Internet of Things Driven Digital Twin for Intelligent Manufacturing in Shipbuilding Workshops

Abstract

Intelligent manufacturing research has focused on digital twins (DTs) due to the growing integration of physical and cyber systems. This study thoroughly explores the Internet of Things (IoT) as a cornerstone of DTs, showing its promise and limitations in intelligent shipbuilding digital transformation workshops. We analyze the progress of IoT protocols, digital twin frameworks, and intelligent ship manufacturing. A unique bidirectional digital twin system for shipbuilding workshops uses the Internet of Things to communicate data between real and virtual workshops. This research uses a steel-cutting workshop to demonstrate the digital transformation of the production line, including data collection, transmission, storage, and simulation analysis. Then, major hurdles to digital technology application in shipbuilding are comprehensively examined. Critical barriers to DT deployment in shipbuilding environments are systematically analyzed, including technical standard unification, communication security, real-time performance guarantees, cross-workshop collaboration mechanisms, and the deep integration of artificial intelligence. Adaptive solutions include hybrid edge-cloud computing architectures for latency-sensitive tasks and reinforcement learning-based smart scheduling algorithms. The findings suggest that IoT-driven digital transformation may modernize shipbuilding workshops in new ways.

1. Introduction

In the 1980s, NASA proposed a precursor to DT—a method for monitoring the status of spacecraft from Earth [1]. Grieves (2002) first proposed the fundamental three-dimensional idea of DTs; later industrial uses helped to validate it in 2014 [2]. These developments sparked multidisciplinary research in digital transformation both in academia and business. Digital transformation has been driven through four sequential phases, namely digital enablement, digital assistance, cyber-physical linkage, and networked physical integration, through the fast development of next-generation information and communication technologies, notably the Internet of Things, artificial intelligence (AI), cloud computing, and big data analytics [3]. The Internet of Things allows several sensors to be connected in order to collect data from physical objects throughout their life spans—including design, manufacturing, operation, and retirement [4]. This data is effectively used in DTs to depict the physical characteristics of objects [5]. Through the analysis of sensor-acquired data [6], DTs show traits including the ability to perform real-time analysis, high precision, high integrity, traceability, and high integration [7], thus facilitating real-time monitoring, diagnosis analysis, process optimization, and predictive maintenance [8]. These features help companies to improve operational performance and spot inefficiencies [9].

With over 80% of global trade in goods and people coming from maritime transportation, sea transport is clearly quite competitive compared to other means of mobility [10]. Paradoxically, although innovative technologies have revolutionized many sectors, innovative technologies’ application in shipbuilding has been slower than in sectors like automotive and machinery manufacturing, thus preventing the integration of efficiency-boosting improvements [11]. A DT combines advanced technologies, including the Internet of Things, big data analytics, and artificial intelligence, thus acting as a key facilitator of intelligent manufacturing [12]. With much less research than in other industrial sectors, the use of DTs in shipbuilding is still rather understudied, even with the growing volume of research in smart manufacturing [13].

IoT is the main technical tool used in the implementation of DTs in shipbuilding facilities. IoT-driven mechanisms essentially define the basic processes of virtual–physical interaction and data exchange in DTs. As theoretically shown in Figure 1. IoT acts as the essential link between physical objects and virtual models in DT systems. Therefore, the fundamental need for the application of DTs in shipbuilding facilities is appropriate IoT exploitation to support these systems.

The core research question investigated in this review centers on exploring the application potential, implementation frameworks, and key challenges of IoT-driven DT technology in facilitating the digital transformation of smart manufacturing within shipbuilding workshops. It delves into how the IoT, as the fundamental enabler of DTs, establishes a bidirectional data bridge between physical workshops and their virtual counterparts using protocols, enabling real-time data acquisition, transmission, and synchronization to optimize production processes, enhance operational efficiency, and support informed decision-making. By leveraging a steel-cutting workshop as a representative case study, this review empirically demonstrates the technology’s end-to-end application chain, encompassing data collection, storage, simulation-based analysis, and feedback control, thereby elucidating its significant value in predictive maintenance, process optimization, and resource scheduling. The study also systematically identifies and analyzes the principal obstacles to deploying IoT-driven DTs in the shipbuilding environment, including fragmented technical standards, inherent communication security vulnerabilities, demanding real-time performance requirements, the absence of cross-workshop collaboration mechanisms, and the intricate challenges of deep AI integration. Moreover, it proposes adaptive solutions—such as hybrid edge-cloud computing architectures and reinforcement learning algorithms—aiming to provide the modern shipbuilding industry with scalable pathways for intelligent transformation.

2. Overview of Internet of Things

2.1. Definition and Development of Internet of Things

The phrase “Internet of Things” was first used by Kevin Ashton to denote a network that integrates radio frequency identification-enabled items or sensors, thereby forming an interconnected system of devices [14]. Over time, the Internet of Things has developed into a network linking physical items via several methods. The Internet of Things is a system consisting of several active sensors or items that interact over designated IoT protocols. IoT enables objects to share data with other connected devices or apps, gather data for local processing, transport information to centralized databases, use cloud-based applications for data analysis, or locally manage sensors and devices to perform specified activities [15].

The core of the Internet of Things is to enable information exchange between physical entities (thing to thing) and between people and things (human to thing). The essential properties might be encapsulated as extensive sensing, dependable transmission, and astute processing [16].

• Comprehensive perception denotes the collection and assessment of object information using sensors, radio frequency (RF) technology, and other approaches to record physical characteristics [17].
• Reliable transmission guarantees uninterrupted and trustworthy information flow across interconnected devices, terminals, or systems over IoT networks, facilitating real-time data sharing beyond geographical and temporal limitations [18].
• Intelligent processing entails the analysis and interpretation of acquired data with sophisticated computational approaches, such as edge computing, to extract actionable insights and facilitate data-driven decision-making [19].

2.2. Current Application Status of Internet of Things

Internet of Things technology has been widely used across several sectors. The Internet of Things creates a comprehensive network by integrating diverse information sensing devices, including radio frequency identification (RFID) systems, infrared sensors, global positioning systems, and laser scanners, enabling objects to autonomously gather data and communicate with each other [20].

In the realm of smart homes, consumers may remotely manage intelligent appliances, lighting systems, and security systems using mobile devices utilizing IoT connection [21]. In smart cities, government agencies use IoT to optimize traffic flow, boost public transit efficiency, monitor environmental quality, and manage trash disposal, ultimately enhancing urban operational efficiency and citizens’ quality of life [22]. In healthcare, users employ wearable or implanted sensors to collect health data, which is communicated over IoT to doctors for real-time monitoring and personalized treatment regimens [23]. In warehousing, IoT-enabled load dynamic balancing solutions enhance resource management in intelligent manufacturing facilities [24].

In Industry 4.0, or smart manufacturing, IoT enhances interconnection among industrial machinery, fosters automation and intelligent processes, and enables real-time monitoring and predictive maintenance. In smart agriculture, practitioners use IoT to gather data from soil temperature and humidity sensors, interpret this information, and control irrigation and fertilization systems for precision farming, hence enhancing crop yields [25].

In shipbuilding, the Internet of Things facilitates real-time oversight and administration of construction equipment status and essential metrics [26]. IoT technology is widely used in several production contexts, especially via the deployment of sensors at essential locations inside each workplace. Vision sensors are used in cutting workshops to gather dimensional data of processed materials, which is communicated over IoT networks to servers for real-time verification of adherence to design criteria [27]. In welding workshops, infrared temperature sensors track thermal fluctuations in weld zones, while IoT-enabled data transfer facilitates server-side threshold analysis to avert material deformation resulting from excessive or inadequate temperatures. Likewise, distance-measuring sensors in assembly workplaces confirm the precision of component alignment by relaying positioning data to centralized systems via IoT infrastructure [28]. Additionally, a cohesive IoT platform enables effortless information transfer between design teams and production facilities, allowing for swift resolution of manufacturing or design-related challenges via integrated data streams [29].

In IoT applications, diverse IoT devices produced by various manufacturers demonstrate considerable discrepancies in hardware designs, operating systems, and data formats [30]. To fully use IoT capabilities, it is essential to thoroughly evaluate current IoT protocols and choose suitable standards that correspond to particular operational needs.

2.3. IoT Protocols

IoT protocols function as a standardized language for device communication by establishing uniform data formats, communication protocols, and interfaces to resolve compatibility challenges across diverse devices. Devices that use the same IoT protocols may facilitate smooth communication inside IoT networks, hence improving interoperability and minimizing system integration complexity. The IoT architecture is often divided into five layers, namely the perception layer, network layer, data layer, application layer, and business layer, each including several protocols. Protocols that are widely used include TCP, MQTT, XMPP, and OPC UA.

2.3.1. TCP

The Transmission Control Protocol (TCP) is the predominant protocol, facilitating applications such as FTP, HTTP, email, and Telnet, by guaranteeing data reliability and integrity through systematic data packet transmission [31]. The establishment of a TCP connection comprises three phases, as depicted in Figure 2:

• Connection Establishment: The client sends an SYN request to the server, which replies with SYN + ACK. Upon receipt, the client transmits an ACK, finalizing the connection establishment.
• Data Transmission: Upon establishment, data is exchanged between the client and server until one party terminates the connection or an interruption transpires.
• Connection Termination: Either party may issue an FIN to terminate the connection. The recipient confirms receipt with an ACK and then sends their own FIN and a subsequent ACK, completing the closure.

During this process, TCP utilizes a sliding window mechanism to manage data flow, improving network congestion control and performance optimization. During the connection process, TCP employs a sliding window mechanism to regulate data flow between the sender and receiver, thereby ensuring network congestion control and optimizing performance [32].

2.3.2. MQTT

MQTT is a streamlined and effective middleware protocol intended for effortless communication between IoT devices. It is defined by its minimal bandwidth, low power usage, scalability, and dependability, rendering it exceptionally appropriate for IoT applications. Employing a publish–subscribe model, MQTT separates message publishers from subscribers, thereby obviating the necessity for direct communication or mutual awareness. A central broker is utilized to oversee message routing and distribution, as depicted in Figure 3. Moreover, MQTT incorporates strong security protocols, such as Transport Layer Security (TLS) encryption and user authentication, to guarantee secure data transmission [33].

2.3.3. XMPP

Extensible Messaging and Presence Protocol (XMPP) is an open-standard protocol intended for real-time communication, distinguished by its real-time functionality, scalability, security, multi-platform compatibility, cross-language support, and adaptability. It is extensively employed across multiple sectors, including corporate communication, social networking, and the Internet of Things. XMPP predominantly enables real-time data transmission and request/response functionalities among various entities across networks.

XMPP, originally developed in 1999, has been widely utilized as a communication protocol for instant messaging services, including Google Hangouts and WhatsApp Messenger. Its applications have progressively extended to VoIP and gaming services. Recent advancements have led to the widespread adoption of XMPP in IoT applications, particularly through its lightweight implementations such as Stanza, BOSH, SASL, TLS, HTTP Binding, Jingle, Jabber-NG, Jingle SDP, Jingle Sessions, and Jingle RTP [34].

2.3.4. OPC UA

Open Platform Communications Unified Architecture (OPC UA) is an open-standard protocol intended for communication within industrial automation. It is defined by its adaptability, durability, and intricacy, with broad applications in industrial automation contexts. It performs essential roles in equipment surveillance, data collection, and remote management across the manufacturing, energy, and healthcare industries. OPC UA facilitates device-level control, comprehensive plant supervision, and supply chain management, while ensuring cross-vendor device interoperability through secure data transmission and access control mechanisms [35].

The protocol consolidates the administration of real-time operational data, historical records, and incident notifications via a standardized framework. The development and field commissioning of OPC UA-compliant products require specialized debugging tools, usually comprising client interfaces and simulated server environments like UA Expert and UA Server.

2.3.5. IoT Protocol Adaptability Analysis

The fundamental aspect of implementing Internet of Things technology is the use of several IoT protocols to facilitate communication, data transport, and interaction among devices and the network. This article outlines six protocols; however, other protocols exist in Internet of Things applications. These protocols encompass several levels, ranging from the physical layer to the application layer, with each protocol possessing distinct design objectives and relevant contexts.

The selection of industrial communication protocols requires comprehensive consideration of 13 indicators, including device compatibility, security, and data transmission efficiency [36]. In shipyard systems, large-scale equipment coordination, high-metal-density environments, complex process flows, and multi-level data interaction are also involved, placing high demands on IoT protocols in terms of stability, low power consumption, interference resistance, and flexible deployment. MQTT, characterized by its lightweight design, low bandwidth consumption, and favorable real-time performance, is well-suited for rapid transmission of sensor data in dynamic industrial environments [37]. In contrast, OPC UA offers comprehensive data modeling capabilities, robust session control, and enhanced security mechanisms, making it more appropriate for structured communication and control command exchange among complex industrial equipment [38]. In practical deployments, each protocol exhibits distinct advantages: MQTT achieves lower latency and better performance in resource-constrained edge devices, whereas OPC UA demonstrates superior reliability and interoperability under high-concurrency industrial control scenarios [39]. Given these complementary strengths, a hybrid communication architecture is recommended for shipbuilding workshops—utilizing MQTT at the sensing layer for efficient data acquisition and transmission while employing OPC UA for device-level control and bidirectional semantic modeling. Gateway-based protocol bridging ensures seamless interoperability between the two, thereby enhancing overall system robustness, scalability, and adaptability within the harsh and complex operational conditions typical of shipyard environments.

Table 1. Table of IoT protocol suitability in shipyards.

IoT Protocol	Situation	Reason	Ref.
Wi-Fi	Connecting the entire shipyard system	Real-time information transmission, high efficiency, high stability	[40]
LoRa	Monitoring systems in shipyard environments	Conveniently build private networks, long distance, low power consumption, low cost	[41]
Bluetooth	Shipyard pipeline tracking and monitoring	Low cost, fast data transmission speed, large broadcast capacity	[42]
RFID	Identification of high-metal-density areas	Metal materials can interfere with wireless signals, but RFID is not affected by metal materials	[43]
Zigbee	Monitoring toxic gases in shipyards	Supports self-organizing networks, multi-hop communication, and easy deployment	[44]
MQTT	Automated construction of steel structures	Scalable and flexible	[45]
OPC UA	Real-time monitoring of the spraying process	High security, reliability, and interoperability	[46]

provides an adaptive analysis of existing shipyard case studies and indicates the reasons for applying this IoT protocol in corresponding scenarios.

Despite the widespread application of IoT technology in the industrial, agricultural, healthcare, and smart home sectors, its inherent limitations, including data fragmentation and insufficient visualization of complex processes, have become increasingly apparent. To rectify these shortcomings, IoT-enabled DTs have arisen. DTs allow for accurate lifecycle monitoring and probable failure prediction for physical entities by creating high-fidelity virtual models of real systems, standardizing dispersed data gathered using IoT technologies, and enabling centralized visualization and sophisticated analytics.

3. Overview of Digital Twin Technology

3.1. Definition and Development of DT Technology

A precursor to DTs was developed in the 1980s, when NASA proposed a technique for monitoring the status of airplanes in space from Earth, but this first concept was more aligned with “physical twins” than with DTs. The procedure entailed producing two identical replicas of the same machine—one preserved on Earth and the other dispatched into space. The terrestrial twin was exposed to conditions as closely to those encountered by its counterpart in space as feasible. Nevertheless, owing to production faults and variations in operating conditions, this approach engenders considerable inaccuracies [1].

Grieves officially defined a DT as “a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level [47]”. In principle, every piece of information derivable from examining a tangible manufactured product should also be accessible through its DT. Grieves posits that the conceptual model of a DT consists of three primary components: (a) the physical product in the tangible realm, (b) the virtual product in the digital realm, and (c) the data and information linkage that connects the virtual and physical products [2].

The Web of Science Core Collection (WoSCC), a distinguished literature search engine, indicates the quantity of articles on DT from 2002 to 2024, as illustrated in Figure 4. Between 2002 and 2015, the volume of papers focused on DTs was rather minimal. During this period, several research topics pertinent to DTs were developed, including “digital model [48]”, “digital shadow [49]”, “hardware-in-the-loop [50]”, and “digital thread [51]”. Improvements in IoT technology revitalized the significance of DTs.

In 2015, Rosen suggested that DTs may utilize IoT to develop a highly accurate model, illustrating the workflow stages and behaviors of actual interactions with their environment [52].In 2016, Schroeder described DTs as a virtual depiction of a tangible object, incorporating information from the inception of a product’s lifespan to its decommissioning. They represent the relevant components of physical devices, machines, or goods within an information logistics system, namely a cyber-physical system (CPS), containing information pertinent to the whole product lifecycle [53].

In 2017, Brenner and Hummel refined the notion, characterizing DTs as digital representations of actual factories, machinery, and personnel, which possess autonomous scalability, automatic upgrades, and worldwide real-time accessibility [54]. Graessler and Poehler enhanced this approach by incorporating DTs into cyber-physical production systems (CPPSs) and mimicking human workers through dynamic modifications of database parameters, including traits, preferences, work schedules, and skill sets [55]. In 2018, Tao introduced a five-dimensional (5D) DT model, denoted by Formula (1) [56].

$${\mathrm{M}}_{\mathrm{D}\mathrm{T}}=(\mathrm{P}\mathrm{E},\mathrm{V}\mathrm{M},\mathrm{S}\mathrm{s},\mathrm{D}\mathrm{D},\mathrm{C}\mathrm{N})$$

(1)

The model comprises physical entities (PEs), virtual models (VMs), services (Ss), DT data (DD), and connections (CNs). Data within DTs are acknowledged as pivotal drivers [57], and with the incorporation of product–service systems in contemporary industries, organizations are progressively acknowledging the significance of services [58].

In 2019, Lee and Kim asserted that the near-real-time digital depiction of physical entities or processes in DTs enhances business performance optimization. Their research amalgamates the Internet of Things and the Internet of Service (IoS) to facilitate smart factories utilizing DTs, underscoring the pivotal function of IoT in this framework [59]. In 2020, Luo emphasized that DTs are comprehensive virtual prototypes of entire systems with one-to-one mapping links. This requires a multidisciplinary digital modeling strategy to create models aligned with genuine machine tool design settings, real-time data mapping techniques for precise synchronization, and efficient machine learning algorithms to evaluate sensor and control system data [60]. Since that time, research on DTs has increasingly aligned with artificial intelligence, enabling these systems to independently generate data-driven judgements. In 2021, Tao characterized DTs as representations of industrial processes and their components, serving as a conduit between the “real” and “digital” realms [4].

Table 2. Data in the WoSCC related to the definition of DTs.

Ref.	Definition	Citation
[3]	The technology seamlessly integrates the physical and virtual spaces, enabling the realization of intelligent manufacturing and Industry 4.0 goals.	1588
[61]	DT-driven product services integrate physical and virtual components with connected data to enable real-time monitoring, energy analysis, user behavior insights, predictive maintenance, optimization, and virtual operations, supporting Industry 4.0 goals.	1421
[62]	A technique that utilizes computational methods to simulate real-world objects or systems.	864
[63]	A method for modeling physical entities in a virtual environment to simulate their behavior and optimize production processes.	800
[64]	A technology that employs digital methods to model real-world objects and systems and perform simulation analysis in a virtual environment.	785
[65]	Establishing a virtual digital model in a computer system that mirrors actual physical systems, enabling simulations, predictions, and optimizations.	756
[66]	A virtual model created using computer technology based on real system data and information, designed to simulate the behavior and performance of physical systems.	741
[67]	A computerized model combining method-based and data analysis approaches, offering end-to-end visibility, business continuity, predictive decision-making, and emergency response planning.	656
[68]	The process of modeling and simulating physical systems using digital technology to predict and optimize their behavior and performance.	645
[4]	Complex models driven by sensor updates and historical data, reflecting various aspects of products, processes, or services.	612

enumerates extensively referenced papers sourced from the WoSCC pertaining to the definition of DTs. The criteria for retrieval were as follows: the search theme was “DT”, concentrating on study domains including engineering, computer science, telecommunications, and automation control systems. The results were organized by citation frequency, with the most cited papers appearing first.

It can be observed that despite variations in how DTs are defined in academic papers, the fundamental definition consistently revolves around three key components.

• Physical objects: This includes real-world products, systems, facilities, or environments.
• Virtual models: This includes virtual representations of physical entities created in a computer environment.
• Interconnection: This represents a bidirectional connection between the virtual and physical entities, enabling real-time data exchange, synchronization, and feedback.

The realization of the aforementioned three components requires the integration of multiple technologies, including IoT, big data analytics, and cloud computing, with IoT technology serving as the most critical enabler. By leveraging IoT architectures, bidirectional coupling between virtual models and physical entities is achieved, enabling real-time data exchange, synchronization, and feedback, thereby forming the core architecture of DT systems.

3.2. Technologies Related to Achieving DTs

3.2.1. Big Data Analysis

Big data analytics denotes the methodology of aggregating, storing, and processing extensive datasets utilizing computational tools. As information technology progresses, a growing variety of different kinds of data are being generated and stored. If this data can be efficiently extracted and employed, it can generate significant value for organizations and individuals. For example, with big data analytics, organizations can reveal concealed patterns and market trends inside datasets, facilitating future trend forecasting, the development of scientific operational plans, and enhancements in efficiency and economic advantages [69].

As data volumes increase dramatically, conventional data processing techniques have proven insufficient, requiring the creation of innovative algorithms to tackle these issues. As a result, big data analytics has become a significant area of research and application. In DT systems, big data analytics functions as an essential technology. The extensive, diverse data gathered from IoT devices is subjected to big data analytics to explore real enhancements in operational efficiency and predictive problem detection using DTs.

3.2.2. Cloud Computing

Cloud computing denotes the delivery of diverse on-demand services through the Internet, encompassing storage, computing, network capacity, and data analytics, among others. In contrast to conventional local computing models, cloud computing allows organizations and people to decrease expenditures on hardware, software, and maintenance while enhancing resource utilization and operational flexibility. Cloud computing systems feature extensive virtualization of resources, facilitating dynamic allocation and release according to demand. This virtualization technique enables customers to scale computing resources flexibly, thus reducing resource waste [70].

The worldwide aspect of cloud computing removes regional limitations, facilitating universal access to resources and services. Cloud systems enable the processing and visualization of extensive datasets using software interfaces, allowing users to derive meaningful business insights. In high-performance computing sectors, cloud computing provides substantial computational power that enhances scientific research, engineering simulations, and financial modeling. In addition to enterprise applications, cloud computing offers benefits for individual users, including personal cloud storage for file backup and sharing, along with expedited application development and deployment for software developers [71].

Cloud computing serves as a vital facilitator of DT, enabling users to remotely manage virtual twin models using cloud platforms, thus gaining coordinated management of physical entities [72].

3.2.3. Summary of Key Technologies

DT technology relies on core technologies such as the Internet of Things, big data analytics, and cloud computing. IoT enables real-time data transmission from sensors to establish bidirectional connectivity between virtual models and their physical counterparts, achieving seamless integration of cyber-physical systems. Cloud computing provides computational power for remote visualization and control, while big data analytics extracts hidden rules from IoT-collected datasets to optimize system performance and enhance predictive decision-making capabilities within DT frameworks. As these technologies advance, DTs are poised for broader applications across manufacturing, urban management, and intelligent transportation systems. Through more efficient data processing and intelligent analytical decision-making, DTs will deliver increasingly precise and reliable solutions for diverse industrial sectors.

3.3. Specific Applications Related to DTs

DTs can replicate, observe, and enhance real-world items or systems by constructing precise representations of actual entities in a virtual environment. They are extensively utilized across various domains, including optimizing product design and manufacturing processes, thus enhancing efficiency and minimizing costs by proactively identifying possible issues. In smart cities, DTs facilitate traffic management, energy distribution, and environmental monitoring to optimize resource utilization and enhance urban governance [73]. In the domain of healthcare, it assists physicians in formulating individualized treatment strategies and facilitates the advancement of telemedicine services [74]. Moreover, it has demonstrated significant applicability in aircraft [75], construction [76], and various other sectors. By collecting and analyzing real-time data with the aid of sophisticated algorithms, DTs can accurately depict the present condition, forecast future trends, and offer recommendations for enhancement, thus facilitating digital transformation across diverse industries.

Table 3. Examples of DT applications.

Area	Ref.	Content
AirCraft	[75]	This study employs a high-fidelity DT model for real-time monitoring of aircraft, evaluating their structural health and predicting lifespan.
Transportation	[77]	This paper introduces a DT framework utilizing vehicle-to-cloud communication to link vehicles, enhancing advanced driver assistance systems by integrating physical entities and their interactions in a virtual environment for real-time monitoring and synchronization.
Energy	[78]	This paper constructs a DT model for energy management in natural gas and power systems using projection transformation methods, enhancing performance and behavior prediction.
City	[73]	By analyzing six high-performing and three emerging cities, the paper presents DTs and anticipated benefits for these urban areas.
Healthcare	[74]	This study highlights an increasing adoption of DT technology in healthcare, capable of enhancing patient care, prolonging life expectancy, and reducing medical costs.
Manufacture	[79]	The paper outlines a DT application framework, demonstrating its practicality and effectiveness using a welding production line as a case study.
	[80]	This article utilizes the AutomationML standard to create a DT model for simulating and forecasting industrial component behavior.
	[81]	Combining DT technology, the study proposes an advanced simulation technique for complex systems, applicable in data processing, behavior analysis, development, optimization, and validation.
	[82]	This study presents ManuChain4. 0, a smart manufacturing system architecture integrating blockchain and DT technologies. The system employs BPMN models to configure and monitor manufacturing resources, while an offline caching mechanism ensures rapid data synchronization and enables fully visualized data management.
	[83]	A method for mapping and integrating shipbuilding workshop data based on DTs is proposed to address issues such as weak real-time management and control capabilities and long data interaction delays between the physical and virtual worlds that exist in traditional shipbuilding workshops.
Building	[76]	Through experiments collecting over 25,000 sensor readings, this paper presents a DT model for building facade elements, enhancing management and maintenance.

illustrates the utilization of DT across several businesses.

The DT architecture comprises six hierarchical layers: the display layer, twin system layer, supporting technology layer, data resource layer, perception layer, and physical layer. The layers are arranged in a vertical stack, advancing progressively, as depicted in Figure 5:

• Display Layer: This layer offers an interface for users to engage with and visualize the DT model.
• Twin System Layer: This layer oversees the fundamental operations of the DT, encompassing data processing and simulation.
• Supporting Technology Layer: This layer includes the foundational technologies that facilitate the use of the DT, such as IoT, big data analytics, and cloud computing.
• Data Resource Layer: This layer pertains to the acquisition, storage, and administration of data vital for the functionality of the DT.
• Perception Layer: This layer is tasked with the real-time gathering of data from the physical system, guaranteeing precise sensory feedback.
• Physical Layer: This layer denotes the tangible components and entities of the system represented by the DT.

The stratified design of the DT architecture guarantees methodical and hierarchical execution, with each layer enhancing the preceding one to form a cohesive and operational system.

The DT system gathers data from physical items, facilities, and environments through perception layer sensors. Data are sent over communication networks and IoT to the data layer, where they are safely stored in databases. Subsequent analysis employs support technologies like optimization algorithms, model fusion, and mechanistic analysis. The processed data are subsequently simulated and optimized using the twin system layer’s simulation platform, with outcomes visualized through intelligent dashboards, office desktops, or interactive panels in the presentation layer, facilitating real-time monitoring, analysis, and optimization of physical entities.

IoT-enabled DT applications create substantial value in ship manufacturing. Data acquired by sensors are communicated by IoT to DT models, yielding extensive real-time datasets that guarantee that virtual models accurately represent physical realities. Ship design and manufacturing procedures utilize these skills for fault diagnostics and performance enhancement. Virtual simulations based on twin models improve operational efficiency, while IoT-enabled transparency and traceability promote intelligent maintenance and management practices in the maritime sector.

4. IoT-Driven DT for Intelligent Manufacturing Applications in Shipbuilding Workshops

In shipbuilding, specialist workshops perform various responsibilities within the integrated manufacturing system, encompassing cutting, welding, assembly, outfitting, and painting. These workshops function consecutively to guarantee manufacturing efficiency and workflow continuity. The cutting workshop transforms raw materials into components with exact geometry, establishing a basis for later production stages. In the welding workshop, various components are fabricated and welded to create the ship’s structural framework. Semi-finished hulls advance to the assembly factory for the installation of essential mechanical systems. The outfitting workshop incorporates external equipment, including navigation gadgets and life-saving apparatus, while the painting factory applies anti-corrosion coatings and cosmetic finishes to safeguard the hull. The final inspection and sea testing confirm adherence to design specifications and safety standards before delivery. The utilization of DT technology markedly improves process visualization, facilitating real-time modeling of product performance and optimization of design workflows, resulting in quantifiable enhancements in production efficiency [84]. For example, advanced pipe production lines utilize DT frameworks to achieve automated control over logistics, cutting, machining, and welding processes. This method enhances the validation of the complete pipe production process via simulation [85].

4.1. Intelligent Development of Shipbuilding

The incorporation of digital technologies, including artificial intelligence, big data, cloud computing, the Internet of Things, and blockchain, into conventional shipbuilding has catalyzed significant progress in design, manufacture, service delivery, and organizational management. Emerging paradigms such as the industrial internet, DT, “shipbuilding + 5G”, and “shipbuilding + AI” have become important to technological rivalry in the maritime industry. Prominent shipbuilding nations are emphasizing intelligent manufacturing to improve productivity and worldwide market competitiveness. Companies are increasing investments in digital innovation to enhance critical shipbuilding performance metrics and bolster their international market presence.

Hyundai Heavy Industries (HHI) exhibits this shift with its DT strategy, which seeks to create intelligent production lines, workshops, and shipyards. This effort combines research and development with sophisticated information technology applications. In management, HHI utilizes virtual reality technology for safety training, employing VR-based solutions to instruct personnel on hazard prevention and response. A centralized control center oversees facility-wide risks using 250 CCTV cameras, utilizing AI-driven image analytics for proactive risk detection and notifications [86]. In manufacturing, HHI has developed

• Miniature welding robots designed for operation in restricted environments, versatile for cutting and spraying via modular software;
• Advanced welding systems for real-time surveillance of process parameters to enhance quality and efficiency;
• A robotic system for processing double-curved plates that standardizes intricate shaping procedures;
• A virtual sea trial system for LNG carriers, certified by Lloyd’s Register (LR) with an Approval in Principle (AIP) certificate, which simulates actual sea trial circumstances in a virtual setting to assess dual-fuel engines, fuel supply systems, and control units in extreme situations, thereby decreasing sea trial duration and expenses by up to 30% [87].

In infrastructure, HHI collaborates with KT Corporation to deploy 5G-enabled shipyards featuring remote monitoring, accelerated blueprint downloads, and enhanced cybersecurity protocols [88].

Japan Marine United Corporation has created several computer-aided design (CAD) software tools, thereby generating a digitally integrated production environment. This method facilitates 3D digital design with a singular data source, including models from the initial design stage to manufacturing readiness. A three-dimensional work guiding system aids on-site personnel in deciphering design plans. The company utilizes advanced robotic welding equipment and automated production lines for the assembly of components and blocks in manufacturing. The JMU-α automatic plate bending system automates activities such as plate positioning, heating, cooling, height adjustment, laser measuring, and flipping, thereby considerably improving production efficiency [89].

Newport News Shipbuilding is adopting Siemens software for digital transformation, employing 3D models as the foundation for virtual simulations of ship construction and maintenance process planning [90].

Meyer Werft has implemented the CATIA 3D design technology to create a “digital shipyard”, achieving advanced integration of design and building processes with predominantly paperless operations. The digital shipyard utilizes intricate simulations in the initial construction stages to detect and correct problems, therefore minimizing deadlines and expenses. Meyer Werft’s Virtual Reality Room facilitates the collaborative design of structural, pipeline, shafting, electrical, and equipment configurations by technical and production specialists inside a virtual environment. Pre-construction process planning is executed through virtual reality [91].

In 2018, China’s Ministry of Industry and Information Technology (MIIT) and the State Administration of Science, Technology and Industry for National Defense (SASTIND) released the Intelligent Transformation Action Plan for Shipbuilding (2019–2021). This policy expedites the amalgamation of next-generation ICT with sophisticated shipbuilding technologies, with the objective of attaining comprehensive lifecycle digitalization, networking, and intelligence in design, construction, management, and services. By 2025, China intends to develop a complete intelligent shipbuilding standard system in accordance with international standards, encompassing fundamental rules, critical technology, and shipyard applications [92].

4.2. DT of Shipbuilding Workshops

In shipbuilding, workshops that handle various materials utilize common technology, including industrial robots and material delivery vehicles, despite differences in raw materials. This commonality facilitates the creation of a cohesive DT system model, customizable to specific workplace attributes via parametric adjustments, thus generating a virtual workshop that accurately reflects physical production environments. The DT system enables real-time surveillance of equipment and logistics, data collection, analytical visualization, and informed decision-making. It additionally facilitates intelligent diagnoses of manufacturing abnormalities and fosters collaboration among design institutes, shipyards, shipowners, and equipment suppliers, hence enhancing operational efficiency and management accuracy.

Implementation necessitates three-dimensional visibility of workshop equipment status, logistics vehicle positioning, block assembly conditions, and production line dynamics. Essential functions encompass interactive visualizations of health status simulations for vital equipment, process optimization assessments, and forecasts of production capacity.

Figure 6 illustrates the DT System Framework for Shipbuilding Workshop Production Lines, whereby the virtual workshop incorporates a DT platform centered on hull fabrication and block erection scenarios. Utilizing CAD tools, lightweight 3D models of facilities, workshops, essential equipment, logistics vehicles, gantry cranes, and workers are created. The virtual environment, created through IoT-enabled cloud platforms, integrates kinematic models and process simulations. Bidirectional IoT connectivity facilitates instantaneous interaction between virtual and actual workshops.

Mechanistic model libraries simulate workshop operations and equipment functions, facilitating real-time assessment of performance measures. Optimization techniques for production scheduling and equipment maintenance are based on these models. Simultaneously, AI-powered predictive analytics for equipment safety and lifecycle management are integrated into the platform. This integrated platform facilitates extensive monitoring, simulation-driven optimization, and predictive maintenance across production planning, operations, and post-delivery services, thereby creating a completely transparent digital shipyard.

The DT-based Manufacturing Workshop Service Platform (DT-MWSP) module functions as an essential element of DT-integrated shipbuilding workshops, designed to facilitate real-time data processing and decision-making support. The DT-MWSP amalgamates conventional systems including Computer-Aided Process Planning (CAPP), Computer-Aided Manufacturing (CAM), Computer-Aided Engineering (CAE), and CAD using IoT technology. This module enables the visualization of machining parameters, monitoring of energy consumption, assessment of tool condition, and optimization of processes using predictive models and dynamic adjustment algorithms, hence ensuring operational efficiency and increasing product quality [93].

The bridging module facilitates bidirectional data transfer between physical and virtual workshops. By employing IoT protocols (such as RFID, TCP/IP, OPC UA, industrial Ethernet, and wireless networks), it facilitates real-time data collection from production machinery, synchronizes lifecycle information (design, installation, operation, and maintenance) of critical assets, and creates virtual–physical correlations of logistics status, personnel location, and intermediate product completion stages. An IoT platform consolidates data from smart sensors, PLC controllers, wearable devices, RFID tags, and vision systems, transferring this information to a cloud-based DT database for storage and analytical processing.

In the physical workshop, production lines consisting of raw materials, machining apparatus, sensors, and operators perform material-to-product conversions. Data acquired by sensors at each station is communicated through the bridging module to the DT database, facilitating real-time monitoring and model-driven control of physical operations.

This DT framework creates real-time 3D virtual representations of workshop components, encompassing hull manufacturing processes, block placements, logistics vehicle paths, crew actions, and dock equipment functions. A digital shipyard sandbox platform facilitates interactive display of essential manufacturing metrics, enhancing data-driven decision-making across the design, production, and post-delivery stages.

Leveraging this foundation, a 3D model-based platform facilitates clear visualization of product process information, production scheduling data, IoT sensor inputs, warehousing logistics status, manufacturing resource allocation, operational performance metrics, project timelines, and safety/environmental controls. This feature improves digital shipyard transparency and operational safety, facilitating multi-tier decision-making for management and workshop-level operations.

A digital block storage yard is created for post-hull production, employing block positioning technology to enhance spatial logistics and inter-process coordination. The amalgamation of 3D positioning devices and laser scanning technology facilitates precise dock measurements with sub-millimeter accuracy. Block erection simulations enhance assembly efficiency in the digital storage yard.

A digital sandblasting and coating workshop is established in painting operations utilizing intelligent spraying apparatus, environmental monitoring sensors, and centralized VOC treatment systems. A painting control center integrates real-time oversight of production metrics, equipment condition, VOC mitigation processes, and energy usage.

An intelligent pipe processing workshop is established for pipe manufacture, focusing on post-welding bending procedures. Automated material distribution systems and comprehensive process traceability tools guarantee systematic workflow execution. Extensive enhancements to cutting, welding, bending, and pressure testing apparatus provide automated process data dissemination and quality-assured production.

4.3. A DT Taking the Cutting Workshop as an Example

The production line of a steel cutting workshop consists of various types of cutting equipment, including flame cutting machines, plasma cutting machines, and laser cutting machines, as well as personnel, materials, and automated material handling systems, such as robotic arms for loading and unloading and devices for plate positioning. These systems are outfitted with sensors, including temperature sensors, vision sensors, and laser rangefinders, to gather real-time data on equipment status (e.g., motor current), material properties (e.g., plate thickness and surface flatness), and environmental conditions (e.g., temperature, humidity, and dust concentration).

The DT virtual model of the cutting line is created with CAD software (e.g., SolidWorks 2025 and AutoCAD 2024) and lightweight engines (e.g., Unity 3D 2023.2.8 and Engine 5.5). This model amalgamates 3D geometric data of essential equipment with dynamic elements and reproduces the spatial configuration of the workshop. Scripting within the virtual engine facilitates analytical operations; for example, C# code integrated with thermodynamic principles can model heat-affected zones during flame or plasma cutting processes in Unity 3D.

After the deployment of sensors and the validation of the DT model in a static state, IoT protocols (such as OPC UA and industrial Ethernet) provide data synchronization between physical and virtual systems. The synchronization method has four stages: data acquisition (sensor-to-gateway collecting), transmission (network transfer to cloud platforms), storage (structured databases), and retrieval (real-time querying for simulation updates).

In the first stage of data acquisition, the data acquisition step entails the deployment of several sensors to monitor equipment status, material properties, and environmental variables. Hall effect sensors quantify motor currents, whereas infrared thermometers monitor cutting head temperatures. Material characterization utilizes laser rangefinders for measuring plate thickness and 3D vision scanners for evaluating surface flatness. Temperature–humidity sensors and particulate matter detectors measure environmental conditions, including dust levels.

In the second stage of data transfer, the MQTT protocol is favored for real-time data transfer because of its lightweight design and minimal latency, which meet industrial IoT standards. A dedicated Wi-Fi network guarantees consistent data transmission speeds, while the OPC UA protocol manages the synchronization of extensive process files. Sensors connect to edge gateways over RS485 or CAN buses, with consolidated data sent to cloud platforms or local servers utilizing MQTT/OPC UA.

OPC UA serves as the middleware for unifying machine-to-machine communication and translating MQTT-streamed data into structured information models compatible with enterprise systems and control logic. It provides robust session control and secure channel encryption via TLS, albeit with slightly higher processing overhead. The OPC UA server component buffers time-series data, performs semantic tagging, and applies compression (e.g., using delta encoding) to manage bandwidth usage. Failover in OPC UA is addressed through session redundancy, where secondary endpoints automatically resume data handling if a primary channel fails. However, frequent handshakes or large model transfers may introduce latency spikes, so session persistence and keep-alive intervals must be fine-tuned.

In the third stage of data storage, data storage classifies production line data into four categories in

Table 4. Data types.

Data Type	Characteristics	Storage Requirements	Examples
real-time streams	High frequency, low latency, time-series	Millisecond-level writing speed, rapid querying	Cutting head position, temperature, current
structured datasets	Relational, fixed schema	ACID transaction support, relational queries	Equipment metadata, process parameter templates
unstructured files	Large files, diverse formats	High throughput, version control	3D-scanned point clouds, NC code files
historical archives	Low-frequency access, long-term retention	Low cost, high compression	Annual production records, equipment logs

: real-time streams, organized datasets, unstructured files, and historical archives. Storage solutions are customized according to data attributes. Real-time streams employ InfluxDB, TimescaleDB, or TDengine; structured data is stored in PostgreSQL or MySQL databases; unstructured files are archived using MinIO (S3-compatible), HDFS, or Alibaba Cloud OSS; and historical archives utilize Apache Hadoop (HDFS + Parquet) or Snowflake.

In the fourth stage of data retrieval, the localization of the DT virtual model is essential. When the virtual model and its corresponding data are located on the same device, direct data access can be facilitated via embedded programming; for instance, Unity3D-based DT models can extract certain datasets from local databases utilizing C# scripts. When the virtual model is hosted on a device remotely connected to a data server through IoT protocols, MQTT or TCP-based communication is necessary to synchronize data between the server and the virtual environment.

Edge gateways buffer outgoing MQTT messages during brief network interruptions, while Unity3D maintains a rolling cache of the last-known good state to ensure rendering continuity in terms of failover. A watchdog system ensures that stale or delayed messages trigger alerts and retry mechanisms. Thus, the end-to-end pipeline balances lightweight communication, structured semantics and resilience, scalable storage, and real-time rendering, collectively enabling a robust, synchronized DT system tailored to the complexities of shipbuilding steel cutting workshops.

As illustrated in Figure 7, sensors installed on cutting machines, steel plate pretreatment lines, and CNC marking/coding devices gather operational data regarding equipment status, material characteristics, and ambient conditions. The datasets are conveyed over IoT protocols to a centralized DT database for virtual workshop integration.

The virtual cutting workshop creates 3D models (e.g., product, material, process, and equipment models) utilizing geometric data from essential machinery and production line configurations. Empirical data from physical sensors facilitate the simulation of virtual work orders, inspection processes, logistics strategies, and testing methodologies. Simulation outcomes are shown on intelligent dashboards or production monitoring interfaces. Operators evaluate these results to provide performance reports, enhance production schedules, and modify model parameters for iterative simulations. Validated configurations are sent as control commands to physical equipment over IoT protocols, facilitating real-time production management, process coordination, and operational supervision.

4.4. Cyber-Physical Integration

At the cyber-physical integration level, big data analytics and processing capabilities constitute the foundation of DT applications. The physical-to-virtual connection conveys sensor-acquired data via IoT protocols and analyzes extensive manufacturing statistics through big data analytics. This facilitates real-time simulation of workshop operations and anticipatory detection of probable problems, permitting proactive responses. This data-driven decision-making improves accuracy in shipyard management and expedites the intelligent transformation of shipbuilding sectors [94].

Multi-source data fusion functions at three hierarchical tiers: data-level, feature-level, and decision-level fusion. Data-level fusion entails the direct amalgamation of raw sensor data to enhance DT models. Feature-level fusion amalgamates collected data characteristics for dimensionality reduction; for example, it consolidates vibration, temperature, and acoustic emission features from several sensors to identify bearing defects in cutting machines. Decision-level fusion integrates information from many models to produce final determinations. In a steel cutting line, data-level fusion modifies cutting head temperature parameters in thermal deformation models; feature-level fusion associates temperature data with material properties; and decision-level fusion calibrates model parameters by integrating thermal and mechanical elements.

Significantly, virtual-to-physical links in DT systems do not inherently require modifications to the physical state. Although DT outputs need to deliver actionable insights, specific applications—like risk-optimized inspection planning for substantial structures—can pinpoint high-risk zones and diminish failure probabilities via simulation-based optimization without altering physical systems [95]. Consequently, cyber-physical integration is contingent upon context rather than uniformly mandated.

4.5. Fault Prediction and Health Management

Conventional fault prediction techniques frequently exhibit reduced accuracy owing to environmental intricacies and sensor constraints. Conversely, cyber-physical integrated DTs create high-fidelity virtual entities that enable system-level real-time monitoring and feedback control [96]. A dynamic closed-loop optimization system is created via real-time operational modifications in actual production lines, facilitating ongoing validation and adjustment of simulation outcomes. This approach improves the intelligence of DT systems in shipbuilding workplaces, facilitating swift adaptability to market swings while ensuring high-efficiency production through continuous learning and iterative model refining.

The architecture incorporates early warning systems, adaptive parameter modification, and process optimization algorithms to mitigate operational hazards and enhance production efficiency [97]. Figure 8 depicts the DT-driven fault prediction workflow, which consists of six stages: DT modeling and calibration, model simulation and interactive testing, validation of virtual–physical consistency, inconsistency analysis, identification of fault root causes, and formulation of predictive maintenance strategies.

Health state evaluation necessitates feature extraction from both temporal and spectral domains. Time-domain analysis provides essential parameters, including the average service life of equipment/components and the standard deviations of operating lifespans. Frequency-domain characterization entails quantifying spectral amplitude distributions during the operation of equipment. These aspects are weighted according to their contributions to healthy state benchmarks, producing composite health indices that indicate the overall degradation status of equipment or components.

DT technology facilitates comprehensive digital management of industrial assets over their entire lifecycle, encompassing demand planning, procurement, operation, maintenance, and decommissioning. This platform facilitates the online administration of maintenance activities, spare parts inventory, knowledge bases, predictive maintenance, problem diagnosis, and operational optimization. Real-time monitoring guarantees precise acquisition of equipment status, thereby optimizing asset use efficiency. In a shipyard cutting workshop, the prediction of plasma cutting nozzle lifespan is accomplished by observing nozzle orifice size, usage duration, and variations in gas pressure. Principal Component Analysis (PCA) enables feature integration, whereas Long Short-Term Memory (LSTM) models predict orifice wear patterns across consecutive cutting cycles. When anticipated orifice expansion occurs above the established criteria, the system autonomously initiates replacement suggestions.

4.6. IoT-Driven DT Feedback

DTs in shipbuilding form closed-loop control circuits through the fusion of information and physical systems, enabling not only simulation but also active control of physical production processes. By integrating real-time sensor data with high-fidelity models, these systems can autonomously adjust production schedules, equipment operations, and process parameters to optimize ship hull manufacturing.

Sun proposes a dynamic scheduling method that enhances shipyard transportation efficiency through DT technology, and experimental comparisons demonstrate that the proposed method effectively improves transportation efficiency and shipbuilding efficiency [98]. Wang introduces a real-time DT flexible job shop scheduling method based on edge computing, which improves the stability and response speed of the scheduling system and has advantages in dealing with frequent abnormal disturbances during production [99]. In ship hull welding, Li implemented a DT-based weld tracking controller for large plate welds, using a virtual model to predict welding gun deviations and feeding correction commands back into the robot’s motion path and timing [100]. Giménez developed a real-time DT system for industrial collaborative robots, establishing bidirectional communication between two robots to achieve synchronized movement and accurate positioning between the virtual robot and the real robot [101].

In the application of DTs in shipbuilding workshops as described in this article, the shipbuilding DT system establishes a closed-loop control system through the deep integration of physical and virtual workshops. Sensors, logistics units, and personnel status in the physical workshop are collected in real-time via the IoT network of the bridge module and then parsed and cleaned before being injected into the DT database. This database serves as a dynamic mirroring hub, housing comprehensive information such as equipment lifecycle parameters, material trajectories, and environmental variables. The virtual workshop platform uses this database to drive 3D model construction and simulation analysis. Lightweight modeling technology enables visual mapping of workshop layout and production processes, while a model library integrating physical principles and intelligent algorithms performs a dynamic simulation of process parameters. When the simulation results deviate from physical states, the decision support module generates optimization instructions and feeds them back to the physical workshop execution layer, forming an autonomous loop of perception–storage–twin–simulation–decision–execution, as shown in Figure 9.

5. Technical Challenges

Empowered by IoT technology, DTs have catalyzed significant progress in intelligent transformation across various industries. Although DT systems exhibit considerable potential across various fields, their implementation and optimization encounter significant challenges stemming from technical complexities, cross-domain collaboration obstacles, inadequate standardization efforts, and the rigorous precision and real-time performance demands of large-scale applications.

5.1. Standardization and Unification

To successfully advocate for IoT-driven DT applications in shipbuilding, it is essential to address IoT technological standardization [102]. Standardization facilitates interoperability among systems, while fragmented protocols and diverse data formats obstruct cross-platform integration. In shipbuilding facilities, independently built legacy systems demonstrate conflicting technical requirements and data encoding standards, hindering the integration of lifecycle information from design and production to maintenance. Significant hurdles encompass incompatible communication protocols across equipment from various manufacturers, private data formats, and offline operational workflows, all of which diminish collaborative efficiency and data use.

Rocha mentions the interoperability barriers of multiple protocols prevalent in the equipment, and the ability to achieve data fusion through the JSON-LD semantic layer is forward-looking, but the solution does not cover the compatibility of private protocols in older systems, nor does it take into account the cost barriers of replacing equipment in small and medium-sized shipyards [103]. Regardless, creating standardized technical specifications—covering hardware interfaces, communication protocols, data formats, security frameworks, and software APIs—necessitates cooperation among manufacturers, technology suppliers, and research organizations. A standardized ecosystem must incorporate suppliers, logistics providers, and shipowners to facilitate real-time monitoring of production quality and material flows, coordinated production logistics planning, and anticipatory inventory management. Digital inventory systems that synchronize material inflows and outflows with real-time data streams are crucial. Addressing these difficulties will enable IoT-driven DTs for intelligent, efficient, and sustainable shipbuilding processes.

5.2. Communication Security

Ensuring communication security and privacy protection is essential for IoT-driven DT applications in shipbuilding workshops [104]. The maritime manufacturing sector manages confidential information, encompassing design schematics, production processes, essential operational parameters, and unique technology. Due to the intricacies of global supply chains and increasing cybersecurity threats, illegal data disclosure may lead to significant financial losses, reputational harm, and national security vulnerabilities.

Akpan analyses the cybersecurity challenges faced by the maritime sector; establishing real-time monitoring systems, using blockchain technology to improve communication security, designing more secure IT and OT system architectures, implementing effective authentication and access control mechanisms, deploying public key infrastructures (PKIs), and adopting spatial correlation-based jamming detection methodologies are some of the important solutions [105]. So, enterprise IT departments must use advanced security protocols, including TLS, in conjunction with fundamental protections such as firewalls and intrusion detection systems. Role-based access control (RBAC) systems must be established to limit data access and avert unlawful disclosure of sensitive operations. A comprehensive data governance system is crucial, incorporating complete lifecycle management of digital assets (e.g., design files and technical manuals), defined data protocols, and centralized metadata management.

Regular vulnerability assessments through third-party penetration testing must be performed to determine system resilience against cyberattacks. Proactive system enhancements and maintenance are essential to meet changing technological and operational demands. These procedures collectively ensure data integrity and confidentiality while reducing the danger of unwanted access.

5.3. Real-Time Performance and Reliability

Shipbuilding entails intricate machinery and high-precision operations necessitating extensive sensor installations for real-time parameter surveillance. These sensors produce extensive data streams, requiring IoT systems that can achieve ultra-low-latency transmission and processing. Conventional IoT systems frequently fall short of these requirements owing to bandwidth limitations or latency surges. In important activities, such as precision cutting, even slight delays or jitter can compromise product quality or precipitate safety problems. Organizations are implementing sophisticated networking solutions such as 5G and Time-Sensitive Networking (TSN) to tackle these difficulties. TSN’s traffic shaping prioritizes essential data, whereas 5G network slicing designates specific channels for quality inspection video streams, resulting in millisecond-level latency and improved dependability [106].

Intensive processing requirements in contexts such as thermodynamic simulations burden edge computing infrastructures. While edge computing mitigates certain latency concerns, physical and financial limitations restrict the processing capabilities of edge nodes, especially for extensive analytics. This computational deficiency can impede decision-making in real-time applications. A collaborative edge-cloud paradigm resolves this issue by utilizing cloud resources for deep learning training and assigning time-critical tasks (e.g., anomaly detection) to edge nodes, thereby enhancing overall system performance and dependability [107].

A shipbuilding-specific DT system must address humidity, EMI interference, latency in steel hulls, or signal attenuation in enclosed bays. Electromagnetic shielding is a key means of suppressing EMI interference. The latest patent from Yangzi Xinfu Shipyard in Jiangsu, China, uses fiberglass brackets combined with plastic insulating pads to physically isolate cables and reduce electromagnetic coupling through the insulating properties of the materials themselves [108]. In addition to electromagnetic shielding, EMI interference can also be suppressed by installing EMI filters or placing sensitive equipment away from sources of interference [109]. The shielding effect of a ship’s steel hull on wireless signals is primarily due to the reflection, absorption, and scattering effects of the metal structure on electromagnetic waves. Within the steel hull, technologies based on visible light communication (VLC) or laser communication can be explored, utilizing light waves to bypass the metal shielding [110].

5.4. Cross-Workshop Collaboration

In extensive shipbuilding endeavors, production workshops generally function autonomously with segregated management systems and data streams, resulting in “information silos” that hinder inter-workshop data integration. The lack of a global production data viewpoint hinders comprehensive optimization of resource allocation, scheduling, and process coordination. To resolve this, an integrated DT platform must be developed to interlink all subsystems, facilitating smooth data interchange and evidence-based decision-making throughout sessions.

A centralized DT platform utilizes IoT technologies, including API gateways, to integrate subsystems like cutting, welding, and assembly. This platform reconciles data discrepancies across design, production, and management by digitizing essential business functions—such as planning, labor hour tracking, financial management, cost control, quality assurance, precision monitoring, procurement, human resources, and maintenance—into seamlessly integrated workflows. The platform mitigates information asymmetry and facilitates real-time data exchange, hence creating cohesive collaborative processes and management frameworks that improve decision-making precision and operational efficacy. This interface enables smooth information transfer and offers extensive data visibility for accurate strategy modifications.

A WebGL-based 3D visualization dashboard, designed using the Unity engine, can be applied to improve communication and coordination amongst workshops [111]. This dashboard facilitates real-time oversight of equipment status, material flows, and production progress across workshops, accessible through PCs, mobile devices, and augmented reality glasses. The incorporation of AR-assisted assembly and 3D operational guidance facilitates remote access to production updates, markedly enhancing operational efficiency. Simultaneously, virtual reality simulation technology facilitates pre-execution testing of intricate cross-workshop operations (e.g., block lifting and assembly) within virtual settings, enabling the identification and resolution of potential issues before physical execution.

In dynamic production settings, reinforcement learning (RL) algorithms enhance global scheduling by autonomously modifying work assignments in response to real-time equipment failure alarms and material delay notifications [112]. This intelligent scheduling reduces downtime by swiftly addressing interruptions and enhances operational patterns through the examination of past data. In shipbuilding, these innovations guarantee efficient production line operations despite variable needs, thus minimizing product delivery cycles.

5.5. Artificial Intelligence

IoT sensors allow for the instantaneous collection of ship design characteristics, equipment operational status, welding stress metrics, and other extensive statistics. AI systems analyze these statistics to derive actionable insights for updating DT models [113]. Machine learning models forecast possible equipment malfunctions to facilitate preventive maintenance, whereas deep learning methodologies discern patterns in photos and videos for quality assurance [114]. Natural language processing (NLP) streamlines documentation creation and improves customer service efficacy.

The collaboration between AI and IoT enhances the intelligence of shipbuilding workshops by enabling autonomous decision-making and real-time process modifications. This integration offers powerful tools for predictive maintenance, quality assurance, and operational optimization, enhancing decision-making capabilities and efficiency in contemporary shipyards.

6. Conclusions

This article centers on the core issue of “how the Internet of Things serves as a key enabling technology for digital twin systems to drive the intelligent transformation of ship-building workshops”, establishing a comprehensive analytical framework. This issue not only focuses on the application of IoT-driven digital twins in shipbuilding but also closely aligns with industrial realities, demonstrating strong problem-oriented and practical value.

In terms of citations from the literature, the article extensively draws upon highly cited works in the fields of IoT and digital twins, including representative studies by authoritative scholars such as Grieves, Tao, and Qi. The references include over 100 entries, covering dimensions ranging from the origins of DT theory and the evolution of IoT protocols to industrial application cases, spanning multiple disciplines including engineering, information science, manufacturing systems, and data communications.

IoT-driven DT applications in shipbuilding workshops offer innovative avenues for improving production efficiency, guaranteeing quality, and attaining intelligent management. DT systems enable interconnected workshop equipment to broadcast real-time status data to a central control system using IoT. These systems observe and evaluate physical operations via data-driven simulations, minimizing downtime and enhancing processes. Engineers utilize simulation outcomes to modify operational parameters, enhancing responsiveness to variations in production workflows.

The amalgamation of IoT and DT transcends just hardware connectivity, encompassing software-driven data analytics. In workshops that necessitate the management of extensive datasets, big data technologies enable efficient data mining to detect safety hazards and productivity constraints. DT models provide early alerts for equipment anomalies and recommend predictive maintenance strategies based on past data trends. The ongoing collection of data improves prediction algorithms, increasing the precision of fault diagnosis and the effectiveness of health management.

Notwithstanding their potential, obstacles remain in the adoption of IoT-driven DT. This encompasses protocol discrepancies among diverse systems, requiring safe data encryption and access restrictions; rigorous real-time performance demands in high-precision contexts; and interoperability challenges due to data silos. Resolving these difficulties necessitates standardization, comprehensive cybersecurity frameworks, optimization of network architecture, and AI-enhanced data processing for informed decision-making.