Additive manufacturing (AM) is widely used in fields, such as aerospace and medical treatment. However, the massive heterogeneous data generated during its production process face challenges, such as high transmission latency and large energy consumption. This article proposes a sustainable intelligent optimization strategy based on edge–cloud collaboration to enhance the intelligence and sustainability of AM. First, a hybrid model that integrates the local feature extraction of convolutional neural network (CNN) and the global dependency modeling of transformer (CNN–transformer) is designed to accurately predict the key process parameters of AM. Second, a multiobjective optimization model for surface roughness, processing time, and energy consumption is constructed. Combined with the improved Pareto set learning (PSL) algorithm, the collaborative optimization of economic and environmental sustainability is achieved. Finally, verification is carried out on selective laser melting (SLM) technology. The experimental results show that the prediction error of the CNN–transformer is lower than that of traditional models. It can reduce energy consumption and processing time while ensuring surface quality, thus providing a systematic solution for green intelligent manufacturing from Industry 4.0 to Industry 5.0.

Porosity is a common defect in the Laser Powder Bed Fusion (LPBF) process, significantly limiting its potential in mass customization applications. Existing studies have shown that porosity can be effectively detected by analyzing the thermal history of the melt pool. However, the relationship between thermal features extracted from thermograms and porosity is highly nonlinear, making defect prediction challenging. This paper proposes a Convolutional Neural Network (CNN)-integrated Transformer (CiT) model for defect prediction and anomaly localization in LPBF. The CiT model enhances predictive accuracy by combining global and local feature extraction while leveraging global average pooling instead of the classification (CLS) token after removing the Transformer decoder. Additionally, a lightweight multi-head self-attention mechanism is designed to optimize the model structure, effectively reducing the number of parameters while maintaining accuracy. Furthermore, an anomaly localization method based on Score-CAM is introduced to identify potential causes of porosity formation, enabling defect traceability in LPBF. The proposed CiT model is evaluated against four state-of-the-art algorithms, demonstrating its superior performance in defect prediction and anomaly localization.

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy.

Proactive human-robot collaborative (HRC) assembly has caught great attention as emerging paradigm for flexible mass personalization in manufacturing with respect to Industry 5.0. To realize adaptive and ergonomic collaboration, it is essential to enable robots recognize human assembly intention based on context-aware information precisely at edge side with Industrial Internet of Thing, also known as human assembly intention recognition (HAIR). For this purpose, this paper systematically reviewed the most relevant papers from major digital databases, where 127 papers are investigated with designed search procedure that published until July 2024. And reviewed papers are summarized from the perspective of: (1) assembly scene perception based on multimodalities data; (2) understanding of HAIR based on machine learning; (3) HAIR application for HRC process. In addition, four current challenges and future research trends are also discussed to facilitate full-adaptive and mutual-cognitive HRC assembly environments.

3-D printing, or additive manufacturing (AM), leverages 3-D computer-aided design models and numerical control to produce objects layer-by-layer, playing a key role in Industry 4.0 and Industry 5.0. Despite its potential to revolutionize manufacturing by creating complex structures more efficiently and cost-effectively, 3-D printing still faces quality issues due to a lack of sufficient data, resulting in improper process parameter settings and poor analyzability. This work introduces a data-driven and physics-assisted machine learning (DP-ML) approach for a 3-D-printed BeltClip object, integrating finite element analysis (FEA) and physics-informed machine learning (PIML). The proposed DP-ML framework provides a cost-effective and time-efficient data collection method using Digimat-AM and a warpage classification algorithm. The data collection begins with obtaining the STereoLithography (STL) file of the BeltClip object from Thingiverse and slicing it in Ultimaker© Cura, considering process parameters such as infill amount, toolpath pattern, layer height, print speed, and extrusion temperature. The resulting G-code file is then input into Digimat-AM for further parameter setting and analysis. In Digimat-AM, glass fiber-filled and unfilled material types are set, undergoing the virtual 3-D printing process, followed by a warpage analysis of the printed BeltClip. The collected 3-D printing data is used to build ML models—deep neural network (DNN), decision tree (DT), support vector machine (SVM), logistic regression (LR), and random forest. The DNN contains three architectures—DNN-1, DNN-2, and DNN-3. Based on the metrics of precision, recall, F1-score, and accuracy, DNN-3 outperforms the others and is chosen for the warpage classification algorithm. The presented DP-ML approach is compared with the state-of-the-art methods and shows a promising capability to predicting warpage, optimizing process parameters, and improving the overall quality and efficiency of a 3-D-printed BeltClip.

With the increasing demand for automotive glass, improving the efficiency of automotive glass manufacturing systems can make full use of production resources and reduce the waste of natural and social resources. Therefore, this work aims to provide an efficient method for a real-world two-stage hybrid flow shop scheduling problem with small batches in an automotive glass manufacturing system. For the investigated problem, there is a significant setup time at the first stage, the second stage is served by machines that should not be interrupted, and each batch has a due date. Such constraints make this scheduling problem challenging. To solve this problem, a mixed integer linear programming model is established. Also, two properties and three theorems are given to enhance the problem-solving process. Subsequently, an efficient genetic algorithm is carefully designed to solve large-scale problems by considering the properties of the system. Meanwhile, an improvement scheme is proposed to decrease the running time of the algorithm, and experimental results show that this scheme can reduce the running time by 280 s at most from the average results of different scale problems . Finally, extensive experiments are carried out and a real-world case is solved to demonstrate the efficiency and effectiveness of the designed genetic algorithm. Also, the Taguchi method is adopted to tune the parameters for the designed algorithm.

Social manufacturing leverages the power of social networks and collaborative processes to enhance manufacturing capabilities and supports the sharing of ideas, resources, and information. However, traditional remote maintenance under a social manufacturing context lacks resilience against disruptions and cyber attacks. These issues often lead to interruptions in production. This article proposed blockchain-of-things-based edge learning contracts for federated predictive maintenance (FPM). First, given the diversity and heterogeneity of equipment, an open platform communication unified architecture (OPCUA)-based equipment meta-model is proposed to facilitate the interconnection and data sharing. Second, a Blockchain-of-Things-based secure access control approach is proposed, to directly collect data from controllers. This approach prevents tampering, unlike traditional local database collection methods. Third, to address the security and efficiency needs, an edge learning contract method is proposed for FPM. An integrated learning algorithm based on smart contracts is designed to achieve prediction performance that is comparable to local centralized training while reducing data transmission load and enhancing data security. Finally, a federated predictive maintenance platform is designed and implemented to enhance the system’s resilience, and its effectiveness is verified through case studies.

Additive manufacturing (AM), also called 3-D printing, is a supporting technology in social manufacturing that has gained significant attention recently. As the AM industry grows, collecting and analyzing data are essential to ensure product quality, process efficiency, and cost-effectiveness. However, obtaining experimental data is challenging owing to cost and time constraints. Therefore, cost-effective and time-efficient strategies for collecting AM data are urgently required. This study proposes a novel data-collection approach that integrates the concept of finite element analysis (FEA) and physics-informed machine learning (PIML). We begin by discussing the importance of data collection in AM and the associated challenges. We then present various types of data that can be collected in AM, including the 3-D models and end-to-end data. End-to-end data comprise experimental data (i.e., sensors and images) and simulation data. Moreover, we present a case study that demonstrates the generation of simulation data and provides a detailed analysis of warpage. The STereoLithography (STL) file format of the BeltClip object from the Thingiverse possesses slicing through the Ultimaker© Cura software. The resulting G-code file is input to the Digimat-AM platform for virtual simulation of the BeltClip printing process. Digimat-AM, as a FEA simulation tool, then generates observational sample data. These data function as a roadmap for understanding the application of physical information for learning, which constitutes the observational bias aspect of PIML. The observational data obtained from the Digimat-AM is suggested for building a machine-learning model. Finally, we conclude with a discussion of inductive and learning biases in the prediction, control, and optimization aspects of AM.

The utilization of cloud infrastructure and its extensive range of Internet-accessible resources holds significant potential for advancing intelligent transportation and robotics. Over the past two decades, interest in cloud-edge collaborative simultaneous localization and mapping (SLAM) has grown markedly. Consequently, a comprehensive review of current trends in this field is crucial for both novice and experienced researchers. This paper examines robots and automation systems that rely on network-based data or code, particularly in the context of SLAM development. Applying SLAM to mobile robots with limited computing power is essential for achieving autonomous navigation, and cloud-edge collaborative SLAM has emerged as an efficient solution. The review is structured around four key benefits of cloud-edge collaborative SLAM: Assisted Cloud Computing, which provides access to cloud computation and reduces the burden on edge devices; Total Cloud Computing, where the majority of computation is offloaded to the cloud, while edge devices primarily handle sensing and low-cost pre-processing; Data Storage, enabling access to large datasets, such as high-resolution environment maps and extensive training datasets, enhancing overall performance; and Data Transmission, involving cloud-edge communication for efficient data transfer and data association. Additionally, we address the challenges in existing work and the development of asynchronous collaboration and implicit representation transmission, which could mitigate transmission latency in communication-constrained environments. We believe that this review will bridge the gap between SLAM systems and deployed robotic systems, promoting the advancement of cloud-edge collaborative SLAM.

Mass individualized prototyping (MIP) is a kind of advanced and high-value-added manufacturing service. In the MIP context, the service providers usually receive massive individualized prototyping orders, and they should keep a stable state in the presence of continuous significant stresses or disruptions to maximize profit. This article proposed a graph convolutional neural network-based deep reinforcement learning (GCNN-DRL) method to achieve the resilient production control of MIP (RPC-MIP). The proposed method combines the excellent feature extraction ability of graph convolutional neural networks with the autonomous decision-making ability of deep reinforcement learning. First, a three-dimensional disjunctive graph is defined to model the RPC-MIP, and two dimensionality-reduction rules are proposed to reduce the dimensionality of the disjunctive graph. By extracting the features of the reduced-dimensional disjunctive graph through a graph isomorphic network, the convergence of the model is improved. Second, a two-stage control decision strategy is proposed in the DRL process to avoid poor solution quality in the large-scale searching space of the RPC-MIP. As a result, the high generalization capability and efficiency of the proposed GCNN-DRL method are obtained, which is verified by experiments. It could withstand system performance in the presence of continuous significant stresses of workpiece replenishment and also make fast rearrangement of dispatching decisions to achieve rapid recovery after disruptions happen in different production scenarios and system scales, thereby improving the system’s resilience.

In Industry 5.0 vision, machines are empowered with the interaction capability to autonomously make local decisions and coordinate with each other as well as humans. However, how to form a group consensus on the rapid self-organizing of the manufacturing process is critical for achieving manufacturing resilience under disturbances and disruptions. Based on our formerly developed system ManuChain (Leng et al., 2020), this article proposes a blockchained smart contract system (BSCS), named ManuChain II, as the digital twin of a decentralized autonomous manufacturing system for achieving resilience in Industry 5.0. A blockchain-secured multiagent system architecture together with a product data model is established to form the BSCS. The BSCS could prevent tampering with data and enhance the transparency of the product manufacturing process. In BSCS, two types of blockchained smart contracts (SCs) are established with a bi-level interplay computing architecture. The lower-level contracts perform the predefined and learned patterns of task coordination for achieving resilience under internal disruptions. The upper-level contracts are incorporated with communication-efficient decentralized deep-learning algorithms for the learning, updating, transferring, and sharing of coordination patterns used in the autonomous decision in lower-level SCs, thereby achieving the continuous improvement of the system’s decentralized autonomous intelligence. Via incorporating the decentralized deep learning algorithms into blockchained SCs, this study reveals a new way to realize the self-organizing intelligence of the manufacturing system for enhancing resilience toward Industry 5.0.

[Purpose/meaning] In this paper, a unified scheme based on blockchain technology to realize the three modules of intellectual property confirmation, utilization, and protection of rights at the application layer is constructed, to solve the problem of unbalanced and inadequate resource distribution and development level in the field of industrial intellectual property. [Method/process] Based on the application of the core technology of blockchain in the field of intellectual property, this paper analyzes the pain points in the current field of intellectual property, and selects matching blockchain types according to the protection of intellectual property and the different decisions involved in the transaction process, to build a heterogeneous multi-chain model based on blockchain technology. [Conclusion] The heterogeneous multi-chain model based on Polkadot[1] network is proposed to realize the intellectual property protection scheme of a heterogeneous multi-chain model, to promote collaborative design and product development between regions, and to make up for the shortcomings of technical exchange, and weaken the phenomenon of "information island" in a certain extent. [Limitation/deficiency] The design of smart contracts in the field of intellectual property, the development of cross-chain protocols, and the formulation of national standards for blockchain technology still need to be developed and improved. At the same time, the intellectual property protection model designed in this paper needs to be verified in the application of practical cases.

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey, we examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines.

Blockchain is a new generation of secure information technology that is fueling business and industrial innovation. Many studies on key enabling technologies for resource organization and system operation of blockchain-secured smart manufacturing in Industry 4.0 had been conducted. However, the progression and promotion of these blockchain applications have been fundamentally impeded by various issues in scalability, flexibility, and cybersecurity. This survey discusses how blockchain systems can overcome potential cybersecurity barriers to achieving intelligence in Industry 4.0. In this regard, eight cybersecurity issues (CIs) are identified in manufacturing systems. Ten metrics for implementing blockchain applications in the manufacturing system are devised while surveying research in blockchain-secured smart manufacturing. This study reveals how these CIs have been studied in the literature. Based on insights obtained from this analysis, future research directions for blockchain-secured smart manufacturing are presented, which potentially guides research on urgent cybersecurity concerns for achieving intelligence in Industry 4.0.

The growth of individualized product demands drives high flexibility of manufacturing processes, which requires large-scale deployment of Industrial Internet of Things (IIoT). Since centralized control of IIoT suffers from poor flexibility in coping with disturbances and changes, a decentralized organization structure is a better choice, in which a permissioned blockchain-driven IIoT can enable partially decentralized self-organization and thus offload and accelerate the optimization of upper-level manufacturing planning. A novel iterative bi-level hybrid intelligence model named ManuChain is proposed to get rid of unbalance/inconsistency between holistic planning and local execution in individualized manufacturing systems. Lower-level blockchain-driven smart contracts proactively decentralize fine-grained and individualized task execution among machine tools via Raspberry Pi-based smart gateways and make the results available on an upper-level digital twin model for iterative coarse-grained holistic optimization. A prototype ManuChain based on a permissioned blockchain network is presented to realize both lower-level crowd self-organizing intelligence and upper-level holistic optimization intelligence.

There is an increasing use of social interaction contexts in the cross-enterprise manufacturing problem solving. To transform these massive and unstructured data into decision-support information for cross-enterprise manufacturing demand-capability matching, we present automated solutions to two phases: (1) extracting relationships based on a semi-supervised learning approach to derive formalized heterogeneous manufacturing network from the unstructured text-based context that contains high levels of noise and irrelevant information and (2) matching group-level relationships among the entities in the established manufacturing network. The extracting phase formulates network data using multiattributed graph that can encode various entities and relationships. The matching phase is based on probabilistic multiattributed graph matching, and implemented using distributed message passing algorithm. We developed a prototype system to verify the proposed model, which is also flexible to new domains of contexts and scale to large datasets. The ultimate goal of this paper is to facilitate knowledge transferring and sharing in the context of cross-enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprise.