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Keywords = traditional shipyard industry

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22 pages, 9855 KiB  
Article
Predictive Control for Steel Rib Bending Based on Deep Learning
by Yijiang Xia, Jinhui Luo, Zhuolin Ou, Xin Han, Junlin Deng and Ning Wu
J. Mar. Sci. Eng. 2025, 13(1), 41; https://doi.org/10.3390/jmse13010041 - 30 Dec 2024
Viewed by 867
Abstract
In the shipbuilding industry, the inefficiency of the successive approximation control method in CNC cold-bending machines has hindered productivity in steel bending manufacturing, particularly for rib profiles. This study proposes control methods for cold bending machines based on deep learning models to address [...] Read more.
In the shipbuilding industry, the inefficiency of the successive approximation control method in CNC cold-bending machines has hindered productivity in steel bending manufacturing, particularly for rib profiles. This study proposes control methods for cold bending machines based on deep learning models to address this challenge, including CNN and Transformer-CNN (T-CNN), to predict the elastic spring-back rate of cold-processed metal profiles and generate precise control pulses for achieving target bending angles. Experimental validation using real-world datasets collected from a shipyard’s CNC cold bending machine demonstrates that the T-CNN model significantly reduces the number of steps required for each bending operation, achieving a 75% reduction in production time and substantially enhancing processing efficiency. By leveraging the strengths of CNNs and Transformer architectures, the T-CNN model excels at handling long sequence data and capturing global dataset characteristics. Results show that the T-CNN model outperforms traditional control methods and standard CNNs in prediction accuracy, stability, and efficiency, making it a superior choice for cold bending control. Full article
(This article belongs to the Section Ocean Engineering)
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27 pages, 33375 KiB  
Article
Worker Presence Monitoring in Complex Workplaces Using BLE Beacon-Assisted Multi-Hop IoT Networks Powered by ESP-NOW
by Raihan Uddin, Taewoong Hwang and Insoo Koo
Electronics 2024, 13(21), 4201; https://doi.org/10.3390/electronics13214201 - 26 Oct 2024
Cited by 1 | Viewed by 1635
Abstract
The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as [...] Read more.
The increasing adoption of Internet of Things (IoT) technologies has facilitated the creation of advanced applications in various industries, notably in complex workplaces where safety and efficiency are paramount. This paper addresses the challenge of monitoring worker presence in vast workplaces such as shipyards, large factories, warehouses, and other construction sites due to a lack of traditional network infrastructure. In this context, we developed a novel system integrating Bluetooth Low Energy (BLE) beacons with multi-hop IoT networks by using the ESP-NOW communications protocol, first introduced by Espressif Systems in 2017 as part of its ESP8266 and ESP32 platforms. ESP-NOW is designed for peer-to-peer communication between devices without the need for a WiFi router, making it ideal for environments where traditional network infrastructure is limited or nonexistent. By leveraging the BLE beacons, the system provides real-time presence data of workers to enhance safety protocols. ESP-NOW, a low-power communications protocol, enables efficient, low-latency communication across extended ranges, making it suitable for complex environments. Utilizing ESP-NOW, the multi-hop IoT network architecture ensures extensive coverage by deploying multiple relay nodes to transmit data across large areas without Internet connectivity, effectively overcoming the spatial challenges of complex workplaces. In addition, the Message Queuing Telemetry Transport (MQTT) protocol is used for robust and efficient data transmission, connecting edge devices to a central Node-RED server for real-time remote monitoring. Moreover, experimental results demonstrate the system’s ability to maintain robust communication with minimal latency and zero packet loss, enhancing worker safety and operational efficiency in large, complex environments. Furthermore, the developed system enhances worker safety by enabling immediate identification during emergencies and by proactively identifying hazardous situations to prevent accidents. Full article
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16 pages, 776 KiB  
Article
Optimizing Manufacturing Cycles to Improve Production: Application in the Traditional Shipyard Industry
by Ikhlef Jebbor, Zoubida Benmamoun and Hanaa Hachimi
Processes 2023, 11(11), 3136; https://doi.org/10.3390/pr11113136 - 2 Nov 2023
Cited by 16 | Viewed by 4339
Abstract
This article explores the important role of traditional shipyards in the global maritime industry, covering aspects of construction, repair, and maintenance. With the advent of faster manufacturing techniques, traditional shipyards face important challenges, such as planning errors, coordination problems, delivery delays, and underutilization [...] Read more.
This article explores the important role of traditional shipyards in the global maritime industry, covering aspects of construction, repair, and maintenance. With the advent of faster manufacturing techniques, traditional shipyards face important challenges, such as planning errors, coordination problems, delivery delays, and underutilization of technology, which results in high costs, reduced productivity, and prolonged projects. The application of Manufacturing Cycle Efficiency (MCE) emerged as an important solution to significantly increase production efficiency. MCE empowers shipyards to deal effectively with waste, bottlenecks, and disruptions, thereby increasing performance, competitiveness, and profitability. Using a comprehensive approach that uses both qualitative and quantitative methods, including field surveys, and in-depth interviews in the traditional shipyard industry, this research identifies Nonvalue-Added (NVA) processes, conducts process mapping, and calculates MCE. The findings reported in this article underscore the significant wastage in the production process, indicating an urgent need for improvement, given the current average MCE value of 67.08%, indicating considerable room for improvement. This article provides innovative perspectives on optimizing the traditional shipyard industry through production cycle efficiencies while offering actionable recommendations. Key focus areas include integrating management systems, adopting advanced technologies, and implementing sustainable strategies to improve MCE, especially by reducing nonvalue-added time wastage, such as inspection and storage. By implementing strategies that optimize production, minimize waste, and overcome the challenges of global competition, this research contributes to improving MCE. In conclusion, this study is an invaluable guide for industry stakeholders, enabling them to enhance their competitiveness and adapt effectively to a dynamic business environment. Full article
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19 pages, 11970 KiB  
Article
A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images
by Wanrou Qin, Yan Song, Haitian Zhu, Xinli Yu and Yuhong Tu
Remote Sens. 2023, 15(20), 4958; https://doi.org/10.3390/rs15204958 - 13 Oct 2023
Cited by 1 | Viewed by 1901
Abstract
Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which [...] Read more.
Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring. Full article
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27 pages, 6290 KiB  
Article
Digital Twin of Shipbuilding Process in Shipyard 4.0
by Remigiusz Iwańkowicz and Radosław Rutkowski
Sustainability 2023, 15(12), 9733; https://doi.org/10.3390/su15129733 - 18 Jun 2023
Cited by 11 | Viewed by 7503
Abstract
Maximum digitalization is the current trend in industrial development. The digital industrial revolution has been underway for more than a dozen years. Industry 4.0 and the idea of digital twins (DT) are becoming the focus of virtually all industrial sectors. Some sectors are [...] Read more.
Maximum digitalization is the current trend in industrial development. The digital industrial revolution has been underway for more than a dozen years. Industry 4.0 and the idea of digital twins (DT) are becoming the focus of virtually all industrial sectors. Some sectors are more predisposed to digitalization, while for others, the process is much more difficult. This mainly depends on the specific characteristics and susceptibility of a given industry, including the current degree of digitalization of companies, as well as the knowledge and mental readiness of employees. The individual characteristics of an industry are important. Shipbuilding is a traditional industry where the level of digitalization is still low. As a result, the efficiency of shipbuilding processes and the quality of ships built are not sufficiently controlled. The article addresses this problem, reviews work in the field of digitalization of shipbuilding processes, and identifies the needs and challenges in this area. The article proposes the concept of a DT system for the entire ship design and production process. Key areas of digitalization of the actual processes were defined, and a division was made into planning, monitoring, and process analysis activities. Special attention was paid to the area of dimensional quality control, and the dimensional quality management metasystem (DQMM) was introduced into the comprehensive DT system. The requirements were defined, and the limitations of the proposed solution were identified, taking into account a number of external factors, including the degree of readiness of the manufacturer—the shipyard. The developed DT system concept was tested using the example of the construction process of a simplified ship. Practical aspects of the implementation of the proposed solution, in particular, DQMM, were pointed out. Full article
(This article belongs to the Special Issue Innovations in Sustainable Manufacturing Management)
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15 pages, 3407 KiB  
Article
Using Digital Twin in a Shipbuilding Project
by Zoran Kunkera, Tihomir Opetuk, Neven Hadžić and Nataša Tošanović
Appl. Sci. 2022, 12(24), 12721; https://doi.org/10.3390/app122412721 - 12 Dec 2022
Cited by 22 | Viewed by 5625
Abstract
Three-dimensional modelling software tools enable the creation of a digital replica of the product—“Digital Twin”—a representative of “Virtual Reality” as one of the prominent trends of Industry 4.0. The development of the Digital Twin can start simultaneously with the development of the product, [...] Read more.
Three-dimensional modelling software tools enable the creation of a digital replica of the product—“Digital Twin”—a representative of “Virtual Reality” as one of the prominent trends of Industry 4.0. The development of the Digital Twin can start simultaneously with the development of the product, primarily for the purpose of selecting optimal technical and technological solutions prior to and during physical construction, and, ultimately, with the intention of managing the entire product life cycle. The Digital Twin, as one of the key technological achievements in the implementation of the business system transformation from traditional to smart, should also be recognized as the cornerstone of the “Shipyard 4.0” model, i.e., its “Cyber-Physical Space.” This paper is based on statistical and empirical data of the observed shipyard with the aim to represent the significance of the Digital Twin ship in preserving and improving the competitiveness of the shipbuilding industry. Namely, with the emphasis this article places on the contribution of “advanced outfitting” in achieving savings in the shipbuilding process as well as its role in attaining high standards of environmental protection and workplace safety, the importance of its further improvement is an obvious conclusion—with Digital Twin being one of the recognized tools for this purpose. Full article
(This article belongs to the Special Issue Smart Shipbuilding and Marine Production Technologies)
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18 pages, 18769 KiB  
Article
A Fog Computing and Cloudlet Based Augmented Reality System for the Industry 4.0 Shipyard
by Tiago M. Fernández-Caramés, Paula Fraga-Lamas, Manuel Suárez-Albela and Miguel Vilar-Montesinos
Sensors 2018, 18(6), 1798; https://doi.org/10.3390/s18061798 - 2 Jun 2018
Cited by 125 | Viewed by 9823
Abstract
Augmented Reality (AR) is one of the key technologies pointed out by Industry 4.0 as a tool for enhancing the next generation of automated and computerized factories. AR can also help shipbuilding operators, since they usually need to interact with information (e.g., product [...] Read more.
Augmented Reality (AR) is one of the key technologies pointed out by Industry 4.0 as a tool for enhancing the next generation of automated and computerized factories. AR can also help shipbuilding operators, since they usually need to interact with information (e.g., product datasheets, instructions, maintenance procedures, quality control forms) that could be handled easily and more efficiently through AR devices. This is the reason why Navantia, one of the 10 largest shipbuilders in the world, is studying the application of AR (among other technologies) in different shipyard environments in a project called “Shipyard 4.0”. This article presents Navantia’s industrial AR (IAR) architecture, which is based on cloudlets and on the fog computing paradigm. Both technologies are ideal for supporting physically-distributed, low-latency and QoS-aware applications that decrease the network traffic and the computational load of traditional cloud computing systems. The proposed IAR communications architecture is evaluated in real-world scenarios with payload sizes according to demanding Microsoft HoloLens applications and when using a cloud, a cloudlet and a fog computing system. The results show that, in terms of response delay, the fog computing system is the fastest when transferring small payloads (less than 128 KB), while for larger file sizes, the cloudlet solution is faster than the others. Moreover, under high loads (with many concurrent IAR clients), the cloudlet in some cases is more than four times faster than the fog computing system in terms of response delay. Full article
(This article belongs to the Special Issue Sensor Networks and Systems to Enable Industry 4.0 Environments)
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