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Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available.

With the evolution of the Internet of things and smart cities, a new trend of the Internet of simulation has emerged to utilise the technologies of cloud, edge, fog computing, and high-performance computing for design and analysis of complex cyber-physical systems using simulation. These technologies although being applied to the domains of big data and deep learning are not adequate to cope with the scale and complexity of emerging connected, smart, and autonomous systems. This study explores the existing state-of-the-art in automating, augmenting, and integrating systems across the domains of smart cities, autonomous vehicles, energy efficiency, smart manufacturing in Industry 4.0, and healthcare. This is expanded to look at existing computational infrastructure and how it can be used to support these applications. A detailed review is presented of advances in approaches providing and supporting intelligence as a service. Finally, some of the remaining challenges due to the explosion of data streams; issues of safety and security; and others related to big data, a model of reality, augmentation of systems, and computation are examined.

A Digital Twin is a virtual representation of a physical product, asset, process, system, or service that allows us to understand, predict, and optimise their performance for better business outcomes. Recently, the use of Digital Twin in industrial operations has attracted the attention of many scholars and industrial sectors. Despite this, there is still a need to identify its value in industrial operations mainly in production, predictive maintenance, and after‐sales services. Similarly, the implementation of a Digital Twin still faces many challenges. In response, a systematic literature review and analysis of 41 papers published between 2016 and 11 July 2020 have been carried out to examine recently published works in the field. Future research directions in the area are also highlighted. The result reveals that, regardless of the challenges, the role of Digital Twin in the advancement of industrial operations, especially production and predictive maintenance is highly significant. However, its role in after‐sales services remains limited. Insights are offered for research scholars, companies, and practitioners to understand the current state‐of‐the‐art and challenges, and to indicate future research possibilities in the field.

The integration of the Mobile Crowdsensing (MCS) and Industrial Internet of Things (IIoT) brings enormous volumes of data that generate significant commercial value. However, the data contain a wealth of sensitive information about devices' environmental situation and collective activities, which draws a flock of adversaries and poses an unprecedented security challenge. Furthermore, sensing gadgets deployed in the IIoT device are usually resource‐constrained and often do not have adequate 3C resources (i.e. communication, computing, caching) to run sophisticated privacy‐preserving methods, making them easier targets for attacks in data sharing. Therefore, a risk‐adaptive privacy protection scheme R‐DP for MCS‐enabled IIoT gadgets is proposed, which comprises a closed‐loop risk‐awareness process and an adaptive privacy protection method DP (a dissemination process with perturbation). The closed‐loop process dynamic awareness of risks and threats in MCS task feeds appropriate privacy protection advice to the decision‐makers for the task. In addition, DP was designed as a lightweight and risk‐adaptive privacy protection method to meet the operational needs of 3C resource‐constrained gadgets. The analysis and evaluation show that R‐DP provides satisfactory privacy protection while the availability of statistical features reaches more than 96%, and the time complexity is only O (1) for sensing gadgets.

Small and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real‐time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty‐seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future. Factors Influencing the Adoption of Industrial IoT for the Manufacturing and Production SMEs in Developing Countries. Identify the influencing factors and proposed the IIoT adoption model for manufacturing and production SMEs for the efficient and successful adoption of IIoT, with suggestions for future policymakers and government.

In the modern age of Industry 4.0 and manufacturing servitisation, energy saving and environment consciousness are regarded as vital themes in manufacturing processes to reduce carbon tax and achieve sustainable development. For the past 20 years, the concept of green manufacturing has grown from infancy to a fully formed framework agreed upon by world‐leading enterprises. With the unprecedented development of the information technology today, the industrial data collected could assist in the in‐depth study on green manufacturing, which ranges from the operations of machining tools all the way to supply chain management. The wide scope of research promises a tremendous amount of annual publications in this field. To better facilitate follow‐up research work, the present study provides a systematic overview of green manufacturing‐related areas, including research progress and the developed features. The article set retrieved from the Web of Science contains 5989 documents related to green manufacturing. It is revealed that Journal of Cleaner Production is the most productive journal, archiving documents within the scope of green manufacturing. P. R. China tops the list of the number of documents with 1357 documents (22.66%), while Zhejiang University is the most productive institution. As the cooperation network indicates, P. R. China and the United States maintain the strongest collaborative links with other countries/regions. Finally, possible future directions are recommended based on the findings in the study. For instance, additive manufacturing technology and industrial IoT both have a great potential in green manufacturing; the weak link between the disciplines of manufacturing engineering and environmental science is expected to be strengthened, and a stronger international cooperation is believed to be beneficial to the field for the otherwise isolated countries/regions.

Lithium‐ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium‐ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real‐time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real‐time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins‐based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.

Efficient sequencing of processes and resource allocation are critical in production planning scenarios, such as manufacturing workshops and construction projects, to enhance efficiency and reduce operational costs. Resource allocation in such environments is often challenged by temporal constraints, process interdependencies, and resource limitations, which complicate scheduling and increase the risk of delays. This study presents a multi‐agent‐based simulation system to address these challenges. A scheduling optimisation model is developed to simulate and optimise resource allocation in complex processes with network structures and temporal constraints. The primary objective is to minimise production completion time while ensuring effective resource allocation. Additionally, an adaptive, partially distributed Agent‐Based Modelling and Simulation framework is proposed to simulate the execution logic of real‐world processes, integrating key factors such as resource limitations, process interdependencies, and real‐time decision‐making. A priority‐based genetic algorithm is also designed and embedded into the multi‐agent system to further optimise process sequencing and resource distribution. Simulation experiments across varying case scales validate the model and algorithm. This study highlights the potential of agent‐based simulation for solving complex engineering challenges and provides new insights for addressing resource allocation problems in network‐structured, time‐constrained environments. A multi‐agent‐based simulation system developed to simulate and optimise resource allocation in complex processes with network structures and temporal constraints. The primary objective is to minimise production completion time while ensuring effective resource allocation.

In competitive growth and Industry 4.0, construction prediction and management have a key role. To find a way to provide a simulation method for the damage assessment of buildings and Industry 4.0, building information modelling technology is the most suitable choice. This work presents and analyses the building material from design modelling to model information extraction, virtual construction, and an imported virtual simulation engine. A simulation system has been built to understand the force and material collision detection of buildings, and a three‐dimensional (3D) simulation platform is developed based on the Unity3D engine. A 3D display of building model and simulation data is realized in this work based on the simulation software platform. The results show that the building 3D simulation images constructed by the designed system are high definition, take little time, and have excellent performance. The outcomes are realized in terms of the engineering cost ratio and have energy consumption and efficiency values of 20% and 40%, respectively, which are much better than the traditional method. Efficiency has also improved to 76% from the traditional method using the proposed method, which makes it a robust platform for construction prediction and management in industries. The virtual simulation technology is applied to solve problems of building design and damage assessment. The influence of this technology on the overall design of the building is discussed, followed by future development directions for industrial automation.

Superalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro‐Fuzzy Inference System for forecasting WEDM parameters using grey‐based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision‐making in the production of high‐quality biomedical devices and components. A comparative study of the prophesied values with the experimentation data has shown a high degree of agreement, according to the analysis. The results of the performance evaluation show that the upgraded system works as intended, so the maker may make an intelligent decision.