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A blockchain-based deployment framework for protecting building design intellectual property rights in collaborative digital environments Comput. Ind. (IF 10.0) Pub Date : 2024-04-20 Weisheng Lu, Liupengfei Wu
Protecting intellectual property rights (IPR) in the architecture, engineering, and construction (AEC) industry is a long-standing challenge. In the collaborative digital environments, where multiple professionals use digital platforms such as building information modelling to collaborate on a building design, this challenge has intensified. This research harnesses the functions of blockchain technology
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E-fulfillment cost management in omnichannel retailing: An exploratory study Comput. Ind. (IF 10.0) Pub Date : 2024-04-05 Miguel Rodríguez-García, Iria González-Romero, Ángel Ortiz-Bas, José Carlos Prado-Prado
The purpose of this study is twofold: investigating how omnichannel (OC) retailers manage e-fulfillment costs and establishing how these costs relate to the evolution of OC retailers' e-fulfillment strategies. Experts in e-fulfillment from 34 European OC retailers across various sectors participated in an exploratory survey. The study's results reveal that although e-fulfillment costs significantly
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Assessing user performance in augmented reality assembly guidance for industry 4.0 operators Comput. Ind. (IF 10.0) Pub Date : 2024-03-28 Emanuele Marino, Loris Barbieri, Fabio Bruno, Maurizio Muzzupappa
In the realm of smart manufacturing, Augmented Reality (AR) technology has gained increasing attention among researchers and manufacturers due to its practicality and adaptability. For this reason, it has been widely embraced in various industrial fields, especially for helping operators assemble products. Despite its widespread adoption, there is a debate in the research community about how effective
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A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines Comput. Ind. (IF 10.0) Pub Date : 2024-03-26 Yaqing Xu, Yassine Qamsane, Saumuy Puchala, Annette Januszczak, Dawn M. Tilbury, Kira Barton
Efficient performance monitoring in production systems holds paramount importance as it enables organizations to optimize their manufacturing processes, enhance productivity, and maintain a competitive edge in the market. Typically, machine and system level performance monitoring systems are investigated independently, whereas an integrated approach that considers both levels can offer valuable insights
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Construction contract risk identification based on knowledge-augmented language models Comput. Ind. (IF 10.0) Pub Date : 2024-03-22 Saika Wong, Chunmo Zheng, Xing Su, Yinqiu Tang
Contract review is an essential step in construction projects to prevent potential losses. However, the current methods for reviewing construction contracts lack effectiveness and reliability, leading to time-consuming and error-prone processes. Although large language models (LLMs) have shown promise in revolutionizing natural language processing (NLP) tasks, they struggle with domain-specific knowledge
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Unlocking maintenance insights in industrial text through semantic search Comput. Ind. (IF 10.0) Pub Date : 2024-03-21 Syed Meesam Raza Naqvi, Mohammad Ghufran, Christophe Varnier, Jean-Marc Nicod, Kamran Javed, Noureddine Zerhouni
Maintenance records in Computerized Maintenance Management Systems (CMMS) contain valuable human knowledge on maintenance activities. These records primarily consist of noisy and unstructured texts written by maintenance experts. The technical nature of the text, combined with a concise writing style and frequent use of abbreviations, makes it difficult to be processed through classical Natural Language
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Style Adaptation module: Enhancing detector robustness to inter-manufacturer variability in surface defect detection Comput. Ind. (IF 10.0) Pub Date : 2024-03-20 Chen Li, Xiakai Pan, Peiyuan Zhu, Shidong Zhu, Chengwei Liao, Haoyang Tian, Xiang Qian, Xiu Li, Xiaohao Wang, Xinghui Li
In recent years, deep learning-based approaches for industrial surface defect detection have shown great promise. To address the domain shift issue among data from different sources in the industrial domain, we present a novel plug-and-play Style Adaptation (SA) module, which endows the equipped defect detector with the capability to exhibit robustness to diverse styles present within the samples.
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“What’s Going On” with BizDevOps: A qualitative review of BizDevOps practice Comput. Ind. (IF 10.0) Pub Date : 2024-03-08 Pedro Antunes, Mary Tate
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Process parameter effects estimation and surface quality prediction for selective laser melting empowered by Bayes optimized soft attention mechanism-enhanced transfer learning Comput. Ind. (IF 10.0) Pub Date : 2024-01-09 Jianjian Zhu, Zhongqing Su, Qingqing Wang, Runze Hao, Zifeng Lan, Frankie Siu-fai Chan, Jiaqiang Li, Sidney Wing-fai Wong
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Neural semantic tagging for natural language-based search in building information models: Implications for practice Comput. Ind. (IF 10.0) Pub Date : 2023-12-21 Mehrzad Shahinmoghadam, Samira Ebrahimi Kahou, Ali Motamedi
While the adoption of open Building Information Modeling (open BIM) standards continues to grow, the inherent complexity and multifaceted nature of the built asset lifecycle data present a critical bottleneck for effective information retrieval. To address this challenge, the research community has started to investigate advanced natural language-based search for building information models. However
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Semi-automated dataset creation for semantic and instance segmentation of industrial point clouds. Comput. Ind. (IF 10.0) Pub Date : 2023-12-21 August Asheim Birkeland, Marius Udnæs
The current practice for creating as-built geometric Digital Twins (gDTs) of industrial facilities is both labour-intensive and error-prone. In aged industries it typically involves manually crafting a CAD or BIM model from a point cloud collected using terrestrial laser scanners. Recent advances within deep learning (DL) offer the possibility to automate semantic and instance segmentation of point
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Implementation of a scalable platform for real-time monitoring of machine tools Comput. Ind. (IF 10.0) Pub Date : 2023-12-19 Endika Tapia, Unai Lopez-Novoa, Leonardo Sastoque-Pinilla, Luis Norberto López-de-Lacalle
In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results.
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A novel physically interpretable end-to-end network for stress monitoring in laser shock peening Comput. Ind. (IF 10.0) Pub Date : 2023-12-15 Rui Qin, Zhifen Zhang, Jing Huang, Zhengyao Du, Xianwen Xiang, Jie Wang, Guangrui Wen, Weifeng He
The data-driven method based on acoustic emission signals is gradually becoming a hot topic in the field of laser shock peening quality monitoring. Although some existing deep learning methods do provide excellent monitoring accuracy and speed, they lack physical interpretability in nature, and the opacity of these decisions poses a great challenge to their credibility. The weak interpretability of
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Incipient fault detection enhancement based on spatial-temporal multi-mode siamese feature contrast learning for industrial dynamic process Comput. Ind. (IF 10.0) Pub Date : 2023-12-12 Yan Liu, Zuhua Xu, Kai Wang, Jun Zhao, Chunyue Song, Zhijiang Shao
Incipient faults are characterized by low-amplitude, unclear fault features, which are susceptible to unknown disturbances, leading to unsatisfactory detection performance. In this paper, an incipient fault detection enhancement method based on siamese spatial-temporal multi-mode feature contrast learning method is proposed. Firstly, we design a novel siamese spatial-temporal multi-mode convolutional
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A comparative study of Augmented Reality rendering techniques for industrial assembly inspection Comput. Ind. (IF 10.0) Pub Date : 2023-12-05 Santina Fortuna, Loris Barbieri, Emanuele Marino, Fabio Bruno
In the manufacturing industry, Augmented Reality (AR) has shown significant potential in enhancing operators’ capabilities while performing inspection and assembly activities. However, the augmented visualization of virtual models on physical components can present challenges and potential misunderstandings, as the visualization mode greatly influences the perception of components and the amount of
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The prototype taxonomised: Towards the capture, curation, and integration of physical models in new product development Comput. Ind. (IF 10.0) Pub Date : 2023-12-08 David Jones, James Gopsill, Ric Real, Chris Snider, Harry Felton, Lee Kent, Mark Goudswaard, Owen Freeman Gebler, Ben Hicks
The management of data related to prototypes created during new product development is seen as a beneficial yet challenging activity. While attempts have been made to understand prototypes and their context in a range of use-cases, there is a gap in the understanding of the data that captures a prototype’s context and physical form. This paper highlights this gap, and addresses it through the development
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Training of physics-informed Bayesian neural networks with ABC-SS for prognostic of Li-ion batteries Comput. Ind. (IF 10.0) Pub Date : 2023-12-08 Juan Fernández, Matteo Corbetta, Chetan S. Kulkarni, Juan Chiachío, Manuel Chiachío
The current surge in the need for Li-ion batteries to power electric vehicles has also translated in a need for more advanced models that can predict their behavior, but also quantify the uncertainty in their predictions, given the amount of variables involved and the varying operating conditions. This manuscript proposes a new Bayesian physics-informed recurrent neural network, where the battery discharge
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Agile digital machine development Comput. Ind. (IF 10.0) Pub Date : 2023-12-08 Jesper Puggaard de Oliveira Hansen, Elias Ribeiro da Silva, Arne Bilberg
In mechatronic machine design and development, it is no longer enough to think about machine functionality and integration as machines are increasingly digitalized. Virtual upgrades are being made to manufacturing systems to keep up with the need for faster product cycles, higher quality, and the introduction of Industry 4.0 technologies. The design and development of new mechatronic discrete manufacturing
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Integrated process safety and process security risk assessment of industrial cyber-physical systems in chemical plants Comput. Ind. (IF 10.0) Pub Date : 2023-11-30 Shuaiqi Yuan, Ming Yang, Genserik Reniers
Aligned with the development needs of Industry 4.0, industrial cyber-physical systems (ICPSs) are widely applied to chemical facilities to facilitate so-called intelligent production processes. Meanwhile, emerging cyber-to-physical (C2P) risks are introduced due to the vulnerability of ICPSs to cyberattacks. An integrated safety and security risk assessment of chemical facilities equipped with industrial
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PLM data transformation: A mesoscopic scale perspective and an industrial case study Comput. Ind. (IF 10.0) Pub Date : 2023-11-28 François Loison, Benoit Eynard
Structured enterprise information systems such as Enterprise Resources Planning (ERP) and Product Lifecycle Management (PLM) have reached a maturity plateau and are storing up to hundreds of millions of objects and links. Such data is crucial for enterprise processes and operations. They are frequently the target of data transformation such as migration to a new data system, re-organisation according
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A human-centric system combining smartwatch and LiDAR data to assess the risk of musculoskeletal disorders and improve ergonomics of Industry 5.0 manufacturing workers Comput. Ind. (IF 10.0) Pub Date : 2023-11-24 Francesco Pistolesi, Michele Baldassini, Beatrice Lazzerini
More than one in four workers reportedly suffer from back pain worldwide, leading to 264 million work days lost yearly. In the U.S. alone, it causes $50 billion in healthcare costs every year, up to $100 billion if including decreased productivity and lost wages. The upcoming Industry 5.0 revolution will introduce human-centric manufacturing systems where workers’ well-being comes first while safeguarding
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Smart PSS modelling language for value offer prototyping: A design case study in the field of heating appliance offers Comput. Ind. (IF 10.0) Pub Date : 2023-11-20 Xavier Boucher, Camilo Murillo Coba, Damien Lamy
The recent convergence between two industrial transitions towards digitalization on the one side and servitization on the other side led to the new business strategies of digital servitization and smart PSS delivery. While inheriting from the previous scientific literature on PSS, because of the multiple impacts of digitalization in the overall system, the processes of ensuring the design and engineering
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Generation of material handling system alternatives: A constraints satisfaction problem approach Comput. Ind. (IF 10.0) Pub Date : 2023-11-20 Zakarya SOUFI, Pierre DAVID, Zakaria YAHOUNI
In the context of Industry 4.0, the design of efficient Material Handling Systems (MHS) plays a critical role in optimizing industrial operations and enhancing productivity. The integration of advanced technologies, automation, and complex systems has revolutionized industrial processes, emphasizing the need for defining coherent MHS alternatives prior to the deployment of a specific solution. This
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Designing an AI purchasing requisition bundling generator Comput. Ind. (IF 10.0) Pub Date : 2023-11-17 Jan Martin Spreitzenbarth, Christoph Bode, Heiner Stuckenschmidt
Following the design science methodology, a recommender system has been created with the research objective of finding a novel approach to the bundling problem in order to generate data-driven insights identifying cost potentials across an organization. In this study, a concept that has been implemented in business-to-business marketing at IBM is taken over to procurement in the automotive industry
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A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications Comput. Ind. (IF 10.0) Pub Date : 2023-11-17 Dieudonné Tchuente, Jerry Lonlac, Bernard Kamsu-Foguem
Artificial Intelligence (AI) is becoming fundamental in almost all activity sectors in our society. However, most of the modern AI techniques (e.g., Machine Learning – ML) have a black box nature, which hinder their adoption by practitioners in many application fields. This issue raises a recent emergence of a new research area in AI called Explainable artificial intelligence (XAI), aiming at providing
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Semantic knowledge-driven A-GASeq: A dynamic graph learning approach for assembly sequence optimization Comput. Ind. (IF 10.0) Pub Date : 2023-11-09 Luyao Xia, Jianfeng Lu, Yuqian Lu, Wentao Gao, Yuhang Fan, Yuhao Xu, Hao Zhang
In the context of an increasingly automated and personalized manufacturing mode, efficient assembly sequence planning (ASP) has emerged as a critical factor for enhancing production efficiency, ensuring product quality, and satisfying diverse market demands. To address this need, our study first transforms the assembly topology and process into a weighted precedence graph, wherein parts represent nodes
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Deep reinforcement learning for continuous wood drying production line control Comput. Ind. (IF 10.0) Pub Date : 2023-11-06 François-Alexandre Tremblay, Audrey Durand, Michael Morin, Philippe Marier, Jonathan Gaudreault
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Digitalization of maintenance activities in small and medium-sized enterprises: A conceptual framework Comput. Ind. (IF 10.0) Pub Date : 2023-11-01 Oliver Fuglsang Grooss
Asset management and digitalization are two timely research topics, especially for small and medium-sized enterprises (SMEs). SMEs continue to struggle with implementing digital technologies while retaining effective asset management processes within maintenance and after-sales service. Therefore, this paper develops a conceptual framework that integrates technological and organizational factors for
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Fundamental requirements of a machine learning operations platform for industrial metal additive manufacturing Comput. Ind. (IF 10.0) Pub Date : 2023-10-27 Mutahar Safdar, Padma Polash Paul, Guy Lamouche, Gentry Wood, Max Zimmermann, Florian Hannesen, Christophe Bescond, Priti Wanjara, Yaoyao Fiona Zhao
Metal-based Additive Manufacturing (AM) can realize fully dense metallic components and thus offers an opportunity to compete with conventional manufacturing based on the unique merits possible through layer-by-layer processing. Unsurprisingly, Machine Learning (ML) applications in AM technologies have been increasingly growing in the past several years. The trend is driven by the ability of data-driven
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Performance analysis of LogisticChain: A blockchain platform for maritime logistics Comput. Ind. (IF 10.0) Pub Date : 2023-10-27 Lifeng Ni, Elnaz Irannezhad
The application of blockchain and smart contracts has been widely acknowledged as essential in digitised logistics, offering improved traceability, transparency, and efficiency. However, concerns regarding performance and implementation limitations persist. To demonstrate the challenges regarding the performance and efficiency of blockchain in logistics use cases, this study presents a proof-of-concept
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Development and implementation of a roadmapping methodology to foster twin transition at manufacturing plant level Comput. Ind. (IF 10.0) Pub Date : 2023-10-16 Marco Spaltini, Sergio Terzi, Marco Taisch
Climate change and resource depletion are reshaping economies, compelling governments, society, and businesses to seek solutions that could meet both economic and environmental needs. Due to their relevance to environmental damage, manufacturers are pushed to achieve a sustainable transition in a relatively short time. In this scenario, Industry 4.0 reportedly act as a facilitator of the processes
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The minimal AR authoring approach: Validation in a real assembly scenario Comput. Ind. (IF 10.0) Pub Date : 2023-10-16 Enricoandrea Laviola, Antonio Emmanuele Uva, Michele Gattullo
This work aims to validate the “minimal AR” authoring approach in a real industrial assembly scenario. It focuses on optimizing visual assets in Augmented Reality (AR) work instructions. The design of AR assembly documentation is influenced by three main variables: work instructions, affordance (dependent on equipment components and operator capabilities), and AR signifiers (combination of visual assets
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Methodology for augmented reality-based adaptive assistance in industry Comput. Ind. (IF 10.0) Pub Date : 2023-10-12 Grégoire Mompeu, Florence Danglade, Frédéric Mérienne, Christophe Guillet
Industry 4.0 technologies are key elements for companies’ competitiveness. Among these technologies, Augmented Reality (AR) already shows great potential and results to assist workers through a large panel of industrial processes. Latest research suggests that AR systems should meet the user’s needs to deliver a personalized experience and therefore improve the adoption of the technology. Nevertheless
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Investigating the effects of spatial augmented reality on user participation in co-design sessions: A case study Comput. Ind. (IF 10.0) Pub Date : 2023-10-12 Maud Poulin, Cédric Masclet, Jean-François Boujut
New technologies such as Spatial Augmented Reality (SAR) have created new opportunities for including end users in the early phases of the design process when prototypes are not always available for functional testing. This study investigates the impact of introducing SAR technology on designers and end users working together in co-design sessions. To this end, a multi-modal analysis focusing on cognitive
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Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data Comput. Ind. (IF 10.0) Pub Date : 2023-10-08 Yoonseok Kim, Taeheon Lee, Youngjoo Hyun, Eric Coatanea, Siren Mika, Jeonghoon Mo, YoungJun Yoo
This study proposes a methodology for detecting anomalies in the manufacturing industry using a self-supervised representation learning approach based on deep generative models. The challenge arises from the limited availability of data on defective products compared with normal data, leading to degradation in the performance of deep learning models owing to data imbalances. To address this limitation
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Pattern-based action engine: Generating process management actions using temporal patterns of process-centric problems Comput. Ind. (IF 10.0) Pub Date : 2023-09-26 Gyunam Park, Daniel Schuster, Wil M.P. van der Aalst
As business environments become more competitive, organizations strive to improve their business processes to reduce costs and increase quality and productivity. As process improvement traditionally embraces manual creative tasks that are time-consuming and labor-intensive, the need for automating it arises. Action-Oriented Process Mining (AOPM) aims to support automated process improvement by leveraging
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Cognitive digital twins for freight parking management in last mile delivery under smart cities paradigm Comput. Ind. (IF 10.0) Pub Date : 2023-09-30 Yu Liu, Shenle Pan, Pauline Folz, Fano Ramparany, Sébastien Bolle, Eric Ballot, Thierry Coupaye
This paper examines the Freight Parking Management Problem (FPMP) of last-mile delivery within the context of Smart Cities where objects are managed by Digital Twins. Specifically, we investigate how Cognitive Digital Twins - Digital Twins with augmented semantic capabilities - can enhance instantly updated knowledge of parking connectivity to optimize logistics operations planning and urban resource
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BIM-supported drone path planning for building exterior surface inspection Comput. Ind. (IF 10.0) Pub Date : 2023-09-21 Xiongwei Huang, Yongping Liu, Lizhen Huang, Sverre Stikbakke, Erling Onstein
Digitalization in the architectural, engineering, and construction (AEC) industry highlights the interdisciplinary collaboration between complex systems to provide fast and efficient services. This paper incorporates Building Information Modeling (BIM) and drone, and generates feasible paths for exterior building surface inspections. A systematic approach was proposed, focusing on the overall automatic
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Multi-scale neighborhood query graph convolutional network for multi-defect location in CFRP laminates Comput. Ind. (IF 10.0) Pub Date : 2023-09-14 Bo Yang, Wenlong Xu, Fengyang Bi, Yang Zhang, Ling Kang, Lili Yi
This paper presents a novel deep learning architecture named multi-scale neighborhood query graph convolutional network (MNQGN). In MNQGN, the spatial relationship between sensors is represented by constructing a vibration sensor distribution map. Furthermore, MNQGN enhances the feature representation by integrating a neighborhood query interaction mechanism to learn features from different scales
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Toward a digital materials mechanical testing lab Comput. Ind. (IF 10.0) Pub Date : 2023-09-09 Hossein Beygi Nasrabadi, Thomas Hanke, Matthias Weber, Miriam Eisenbart, Felix Bauer, Roy Meissner, Gordian Dziwis, Ladji Tikana, Yue Chen, Birgit Skrotzki
To accelerate the growth of Industry 4.0 technologies, the digitalization of mechanical testing laboratories as one of the main data-driven units of materials processing industries is introduced in this paper. The digital lab infrastructure consists of highly detailed and standard-compliant materials testing knowledge graphs for a wide range of mechanical testing processes, as well as some tools that
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A Metamodel for Designing Assessment Models to support transition of production systems towards Industry 5.0 Comput. Ind. (IF 10.0) Pub Date : 2023-08-26 Mariateresa Caggiano, Concetta Semeraro, Michele Dassisti
Industry 4.0 paradigm has focused the attention on information and communication technologies, requiring greater connectivity between physical devices. A good effect of this is the significant productivity enhancement in all its aspects such as reduction of raw material consumption and of production times. But production systems are made of an interaction between human and machines, as the recent literature
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OpenTwins: An open-source framework for the development of next-gen compositional digital twins Comput. Ind. (IF 10.0) Pub Date : 2023-08-22 Julia Robles, Cristian Martín, Manuel Díaz
Although digital twins have recently emerged as a clear alternative for reliable asset representations, most of the solutions and tools available for the development of digital twins are tailored to specific environments. Furthermore, achieving complex digital twins often requires the orchestration of technologies and paradigms such as machine learning, the Internet of Things, and 3D visualization
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Uncertainty-aware and dynamically-mixed pseudo-labels for semi-supervised defect segmentation Comput. Ind. (IF 10.0) Pub Date : 2023-08-12 Dejene M. Sime, Guotai Wang, Zhi Zeng, Bei Peng
Deep learning-based defect segmentation is one of the important tasks of machine vision in automated inspection. Supervised learning methods have recently achieved remarkable performance on this task. However, the effectiveness of the supervised methods is limited by the scarcity and high cost of pixel-level annotation of training data. Semi-supervised learning methods have been proposed for training
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Emergence of collective intelligence in industrial cyber-physical-social systems for collaborative task allocation and defect detection Comput. Ind. (IF 10.0) Pub Date : 2023-08-12 Inno Lorren Désir Makanda, Pingyu Jiang, Maolin Yang, Haoliang Shi
The manufacturing industry is facing the challenge of meeting the growing demand for personalized products, which requires enhanced agility, flexibility, reconfigurability, and sustainability on the shop floor. To tackle these requirements, one possible solution is to foster collective intelligence (CI) by sharing data and knowledge among human operators, machines, and workpieces, thereby improving
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Development of taxonomy for classifying defect patterns on wafer bin map using Bin2Vec and clustering methods Comput. Ind. (IF 10.0) Pub Date : 2023-08-07 Dong-Hee Lee, Eun-Su Kim, Seung-Hyun Choi, Young-Mok Bae, Jong-Bum Park, Young-Chan Oh, Kwang-Jae Kim
A wafer consists of several chips, and serial electrical tests are conducted for each chip to investigate whether the chip is defective. A bin indicates the test results for each chip with information on which tests the chip failed. A wafer bin map (WBM) shows the locations and bins of the defects on the wafer. WBMs showing spatial patterns of defects usually result from assignable causes in the wafer
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Automatic definition of engineer archetypes: A text mining approach Comput. Ind. (IF 10.0) Pub Date : 2023-08-08 Francesco Lupi, Mohammed M. Mabkhot, Eleonora Boffa, Pedro Ferreira, Dario Antonelli, Antonio Maffei, Niels Lohse, Michele Lanzetta
With the rapid and continuous advancements in technology, as well as the constantly evolving competences required in the field of engineering, there is a critical need for the harmonization and unification of engineering professional figures or archetypes. The current limitations in tymely defining and updating engineers' archetypes are attributed to the absence of a structured and automated approach
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A two-stage unsupervised approach for surface anomaly detection in wire and arc additive manufacturing Comput. Ind. (IF 10.0) Pub Date : 2023-07-26 Hao Song, Chenxi Li, Youheng Fu, Runsheng Li, Haiou Zhang, Guilan Wang
Wire and arc additive manufacturing (WAAM) has gradually been applied in industrial applications in recent years due to its low cost, high deposition rate, and high material utilization rate. Anomalies in the WAAM process, such as inclusion, porosity, and lack of fusion, can have unpredictable effects on the quality of the final product. While some studies have investigated anomaly detection methods
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Question answering models for human–machine interaction in the manufacturing industry Comput. Ind. (IF 10.0) Pub Date : 2023-07-25 Eneko Ruiz, María Inés Torres, Arantza del Pozo
This paper presents a question answering (QA) system that will enable workers from the manufacturing industry to ’hands-free’ request information. This kind of systems, that are broadly used in household context, have started to gain popularity in industrial environments. To develop the system, PDF-based industrial manuals in Spanish have been processed, annotated by experts and used to train the different
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Neuro-symbolic model for cantilever beams damage detection Comput. Ind. (IF 10.0) Pub Date : 2023-07-22 Darian M. Onchis, Gilbert-Rainer Gillich, Eduard Hogea, Cristian Tufisi
In the last decade, damage detection approaches swiftly changed from advanced signal processing methods to machine learning and especially deep learning models, to accurately and non-intrusively estimate the state of the beam structures. But as the deep learning models reached their peak performances, also their limitations in applicability and vulnerabilities were observed. One of the most important
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Reinforcement learning for disassembly sequence planning optimization Comput. Ind. (IF 10.0) Pub Date : 2023-07-22 Amal Allagui, Imen Belhadj, Régis Plateaux, Moncef Hammadi, Olivia Penas, Nizar Aifaoui
The disassembly process is one of the most expensive phases in the product life cycle for both maintenance and the End of Life dismantling process. Industry must optimize the disassembly sequence to ensure time-cost-efficiency. This paper presents a new approach based on the Reinforcement Learning algorithm to optimize Disassembly Sequence Planning. This research work focuses on two types of dismantling:
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Non data hungry smart composite manufacturing using active transfer learning with sigma point sampling (SPSATL) Comput. Ind. (IF 10.0) Pub Date : 2023-07-20
As an emerging smart manufacturing paradigm, Industry 4.0 employs advancements in sensory technologies and cyber physical systems (CPS), to automate and optimize manufacturing processes. However, using conventional machine learning (ML) in this paradigm can be challenging especially in dynamic systems where materials used in the production plan can change constantly in order to adapt to the market
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Multi-view expressive graph neural networks for 3D CAD model classification Comput. Ind. (IF 10.0) Pub Date : 2023-07-20
The creation of effective content-based retrieval systems for the 3D models of engineering components created by Mechanical Computer Aided Design (MCAD) systems has been a subject of academic investigation since the 1990 s. Recently some of the most promising results have been reported by researchers using Deep Neural Nets (DNN) to classify industrial parts represented by collections of images generated
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Leveraging the power of formal methods in the realm of enterprise modeling—On the example of extending the (meta) model verification possibilities of ADOxx with Alloy Comput. Ind. (IF 10.0) Pub Date : 2023-07-18
Verification in the realm of enterprise modeling (EM) ensures both the consistency of EM language specifications (i.e., meta models and additional well-formedness constraints), as well as of enterprise models. The consistency of enterprise models, which integrate different perspectives on an enterprise, ensures that they contain the necessary, in line with domain-specific rules, information for carrying
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CyPhERS: A cyber-physical event reasoning system providing real-time situational awareness for attack and fault response Comput. Ind. (IF 10.0) Pub Date : 2023-07-14
Cyber–physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous
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Industrial anomaly detection with domain shift: A real-world dataset and masked multi-scale reconstruction Comput. Ind. (IF 10.0) Pub Date : 2023-07-15
Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on diversity of data categories, overlooking the diversity of domains within the same data category. In this paper, to bridge this gap, we propose the Aero-engine Blade Anomaly Detection (AeBAD)
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Fault detection and diagnostics in the context of sparse multimodal data and expert knowledge assistance: Application to hydrogenerators Comput. Ind. (IF 10.0) Pub Date : 2023-07-12
Deep learning-based methods for industrial fault detection and diagnostics (FDD) depend strictly on good quality and sufficient quantity of condition monitoring data. However, in real-world industrial settings, data collection is usually limited, leading to sparse and insufficient data to train a data-driven model. Therefore, this work proposes a new methodology to address this issue by leveraging
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An improved stacking ensemble learning model for predicting the effect of lattice structure defects on yield stress Comput. Ind. (IF 10.0) Pub Date : 2023-07-08 Zhiwei Zhang, Yuyan Zhang, Yintang Wen, Yaxue Ren, Xi Liang, Jiaxing Cheng, Mengqi Kang
To address the challenge of predicting mechanical properties due to the unavoidable and multi-characteristic nature of defects in additive manufacturing lattice structures, an improved ensemble learning prediction model is proposed. The objective is to predict the true value of the yield stress of the lattice structure by using the data obtained from finite element simulation. The prediction model