Computers in Industry最新文献

Structural components produced through hot stamping of lightweight materials, such as aluminium alloys, play a pivotal role in mass reduction, leading to decreased CO₂ emissions and enhanced fuel efficiency, especially in applications such as electric vehicles, high-speed trains, and aircraft. Concurrently, the hot stamping process is experiencing an exponential increase in data generation, stemming from ongoing production, research, and development activities. Yet, translating the inherent values of these voluminous metadata into scientific innovations and industrial breakthroughs requires the emerging expertise by consolidating the knowledge of hot stamping and data science. Here, the authors have conceptualised and developed the digital characteristics (DC) for manufacturing processes. The DC serves as the ‘DNA’ of every manufacturing process by encompassing its inherent and distinctive natures spanning over the design, manufacturing and application phases of the manufactured products. Focusing on the hot stamping process, the authors have developed the unique DC from voluminous hot stamping data derived from experimentally validated simulations and sensing networks. Results demonstrate that the DC revealed the distinct evolutionary thermo-mechanical characteristics of the hot stamping process in terms of representative geometric features, which facilitates the fundamental scientific understanding and unlocks the potential on implementing data-centric scientific innovations in advanced manufacturing paradigms.

The Digital Twin paradigm is a very promising technology that can be applied to various fields and applications. However, it lacks a unifying framework for classifying and defining use cases. The goal of this paper is to address the identified gap. Using a field study and a bottom-up approach, it aims to categorize the various uses of the industrial Digital Twin to help formalize the concept and rationalize its adoption by a range of industrial sectors. The study is based on an iterative process of collecting use cases from a wide variety of verticals, applying grounded theory principles. The usage scenarios were extracted, synthesized, grouped and abstracted to develop an actionable use cases classification framework. This article presents the resulting taxonomy and illustrates it by detailing real industrial use cases, including their value proposition and application areas. This collection, classification and analysis of use cases led to a study of the common aspects proposed in academic and industrial definitions of the Digital Twin. The goal was to combine and generalize these aspects into a pragmatic and unifying definition, on which the Alliance for Industry of the Future (AIF) committee has converged. The main contributions of this work include proposing, from a joint industrial and academic perspective, (i) the first domain-independent and industry-focused systematic collection of Digital Twin use cases, (ii) a comprehensive framework for analyzing and classifying Digital Twin use cases and their requirements, and (iii) a consensual general definition of the industrial Digital Twin to contribute to the structuring and standardization of this very active ecosystem.

The introduction of assistive construction robots can significantly alleviate physical demands on construction workers while enhancing both the productivity and safety of construction projects. Leveraging a Building Information Model (BIM) offers a natural and promising approach to driving robotic construction workflows. However, because of uncertainties inherent in construction sites, such as discrepancies between the as-designed and as-built components, robots cannot solely rely on a BIM to plan and perform field construction work. Human workers are adept at improvising alternative plans with their creativity and experience and thus can assist robots in overcoming uncertainties and performing construction work successfully. In such scenarios, it is critical to continuously update the BIM as work processes unfold so that it includes as-built information for the ensuing construction and maintenance tasks. This research introduces an interactive closed-loop digital twin framework that integrates a BIM into human-robot collaborative construction workflows. The robot’s functions are primarily driven by the BIM, but it adaptively adjusts its plans based on actual site conditions, while the human co-worker oversees and supervises the process. When necessary, the human co-worker intervenes in the robot’s plan by changing the task sequence or workspace geometry or requesting a new motion plan to help the robot overcome the encountered uncertainties. A drywall installation case study is conducted to verify the proposed workflow. In addition, experiments are carried out to evaluate the system performance using an industrial robotic arm in a research laboratory setting that mimics a construction site and in the Gazebo simulation. Integrating the flexibility of human workers and the autonomy and accuracy afforded by the BIM, the proposed framework offers significant promise of increasing the robustness of construction robots in the performance of field construction work.

Large numbers of maintenance Work Request Notification (WRN) records are created by industry as part of standard business work flows. These digital records hold invaluable insights crucial to best practice in asset management. Of particular interest are the cause–effect relations in the long text WRN field. In this research we develop a two-stage deep learning pipeline to extract cause-and-effect triples and construct a causal graph database. A novel sentence-level noise removal method in the first stage filters out information extraneous to causal semantics. The second stage leverages a joint entity-and-relation extraction model to extract causal relations. To train the noise removal and causality extraction models we produced an annotated dataset of 1027 WRN records. The results for causality extraction as measured by F1-score are 83% and 92% for the identification of Cause and Effect entities respectively, and 78% for a correct causal relation between these entities. The pipeline is applied to a real-word, industrial plant dataset of 98,000 WRN records to produce a graph database. This work provides a framework for technical personnel to query the causes of equipment failures enabling answers to questions such as “what are the most common, costly, and recent causes of failures at my facility?”.

Effective fault detection and diagnosis (FDD) is crucial for proactively identifying irregular states that could jeopardize operator well-being and process integrity. In the era of Industry 4.0, data-driven FDD techniques have received particular attention, driven by the proliferation of stored manufacturing sensor data. While these methods have proven adept at categorizing established process fault scenarios, there remains an imperative to identify and explain anomalies stemming from uncharted faults or the interplay of consecutive anomalies. To address this we present a knowledge-enhanced FDD approach that integrates well-defined chemical engineering knowledge with cutting-edge deep learning techniques. We apply our methodology, named Knowledge-Enhanced Spatiotemporal Analysis (KESA), to identify abnormal process conditions that may be a precursor to failure. Furthermore, we utilize the knowledge of the fundamental relationships governing the process to explain why this fault case has occurred. This type of in-depth fault analysis is only possible through leveraging domain expertise and marks a step forward in FDD technology in comparison to current literature. Using the benchmark Tennessee Eastman process dataset, we establish superiority in the accuracy and efficiency of our KESA model against state-of-the-art FDD algorithms. This work highlights the importance of a knowledge-enhanced approach to deep learning in complex environments, emphasizing the critical role of timely and interpretable fault detection. By providing explanations for model results, our KESA framework not only aids in effective decision-making but also has the potential to significantly reduce the time between fault detection and the implementation of proactive mitigation actions. This capability is paramount for improving overall safety, minimizing downtime, and ultimately contributing to substantial cost savings in industrial processes.

Ultrasound welding technology is widely applied in the field of industrial manufacturing. In complex working conditions, various factors such as welding parameters, equipment conditions and operational techniques contribute to the formation of diverse and unpredictable line defects during the welding process. These defects exhibit characteristics such as varied shapes, random positions, and diverse types. Consequently, traditional defect surface detection methods face challenges in achieving efficient and accurate non-destructive testing. To achieve real-time detection of ultrasound welding defects efficiently, we have developed a lightweight network called the Lightweight Attention Detection Network (LAD-Net) based on an attention mechanism. Firstly, this work proposes a Deformable Convolution Feature Extraction Module (DCFE-Module) aimed at addressing the challenge of extracting features from welding defects characterized by variable shapes, random positions, and complex defect types. Additionally, to prevent the loss of critical defect features and enhance the network's capability for feature extraction and integration, this study designs a Lightweight Step Attention Mechanism Module (LSAM-Module) based on the proposed Step Attention Mechanism Convolution (SAM-Conv). Finally, by integrating the Efficient Multi-scale Attention (EMA) module and the Explicit Visual Center (EVC) module into the network, we address the issue of imbalance between global and local information processing, and promote the integration of key defect features. Qualitative and quantitative experimental results conducted on both ultrasound welding defect data and the publicly available NEU-DET dataset demonstrate that the proposed LAD-Net method achieves high performance. On our custom dataset, the F1 score and [email protected] reached 0.954 and 94.2%, respectively. Furthermore, the method exhibits superior detection performance on the public dataset.

The development of Industry 5.0 focuses on customization, personalization in production, and the innovative thinking of employees, elevating the value of human contribution. Design, being an innovation-driven domain, demands greater flexibility in resource allocation. Consequently, rapidly and effectively allocating cloud service resources for personalized design tasks becomes crucial. With the emergence of the Industrial Metaverse, which blurs the boundaries between real and virtual design and manufacturing, it is gaining increasing attention. To embrace the advent of Industry 5.0 and the Industrial Metaverse, swift collaborative cloud services for design and manufacturing resources are essential. In this context, this article introduces a novel approach combining complex networks with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), aimed at rapidly optimizing the dynamic allocation of distributed design resources (DRs). Initially, a multipartite graph is created from raw data and mapped to multiple bipartite graphs to identify key nodes in the network through intersection. Subsequently, these key nodes are used as reference points in the NSGA-III algorithm to achieve high-quality cloud service combinations, meeting the needs of design tasks with multiple subtasks, and related multi-objective optimization, including time, cost, reliability, maintainability, and reputation associated with the design. Finally, the Pareto service combinations obtained are used to construct a new complex network and employ the Girvan-Newman algorithm based on edge betweenness to identify community structures. In case of anomalies in the best service combination, alternative options can be swiftly searched from the identified communities, thereby enhancing the resilience of the cloud service process. Experimental results demonstrate the method's advantages in recovery and robustness, contributing significantly to the optimization of rapid cloud service allocation for DRs in the context of Industry 5.0.

Organizations use business process management systems to automate processes that they use to perform tasks or interact with customers. However, several variants of the same business process may exist due to, e.g., mergers, customer-tailored services, diverse market segments, or distinct legislation across borders. As a result, reliable support for process variability has been identified as a necessity. In this article, we introduce the concept of declarative process families to support process variability and present a procedure to formally verify whether a business process model is part of a specified process family. The procedure allows to identify potential parts in the process that violate the process family. By introducing the concept of process families, we allow organizations to deviate from their prescribed processes using normal process model notation and automatically verify if such a deviation is allowed. To demonstrate the applicability of the approach, a simple example process is used that describes several variants of a car rental process which is required to adhere to several process families. Moreover, to support the proposed procedure, we present a tool that allows business processes, specified as Petri nets, to be verified against their declarative process families using the NuSMV2 model checker.

Employee attrition and absenteeism are major problems that affect many industries and organizations, resulting in diminished productivity, elevated costs, and losses. These phenomena can be attributed to multiple factors that are difficult to anticipate for human resources or management. Therefore, this paper proposes a content-based methodology for the analysis and classification of employee attrition and absenteeism that can be used for talent analysis and management, a task that is traditionally carried out ex-post. The developed methodology, called E(3A)CSPM, is based on SPM (sequential pattern mining). In the methodology, four public datasets with diversified employee data are adopted, which are initially transformed into a suitable format. Then, SPM algorithms are applied to the transformed datasets to reveal recurring patterns and rules of features. The discovered patterns and rules not only offer information regarding features that have a key role in employee attrition and absenteeism but also their values. These frequent patterns of features are thereafter used to classify/predict employee attrition and absenteeism. Eight classifiers and multiple evaluation metrics are used in experiments. The performance of E(3A)CSPM is contrasted with state-of-the-art approaches for employee attrition and absenteeism and the obtained findings reveal that E(3A)CSPM surpasses these approaches.

This study presents a framework for the real-time detection of surface cracking in large-sized stamped metal parts. The framework aims to address the challenges of low detection efficiency and high error rates associated with manual cracking detection. Within this framework, a novel network, SNF-YOLOv8, is proposed to efficiently detect cracking while ensuring that the detection speed matches the production speed. The network incorporates a convolutional spatial-to-depth module to enhance the detection of small-sized cracking and mitigate surface interference during inspections. Furthermore, a visual self-attention mechanism is introduced to improve feature extraction. A combination of standard convolutional and depth-wise separable convolutional layers in the neck network enhances speed without compromising accuracy. Experimental validation conducted using a dataset from actual production lines, in collaboration with a multi-national corporation, demonstrates that SNF-YOLOv8 achieves an average precision of 85.2% at a detection speed of 164 frames per second. The framework achieves an accuracy rate of 98.8% in detecting large-sized cracking and 96.4% in detecting small-sized cracking, meeting the requirements for high-precision and real-time detection applications.