, and smart cities. To address security concerns in IIoT, researchers have proposed a novel certificateless signature scheme using blockchain technology and smart contracts, demonstrating its advantages in security and efficiency.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Industrial
Hongru Cao, Haidong Shao, Xiang Zhong, Qianwang Deng, Xingkai Yang, Jianping Xuan
Summary: This paper proposes an unsupervised domain-share convolutional neural network method for efficient fault transfer diagnosis of machines from steady speeds to time-varying speeds. By improving the efficiency and robustness of feature adaptation and simultaneously extracting domain invariant features from the source domain and target domain, the proposed method aims to improve diagnosis accuracy and transferability.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Engineering, Industrial
Enrico Zio
Summary: The paper discusses the impact of digital transformation on industry and emphasizes the importance of prognostics and health management methods for ensuring safety and reliability of structures and systems. The author highlights the advantages and application areas of PHM, while also pointing out key issues impeding the full deployment of PHM.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Review
Automation & Control Systems
Hai Liu, Chao Zheng, Duantengchuan Li, Xiaoxuan Shen, Ke Lin, Jiazhang Wang, Zhen Zhang, Zhaoli Zhang, Neal N. Xiong
Summary: In this article, an efficient deep matrix factorization method with review feature learning for industrial recommender system is proposed. The method utilizes the interactive features and sparsity property in user reviews to improve recommendation accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Engineering, Industrial
Jin Yang, J. P. Oliveira, Yulong Li, Caiwang Tan, Chenkai Gao, Yixuan Zhao, Zhishui Yu
Summary: The use of multi-material structures is an effective solution to reduce weight and emissions in the automotive industry. However, joining aluminum alloys to steels is challenging due to their mismatched properties. Laser-based processes have been explored to overcome this issue and this review examines the recent achievements and progress in this field, evaluating factors such as joining conditions, filler materials, microstructure, and mechanical properties.
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
(2022)
Article
Automation & Control Systems
Gai-Ge Wang, Da Gao, Witold Pedrycz
Summary: The job-shop scheduling problem is of great practical significance, but is difficult to solve due to many uncontrollable factors. The introduction of fuzzy processing time and completion time allows for a more comprehensive scheduling model, which can be optimized using a hybrid adaptive differential evolution algorithm. Experimental results show that this algorithm outperforms other state-of-the-art algorithms.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Engineering, Industrial
Junliang Wang, Chuqiao Xu, Jie Zhang, Ray Zhong
Summary: This paper provides a comprehensive review of big data analytics (BDA) for intelligent manufacturing systems, covering the concepts, methodologies, and applications. BDA has shown great potential in improving the efficiency and outcomes of product design, manufacturing, and maintenance. However, there are still challenges and opportunities that need further research and exploration.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Engineering, Industrial
Samuel Fosso Wamba, Maciel M. Queiroz
Summary: The emergence of Industry 4.0 has presented numerous challenges and opportunities for organizations worldwide. Blockchain technology, as one of the most disruptive and promising technologies, has the potential to transform various aspects of businesses and operations, particularly in supply chain relationships. This study proposes a multi-stage model to better understand the diffusion of blockchain across supply chains. The findings reveal significant differences in the variables and stages of blockchain adoption across different countries. This research makes substantial contributions to theory and management.
PRODUCTION PLANNING & CONTROL
(2022)
Article
Automation & Control Systems
Kexin Li, Han Bao, Houzhen Li, Jun Ma, Zhongyun Hua, Bocheng Bao
Summary: This article introduces a discrete memristive Rulkov (m-Rulkov) neuron model that can simulate magnetic induction effects and better characterize the actual firing activities in biological neurons.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Engineering, Industrial
Sajad Saraygord Afshari, Fatemeh Enayatollahi, Xiangyang Xu, Xihui Liang
Summary: This paper provides a review of the development and use of machine learning models in structural reliability analysis (SRA). It explains the most common types of machine learning methods used in SRA, including artificial neural networks, support vector machines, Bayesian methods, and Kriging estimation. The focus is on the different model structures and diverse applications of each machine learning method in various aspects of SRA. The review also highlights important considerations in sample management and treating the SRA problem as a pattern recognition or classification task.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Hai Liu, Tingting Liu, Zhaoli Zhang, Arun Kumar Sangaiah, Bing Yang, Youfu Li
Summary: This article addresses two key problems in head pose estimation - better prediction performance and using adjacent poses information. It proposes a method using label learning and asymmetric relation cues to improve the accuracy of head pose estimation.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Xiaolong Xu, Bowen Shen, Sheng Ding, Gautam Srivastava, Muhammad Bilal, Mohammad R. Khosravi, Varun G. Menon, Mian Ahmad Jan, Maoli Wang
Summary: This article addresses the service offloading problem in digital twinning-empowered edge computing Internet of vehicles (IoV) systems. By proposing a service offloading method with deep reinforcement learning, the offloading decisions can be optimized, leading to improved service quality.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Industrial
Yuqian Lu, Hao Zheng, Saahil Chand, Wanqing Xia, Zengkun Liu, Xun Xu, Lihui Wang, Zhaojun Qin, Jinsong Bao
Summary: This position paper discusses the concept, needs, reference model, enabling technologies, and system frameworks of human-centric manufacturing. It provides a relatable vision and research agenda for future work in human-centric manufacturing systems. Human-centric manufacturing should address human needs and the human-machine relationships will evolve.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Automation & Control Systems
Hang Su, Wen Qi, Yingbai Hu, Hamid Reza Karimi, Giancarlo Ferrigno, Elena De Momi
Summary: Recently, the kinematic model establishing the relationship of an anthropomorphic manipulator and human arm motions has enabled the accomplishment of human-like behavior on the anthropomorphic robot manipulator. This article introduces a novel incremental learning framework that combines a deep convolutional neural network with an incremental learning approach for fast and efficient imitation learning in anthropomorphic robotics.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Behavioral Sciences
Thomas O'Neill, Nathan McNeese, Amy Barron, Beau Schelble
Summary: This study defines human-autonomy teaming and provides a synthesis of existing empirical research on the topic. The research environments, dependent variables, key findings, and future research directions are identified. The findings suggest the need for further research on mechanisms linking team input to team output variables.
