Journal of Industrial Information Integration最新文献

英文中文

Managing lifecycle of product information with an ontology-based knowledge framework

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-03-11 DOI: 10.1016/j.jii.2025.100820

Lorenzo Failla , Marco Rossoni , Marco Quirini , Giorgio Colombo

The effective management of product information within a formalized, digital and interoperable infrastructure remains a significant gap in realizing the full potential of modern Product Lifecycle Management (PLM) implementations in industrial contexts. While the academic paradigm of PLM has been extensively emphasized in the scientific literature for over two decades as a sustainable company strategy, contemporary PLM implementations prove inadequate in handling the extensive volume and variety of information generated throughout a product’s lifecycle. Starting from a comprehensive overview of the evolution of the PLM paradigm and of its inherent implications, the analysis of the PLM implementation of a big player in engineering and manufacturing of turbomachinery products for Oil & Gas and Energy markets is analyzed, allowing to identify existing major general contradictions from an industrial perspective. While it is reaffirmed that the attainment of a neutral, harmonized and universally agreed standardization is nowadays missing and is crucial in the enabling of the PLM paradigm through digital technologies, the present study attempts to demonstrate how a general and agnostic ontology-based framework may straightforwardly fulfill all the identified demands of the PLM paradigm and, therefore, how ontologies play a central role in this field of research by bridging different domains to enable a holistic product conceptualization, lifecycle management, and data interoperability among different digital agents.

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引用次数: 0

Data center multidimensional management strategy based on descending neighborhood DBSCAN algorithm in unsupervised learning

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-03-10 DOI: 10.1016/j.jii.2025.100830

Bin Liang, Junqing Bai

Cloud users rent virtual machines (VMs) with varying parameters tailored to their unique business requirements. These diverse VM parameters add complexity to data center (DC) management strategies. Among the crucial parameters are CPU and memory, which must be optimized to ensure efficient physical resource utilization and decreased DC energy consumption. This article proposes three algorithms to manage and optimize VMs. Firstly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is enhanced, leading to the introduction of the descending neighborhood DBSCAN (DNDBSCAN) algorithm. This algorithm facilitates the clustering of physical machines (PMs). Secondly, the cluster center nearest classification algorithm (CCN) is proposed, leveraging VM attributes and the remaining capacity of the cluster center to classify the VMs for deployment. Additionally, the avoid hot spot time correlation algorithm (AHTC) is introduced to handle VM mapping, deploying VMs on the most time-relevant PMs while mitigating hot spots. Lastly, these three algorithms are integrated into a DC multidimensional management strategy based on the DNDBSCAN algorithm within the framework of unsupervised learning (DND). When compared to other algorithms, the DND algorithm demonstrates significant improvement in PM balanced utilization and reduction of DC energy consumption. The average balanced utilization of PM of the DND algorithm is 86 %, which is an average improvement of 11 % compared to the comparative algorithm. The average total energy consumption of the DND algorithm is 124 kW•h, which is an average reduction of 41 % compared to the comparative algorithm.

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引用次数: 0

An integrated weighted multi-criteria decision making method using Z-number and its application in failure modes and effect analysis

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-03-08 DOI: 10.1016/j.jii.2025.100805

Muhammad Akram , Inayat Ullah , Tofigh Allahviranloo , Mohammadreza Shahriari

In this study, a new technique of

Z

-number preference ranking by similarity to the ideal solution is proposed for estimating risk in failure mode and effects analysis. The method ranks all identified faults using subjective and objective weights of risk factors. The subjective weights are calculated by the

Z

-number analytical hierarchy process, and the objective weights are calculated using the

Z

-number Shannon entropy measure. The combination of the subjective and objective weights of the risk factors is used to prioritize the failure modes. Firstly, the expert team is asked to rate the failure modes concerning the risk factors in linguistic terms consisting of two parts, possibility and reliability. The experts’ evaluation is then converted into

Z

-numbers, treating the two components as triangular fuzzy numbers. This method also incorporates the experts’ weights to aggregate the individual ratings by the average method. The method ranks the failure modes by calculating the distance of each failure mode from the

Z

-number positive ideal solution and

Z

-number negative ideal solution. The proposed methodology is illustrated through a flowchart. The strategy is further explained by applying it to a case study of the operation of a crane in a steel mill. Furthermore, the validity and effectiveness of the proposed technique are verified by presenting a comparative analysis of outcomes with the existing techniques. Finally, the sensitivity of the proposed study is also tested and explained by using various diagrams. In the proposed strategy, conversion of

Z

-numbers into fuzzy numbers is avoided, which means there is little loss of information as compared to the existing techniques, which, in turn, lead to optimal decisions.

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引用次数: 0

Systematic review of mobile robots applications in smart cities with future directions

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-03-08 DOI: 10.1016/j.jii.2025.100821

Ravinesh Chand , Bibhya Sharma , Sandeep Ameet Kumar

Smart cities can create a connected and efficient urban environment by integrating advanced technologies with mobile robots. Mobile robots play a significant role in various smart city applications, ranging from transportation and logistics to surveillance and maintenance tasks, and have the potential to revolutionize the way people live and work in smart cities. Ensuring the efficient, greener and safe operation of these robots is crucial for the success and sustainability of smart city operations. However, several challenges must be addressed, such as safe autonomous navigation, motion control and security. This systematic literature review explores the current state of knowledge and emerging trends in autonomous motion control of personal and assistive mobile robots from 2015 to 2023, as well as the challenges and opportunities for possible application in smart city environments. In particular, it investigates the navigational approaches for future application of mobile robotic systems in the essential smart city components of personal transportation, assistive technologies and road and highway robots. Additionally, it contributes to the ongoing research about integrating mobile robotics into smart city applications and highlights future research directions. Researchers can incorporate insights from this review into their development plans for industrial integration by designing infrastructure that accommodates and leverages mobile robots for numerous smart city operations, including transportation, waste management and surveillance.

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引用次数: 0

A hybrid constraint programming and cross-entropy approach for balancing U-Shaped disassembly line with flexible workstations and spatial constraints

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-03-07 DOI: 10.1016/j.jii.2025.100817

Yu Zhang , Zeqiang Zhang , Feng Chu , Saïd Mammar

Disassembly lines are an effective means for the large-scale, industrialized recycling of end-of-life products. Among these, U-shaped disassembly lines are particularly noted for their combination of flexibility and production efficiency. This study addresses the U-shaped disassembly line balancing problem, considering the coexistence of separate stations and spatial limitations within workstations. A mixed-integer nonlinear programming model and a constraint programming model are developed to accurately capture this complex problem. Additionally, a novel hybrid constraint programming with a goal-driven cross-entropy optimization algorithm (CP–GDCE) is introduced. This algorithm combines a multi-objective cross-entropy grouping framework, a constraint programming-based heuristic initialization, a multi-point crossover recombination mechanism, and large neighborhood search techniques, significantly enhancing solution efficiency and accuracy. Extensive benchmarking and experimental validation indicate that the CP–GDCE not only excels in addressing the specific problem of this study but also demonstrates superiority in classic disassembly line balancing issues. In 21 test cases, the CP–GDCE achieved superior hypervolume and inverted generational distance values compared to 11 benchmark algorithms. A practical application using a printer disassembly example shows that the proposed U-shaped configuration is highly flexible and efficient, compatible with both traditional U-shaped and straight disassembly lines. This configuration significantly reduces the total length of the disassembly line, improving space utilization and highlighting its practical potential and advantages.

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引用次数: 0

Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-03-06 DOI: 10.1016/j.jii.2025.100807

Lukas Bahr , Christoph Wehner , Judith Wewerka , José Bittencourt , Ute Schmid , Rüdiger Daub

Failure mode and effects analysis (FMEA) is an essential tool for mitigating potential failures, particularly during the ramp-up phases of new products. However, its effectiveness is often limited by the reasoning capabilities of the FMEA tools, which are usually tabular structured. Meanwhile, large language models (LLMs) offer novel prospects for advanced natural language processing tasks. However, LLMs face challenges in tasks that require factual knowledge, a gap that retrieval-augmented generation (RAG) approaches aim to fill. RAG retrieves information from a non-parametric data store and uses a language model to generate responses. Building on this concept, we propose to enhance the non-parametric data store with a knowledge graph (KG). By integrating a KG into the RAG framework, we aim to leverage analytical and semantic question-answering capabilities for FMEA data. This paper contributes by presenting set-theoretic standardization and a schema for FMEA data, an algorithm for creating vector embeddings from the FMEA-KG, and a KG-enhanced RAG framework. Our approach is validated through a user experience design study, and we measure the precision and performance of the context retrieval recall.

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引用次数: 0

Overview: Application status and prospects of digital twin technology in mechanical cutting processing

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-03-06 DOI: 10.1016/j.jii.2025.100822

Li Xin , Gao Hanjun , Chen Xiaoman , Xue Nianpu , Wu Qiong

With the advancement of digitalization and intelligence, the demand for improving processing quality and efficiency is becoming increasingly urgent. Digital twin technology, a key supporting technology for intelligent manufacturing, can accurately simulate and predict the machining process in virtual space. This is achieved through data fusion analysis and iterative optimization, effectively ensuring the shape and quality of key components. The article provides a detailed review of the development history of digital twin technology, introduces the progress of its theoretical system construction and technical standard formulation, and explores its broad application prospects in the field of intelligent manufacturing. Through the analysis of relevant research and engineering cases, this article summarizes the current research status of relevant technologies, analyzes the future development directions, provides an application paradigm of digital twin in machining cutting processing, and reveals the important role and enormous potential of digital twin technology in promoting the transformation and upgrading of the manufacturing industry.

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引用次数: 0

Compendium law in iterative information management: A comprehensive model perspective

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-02-24 DOI: 10.1016/j.jii.2025.100808

Qiang Li , Zhi Li

The existing limitations of the fundamental laws necessary for constructing a comprehensive and widely accepted theoretical framework have significantly hindered the progress of Information Management. This lack has resulted in a predominant reliance on indirect strategies to address information management challenges, often leading to complex, inefficient, and somewhat stochastic analyses and evaluations. For instance, the failure rate of digital transformation in global enterprises is as high as 80 %, and that of data-driven organizational change reaches 85 %, highlighting the urgency and difficulty of resolving these challenges. Through an in-depth analysis of the spiral model and derivation of the Shannon-Weaver model, we unearthed the objective and universal Compendium Law of iterative information management. Building on this law, we propose the application of information system modeling and Hamiltonian graph theory to develop a comprehensive analytical model for iterative information management. This model provides a theoretical approach for the scientific analysis and optimal design of iterative information management, enabling efficient comparative analysis and knowledge transfer among various iterative information management systems. This study contributes to the foundational understanding of Information Management as an independent discipline capable of addressing cross-disciplinary challenges related to information resources, including those found in artificial intelligence, blockchains, quantum communication, the Internet of Things, and digitization.

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引用次数: 0

Challenges in feature importance interpretation: Analyzing LSTM-NN predictions in battery material flotation

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-02-24 DOI: 10.1016/j.jii.2025.100809

Yoshiyasu Takefuji

Gomez-Flores et al. proposed a Long Short-Term Memory Neural Network (LSTM-NN) for predicting the flotation behavior of battery active materials using various physicochemical and hydrodynamic variables. While they achieved high prediction accuracy, validated through Mean Relative Error (MRE) and Mean Squared Error (MSE) metrics, concerns arise regarding the integrity of feature importance assessments derived from SAGE and SHAP methodologies. Specifically, the reliance on these model-specific techniques can introduce biases, obscuring the true relationships between features. Additionally, while Spearman's correlation elucidated significant relationships among variables, the absence of discussion on p-values left gaps in interpretation. This study emphasizes the need for cautious interpretation of feature importance metrics and the elimination of less significant variables, aiming to enhance model robustness and improve actionable insights in machine learning contexts.

引用次数: 0

Geometric deep learning as an enabler for data consistency and interoperability in manufacturing

IF 10.4 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Industrial Information Integration

Pub Date : 2025-02-22 DOI: 10.1016/j.jii.2025.100806

Patrick Bründl , Benedikt Scheffler , Christopher Straub , Micha Stoidner , Huong Giang Nguyen , Jörg Franke

Skilled labor shortages and the growing trend for customized products are increasing the complexity of manufacturing systems. Automation is often proposed to address these challenges, but industries operating under the engineer-to-order, lot-size-one production model often face significant limitations due to the lack of relevant data. This study investigates an approach for the extraction of assembly-relevant information, using only vendor-independent STEP files, and the integration and validation of these information in an exemplary industrial use case. The study shows that different postprocessing approaches of the same segmentation mask can result in significant differences regarding the data quality. This approach improves data quality and facilitates data transferability to components not listed in leading ECAD databases, suggesting broader potential for generalization across different components and use cases. In addition, an end-to-end inference pipeline without proprietary formats ensures high data integrity while approximating the surface of the underlying topology, making it suitable for small and medium-sized companies with limited computing resources. Furthermore, the pipeline presented in this study achieves improved accuracies through enhanced post-segmentation calculation approaches that successfully overcome the typical domain gap between data detected solely on virtual models and their physical application. The study not only achieves the accuracy required for full automation, but also introduces the Spherical Boundary Score (SBS), a metric for evaluating the quality of assembly-relevant information and its application in real-world scenarios.

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