As semiconductor manufacturing advances into ultra-scaled technology nodes, Static Random Access Memory (SRAM) verification faces critical challenges stemming from increasingly stringent geometries, heightened process variability, and the sheer density of contemporary designs. Existing methodologies, notably Design Rule Checking and conventional pattern matching, often fall short in capturing the intricate multilayer interactions and subtle geometric deviations characteristic of advanced SRAM layouts. These limitations can lead to missed layout anomalies and potential functional failures, resulting in diminished yield. This paper introduces a specialized pattern-matching-based algorithm designed to address these challenges in SRAM verification. The algorithm integrates novel localization techniques, efficient spatial indexing, and an overlap detection procedure that together achieve exact verification while delivering substantial improvements in runtime performance. Experimental results confirm that this approach not only maintains 100% detection accuracy on complex SRAM benchmarks but also achieves speedups ranging from threefold to over twentyfold compared to state-of-the-art methods, and from twofold to several thousandfold on classic single-layer pattern matching datasets.

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.

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.

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.

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.

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.

Because the authentication method based on username‐password has the disadvantage of easy disclosure and low reliability and the excess password management degrades the user experience tremendously, the user is eager to get rid of the bond of the password in order to seek a new way of authentication. Therefore, the multifactor biometrics‐based user authentication wins the favor of people with advantages of simplicity, convenience, and high reliability. Now the biometrics‐based (especially the fingerprint information) authentication technology has been extremely mature, and it is universally applied in the scenario of the mobile payment. Unfortunately, in the existing scheme, biometric information is stored on the server side. As thus, once the server is hacked by attackers to cause the leakage of the fingerprint information, it will take a deadly threat to the user privacy. Aiming at the security problem due to the fingerprint information in the mobile payment environment, we propose a novel multifactor two‐server authenticated scheme under mobile cloud computing (MTSAS). In the MTSAS, it divides the authentication method and authentication means; in the meanwhile, the user’s biometric characteristics cannot leave the user device. Thus, MTSAS avoids the fingerprint information disclosure, protects user privacy, and improves the security of the user data. In the same time, considering user actual requirements, different authentication factors depending on the privacy level of authentication are chosen. Security analysis proves that MTSAS has achieved the authentication purpose and met security requirements by the BAN logic. In comparison with other schemes, the result shows that MTSAS not only has the reasonable computational efficiency, but also keeps the superior communication cost.

The target of process mining is to automatically extract process models from event logs related to actual business process executions. One of the most important fields concerned is control flow mining, i.e., ordering of activities. However, the presence of complicated constructs, such as duplicate tasks, invisible tasks and non-free-choice structures, hinders us from correctly discovering the relations between activities. Therefore, an improved simulated annealing approach is proposed in this paper to tackle these problems. To verify the performance, experiments are conducted in the process minig framework. The result is expressed in terms of Petri net.

Sentence similarity computation has been used widely in the field of information processing. Many methods have been proposed to measure the similarity of sentences, but they focus mainly on one or two features, e.g. words, structure or semantic information and so on. The accuracy of these methods is usually lower. In this paper, we present a new approach to compute the similarity of sentences based on feature set. This method defines the key features in similarity computation and then combines their contribution to obtain the sentence similarity. Experiments show that this method has higher accuracy in sentence similarity computation.