Review
Computer Science, Artificial Intelligence
William Derigent, Olivier Cardin, Damien Trentesaux
Summary: The flexibility and reactivity of control systems are enhanced under the Industry 4.0 paradigm, with control structures based on the holonic paradigm becoming more cooperative. As the concept evolves, holonic control architectures have gradually become a major paradigm for intelligent manufacturing systems, partly fulfilling the key enablers of Industry 4.0.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Computer Science, Interdisciplinary Applications
Anil Kumar Inkulu, M. V. A. Raju Bahubalendruni
Summary: Industries are adopting advanced technologies to achieve the goals of the manufacturing system and meet the demands of the current industrial era. A collaborative and flexible manufacturing system that integrates human intelligence with a robotic workforce enables customized automation and enhances the safety of human labor. The performance of this system is validated through implementation and discussion of results in an industrial case study.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Minakshi Kumari, Makarand S. Kulkarni
Summary: The reported study aims to connect advanced analytics and digital simulation in order to facilitate real-time control of manufacturing operations. The study proposes a framework for designing a decision support system and demonstrates it using a case study of a high pressure die casting manufacturer. The findings highlight the importance of evaluating and controlling intervention plans for each machine and environmental variable dynamically.
INDUSTRIAL MANAGEMENT & DATA SYSTEMS
(2022)
Article
Automation & Control Systems
Jiaye Song, Zequn Zhang, Dunbing Tang, Haihua Zhu, Liping Wang, Qingwei Nie
Summary: The increasing demand for personalized products has led to a reform in the manufacturing paradigm. Traditional manufacturing systems do not often analyze and provide feedback on the data collected during production. The lack of interoperability is the bottleneck between the physical and digital worlds of manufacturing systems. This paper presents a digital twin-based self-organizing manufacturing system (DT-SOMS) under the individualization paradigm. A decentralized self-organizing network is established based on the interconnection between smart workpieces and smart resources through decentralized digital twin models to achieve intelligent collaboration between tasks and resources. The mechanism of job-machine optimal assignment and adaptive optimization control is constructed to improve the reconfiguration and responsiveness capabilities of the DT-SOMS. An implementation case is designed to illustrate that the proposed DT-SOMS can achieve synchronized online intelligence in resource configuration and response to disturbances.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Engineering, Industrial
Andreas Kuhnle, Marvin Carl May, Louis Schaefer, Gisela Lanza
Summary: In the age of Industry 4.0, manufacturing is characterized by high product variety and complex material flows, requiring adaptive production planning systems. This paper investigates methods of explainable reinforcement learning in production control, presenting an approach that combines high prediction accuracy and explainability to generate understandable control strategies. The results are demonstrated on a real-world system from semiconductor manufacturing in a simulated approach.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2022)
Article
Automation & Control Systems
Daqiang Guo, Ray Y. Zhong, Yiming Rong, George G. Q. Huang
Summary: This article introduces the concepts and principles of shop-floor logistics and manufacturing synchronization. It proposes an overall framework based on an intelligent manufacturing system and explores synchronization mechanisms using mixed-integer programming and an equivalent constraint programming model. The case study demonstrates the advantages of this approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Green & Sustainable Science & Technology
Kai Ding, Yijie Zhang, Felix T. S. Chan, Chaoyang Zhang, Jingxiang Lv, Qiang Liu, Jiewu Leng, Hailing Fu
Summary: Manufacturers collaborate with equipment providers and customers to produce customer-centered individualized products, using new technologies such as Cyber-Physical System (CPS) and advanced business models such as Product-Service System (PSS). A Cyber-Physical Production Monitoring Service System (CPPMSS) is proposed to support service-oriented collaborative manufacturing operations, which can satisfy the collaborative manufacturing operations among different stakeholders in the mass individualization environment.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiaolang Yang, Xuemei Liu, Heng Zhang, Ling Fu, Yanbin Yu
Summary: Digital twin is a virtual representation of physical entities, providing support for cyber-physical systems and intelligent manufacturing. There is a lack of research on digital twin in the shop-floor domain and a comprehensive model-driven architecture. This paper proposes a meta-model-based approach and architecture for shop-floor digital twin construction, which is validated through a case study.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2023)
Article
Computer Science, Interdisciplinary Applications
Armin F. Buckhorst, Lea Grahn, Robert H. Schmitt
Summary: The Line-less Mobile Assembly System paradigm (LMAS) provides flexibility for large-scale product production. A suitable control system is needed to connect resources and autonomously configure transient assembly stations. This paper introduces a suitable decentralized multi-agent control system approach.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2022)
Article
Engineering, Industrial
Daisy Valle Enrique, Erico Marcon, Fernando Charrua-Santos, Alejandro G. Frank
Summary: This study examines the contribution of Industry 4.0 technologies to manufacturing flexibility through multiple case studies. The findings suggest that Industry 4.0 technologies are mainly used to improve machine flexibility, with cloud services, IoT, and data analytics serving as the foundation for flexible operations.
JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT
(2022)
Article
Green & Sustainable Science & Technology
Angelo Corallo, Vito Del Vecchio, Marianna Lezzi, Paola Morciano
Summary: The digital twin is a key technology in smart manufacturing, enabling the management and analysis of physical and digital processes for sustainability. However, fragmented studies and a lack of in-depth knowledge about digital twin concepts are still evident in the shop floor domain. A comprehensive framework, such as the proposed HexaSFDT, is needed to integrate main components and relationships for manufacturing organizations to understand the shop floor digital twin effectively.
Article
Mathematics
Varun Tripathi, Somnath Chattopadhyaya, Alok Kumar Mukhopadhyay, Shubham Sharma, Changhe Li, Gianpaolo Di Bona
Summary: The present study aims to develop a methodology for cleaner production management using lean and smart manufacturing in Industry 4.0. The results of two case studies show that the developed methodology can achieve a sustainable production system and problem-solving, while enhancing productivity within limited constraints.
Article
Automation & Control Systems
Apostolos Evangelidis, Nikolaos Dimitriou, Lampros Leontaris, Dimosthenis Ioannidis, Gregory Tinker, Dimitrios Tzovaras
Summary: Deep learning has made significant progress in industrial inspection and combining it with metrology has yielded impressive results. However, deploying metrology sensors in factories is challenging due to cost and special acquisition conditions. This article proposes a methodology that replaces a high-end sensor with a low-cost data-driven soft sensor model. It introduces a residual architecture (R(2)esNet) for quality inspection and an error-correction scheme to reduce noise impact. The methodology is evaluated in PCB manufacturing and achieves promising results, significantly reducing the inspection time compared to other methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Jiapeng Zhang, Jianhua Liu, Cunbo Zhuang, Haoxin Guo, Hailong Ma
Summary: To address the challenges of uncertainty, dynamics, and complexity in a discrete manufacturing shop floor, a data-driven smart management and control framework called digital twin shop floor (DTS) is proposed. It focuses on five key tasks, including the construction of a shop floor digital twin model, data acquisition and management, real-time data-driven modeling, online prediction, and multi-agent-based operation decision. Additionally, a DT-based smart management and control system named DT-VPPC is developed for complex products on the assembly shop floor, and its effectiveness is demonstrated through a specific application example.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Gregor Klancar, Marija Seder
Summary: This paper proposes a global navigation function for autonomous mobile robots in warehouse automation using model predictive control (MPC). The approach considers both static and dynamic obstacles, generating collision-free trajectories. The navigation function is derived from an E* graph search algorithm and bicubic interpolation on a discrete occupancy grid, with pre-computation for improved computational efficiency. The novel optimization strategy in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. The effectiveness of the proposed approaches is validated through simulations and experiments.
Article
Computer Science, Interdisciplinary Applications
Fouzia Ounnar, Patrick Pujo, Maroua Hachicha, Yves Dubromelle
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2016)
Article
Computer Science, Interdisciplinary Applications
Patrick Pujo, Fouzia Ounnar, Damien Power, Selma Khader
COMPUTERS IN INDUSTRY
(2016)
Article
Automation & Control Systems
Olivier Cardin, Fouzia Ounnar, Andre Thomas, Damien Trentesaux
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2017)
Article
Computer Science, Artificial Intelligence
F. Ounnar, P. Pujo
JOURNAL OF INTELLIGENT MANUFACTURING
(2012)
Proceedings Paper
Automation & Control Systems
Patrick Pujo, Fouzia Ounnar, Tarik Remous
SERVICE ORIENTATION IN HOLONIC AND MULTI-AGENT MANUFACTURING
(2015)
Proceedings Paper
Energy & Fuels
Fouzia Ounnar, Aziz Naamane, Patrick Pujo, Nacer-Kouider M'Sirdi
MEDITERRANEAN GREEN ENERGY FORUM 2013: PROCEEDINGS OF AN INTERNATIONAL CONFERENCE MGEF-13
(2013)
Proceedings Paper
Automation & Control Systems
Thamer Louati, Fouzia Ounnar, Patrick Pujo, Christophe Pistoresi
2012 2ND INTERNATIONAL CONFERENCE ON COMMUNICATIONS, COMPUTING AND CONTROL APPLICATIONS (CCCA)
(2012)
Proceedings Paper
Automation & Control Systems
Yves Dubromelle, Fouzia Ounnar, Patrick Pujo
2012 2ND INTERNATIONAL CONFERENCE ON COMMUNICATIONS, COMPUTING AND CONTROL APPLICATIONS (CCCA)
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Yves Dubromelle, Fouzia Ounnar, Patrick Pujo
SERVICE ORIENTATION IN HOLONIC AND MULTI-AGENT MANUFACTURING CONTROL
(2012)
Article
Engineering, Industrial
Patrick Pujo, Ilham El Khabous, Fouzia Ounnar
INTERNATIONAL JOURNAL OF LEAN SIX SIGMA
(2015)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
