Journal of Industrial Information Integration最新文献_第5页

Empowering robotic training with kinesthetic learning and digital twins in human–centric industrial systems 在以人为本的工业系统中，利用动觉学习和数字孪生赋予机器人培训能力

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

Journal of Industrial Information Integration

Pub Date : 2025-01-01 DOI: 10.1016/j.jii.2024.100743

Thien Tran , Quang Nguyen , Toan Luu , Minh Tran , Jonathan Kua , Thuong Hoang , Man Dien

This paper presents a human-centric mixed reality (MR) collaborative training platform that employs a kinesthetic learning technique in industrial robotic training, specifically focusing on robot pick–and–place (RPP) operations. Collaborating with ABB Robotics Vietnam, we conducted a user study to investigate the user experiences and practical perceptions of university students and novice trainees via the human–centric training assessment. The study compares the traditional training (TT) RPP classroom as a conventional method with a new collaborative MR RPP training approach (N = 50). The MR training features a digital twin (DT) of ABB GoFa™ CRB–15000 collaborative robot in an immersive 360° Digital–Objects–Based Augmented Training Environment (360–ATE) using Microsoft HoloLens devices. The research evaluated the impact of MR and DT on human–robot interaction and collaboration, user experience, task performance, knowledge retention, and interpretation, as well as differences in perceptions between the two novice cohorts under each training condition. The primary research question explores “Whether the MR collaborative training platform with DT integration in 360–ATE can serve as an alternative approach for novice students and industrial trainees in RPP operations?”. The findings indicate that MR training is more engaging and effective in enhancing participant safety, confidence, and task performance, which also augments cognitive capabilities. The virtual contents on HoloLens, especially the DT, captured the attention and stimulated active learning abilities. Overall, participants in the MR cohort find the proposed training platform useful and easy to use. The platform has a positive influence on their intention to use similar 360–ATE–assisted training platforms in the future.

本文提出了一个以人为中心的混合现实（MR）协同训练平台，该平台在工业机器人训练中采用了动觉学习技术，特别关注机器人拾取和放置（RPP）操作。与ABB越南机器人公司合作，我们进行了一项用户研究，通过以人为中心的培训评估来调查大学生和新手学员的用户体验和实践感受。本研究比较了传统训练（TT） RPP课堂作为常规方法与一种新的协作式MR RPP训练方法（N = 50）。磁共振训练采用ABB GoFa™CRB-15000协作机器人的数字孪生体（DT），在使用微软HoloLens设备的沉浸式360°基于数字对象的增强训练环境（360 - ate）中进行。研究评估了MR和DT对人机交互和协作、用户体验、任务表现、知识保留和解释的影响，以及两组新手在每种训练条件下的感知差异。主要的研究问题是探讨“360-ATE集成DT的MR协同培训平台是否可以作为新手和工业培训生在RPP操作中的替代方法？”研究结果表明，磁共振训练在增强参与者的安全、信心和任务表现方面更有吸引力，也更有效，这也增强了认知能力。HoloLens上的虚拟内容，尤其是DT，吸引了注意力，激发了主动学习能力。总体而言，MR队列的参与者发现所提出的培训平台有用且易于使用。该平台对他们未来使用类似360 - ate辅助培训平台的意愿有积极影响。

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引用次数: 0

Multimodal-information-based optimized agricultural prescription recommendation system of crop electronic medical records 基于多模式信息的作物电子病历优化农业处方推荐系统

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

Journal of Industrial Information Integration

Pub Date : 2025-01-01 DOI: 10.1016/j.jii.2024.100748

Chang Xu , Junqi Ding , Bo Wang , Yan Qiao , Lingxian Zhang , Yiding Zhang

Multimodal Crop Electronic Medical Records (CEMRs) contain complex information, including disease symptoms, crop conditions, environmental factors, and diagnostic prescriptions, making them crucial for intelligent prescription recommendations. However, effectively integrating complementary features from different CEMRs modalities has remained a key challenge. Current CEMRs research primarily focuses on unimodal data, and simplistic approaches like feature concatenation struggle to achieve in-depth cross-modal interactions. This study introduces a novel agricultural prescription recommendation model (named AgriPR) based on cross-modal multi-layer feature fusion. The model initially employs task-adaptive pre-trained BERT (TA-BERT) and ConvNeXt to encode text and image unimodal features respectively. Subsequently, it utilizes Bilinear Attention Networks (BAN) to bilinear features and combines them with bimodal encoding features for a multilayer fusion representation. Finally, a dual-layer Transformer performs re-interaction to emphasize key fused features, resulting in precise prescription recommendations. To evaluate AgriPR, we constructed a real CEMRs dataset containing 13 prescription categories from Beijing Plant Clinic. Experimental results demonstrate that AgriPR achieves outstanding performance, with a classification accuracy of 98.88 %, surpassing state-of-the-art models. Furthermore, the study compares and analyzes 8 encoder combinations, 6 feature fusion strategies, and 6 network layer configurations, highlighting the model's design advantages. Lastly, the model's adaptability was also tested with incomplete modality inputs (text-only or image-only) and missing information inputs (e.g., crop, environment, symptoms). The findings confirm AgriPR's practical applicability, providing a high-performance solution for agricultural management systems.

多模态作物电子病历（CEMR）包含复杂的信息，包括疾病症状、作物状况、环境因素和诊断处方，因此对智能处方建议至关重要。然而，如何有效整合来自不同 CEMR 模式的互补特征仍是一项关键挑战。目前的 CEMRs 研究主要集中在单模态数据上，而特征串联等简单方法难以实现深入的跨模态交互。本研究介绍了一种基于跨模态多层特征融合的新型农业处方推荐模型（名为 AgriPR）。该模型最初采用任务自适应预训练 BERT（TA-BERT）和 ConvNeXt 分别对文本和图像单模态特征进行编码。随后，该模型利用双线性注意网络（BAN）对特征进行双线性处理，并将其与双模编码特征相结合，形成多层融合表示法。最后，双层变换器执行再交互，以强调关键的融合特征，从而提供精确的处方建议。为了评估 AgriPR，我们构建了一个真实的 CEMRs 数据集，其中包含来自北京植物园诊所的 13 个处方类别。实验结果表明，AgriPR 性能卓越，分类准确率高达 98.88%，超过了最先进的模型。此外，研究还对 8 种编码器组合、6 种特征融合策略和 6 种网络层配置进行了比较和分析，凸显了模型的设计优势。最后，该模型的适应性还在不完整模态输入（纯文本或纯图像）和缺失信息输入（如作物、环境、症状）的情况下进行了测试。研究结果证实了 AgriPR 的实用性，为农业管理系统提供了一个高性能的解决方案。

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引用次数: 0

Towards cognitive intelligence-enabled product design: The evolution, state-of-the-art, and future of AI-enabled product design 面向认知智能产品设计：人工智能产品设计的演变、最新技术和未来

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

Journal of Industrial Information Integration

Pub Date : 2025-01-01 DOI: 10.1016/j.jii.2024.100759

Zuoxu Wang , Xinxin Liang , Mingrui Li , Shufei Li , Jihong Liu , Lianyu Zheng

Engineering design researchers have increasing interests in leveraging artificial intelligence (AI) techniques to a wide range of product design tasks, such as customer requirement analysis, product concept generation, design synthesis, and decision-making in product design. Indeed, AI techniques perform excellently on well-defined design tasks with clear problem definition, specialized solutions, and abundant training data. However, facing the ever-evolving AI techniques rapidly and radically changing the product design manner, there is still a lack of a systematic summary about the current stage of AI-enabled product design. Besides, although the current AI-enabled product design performs excellently on the well-defined tasks, the other advanced design tasks that need cognitive capability can still hardly be satisfyingly completed by the current product design system. This study systematically reviewed the literature on AI-enabled product design to understand its evolution and state-of-the-arts. To bridge the semantic gap between humans and systems, a novel cognitive intelligence-enabled product design (CIPD) framework is proposed, in which cognitive intelligence is the key enabler. The CIPD's key aspects, including its system architecture, human-like capabilities, enabling technologies, and potential applications, are also systematically discussed. It is hoped that this study could contribute to the future directions of the product design field and offer insightful guidance to the practitioners and researchers in their product design process.

工程设计研究人员对利用人工智能（AI）技术进行广泛的产品设计任务越来越感兴趣，例如客户需求分析、产品概念生成、设计综合和产品设计决策。事实上，人工智能技术在定义明确的设计任务上表现出色，具有清晰的问题定义、专门的解决方案和丰富的训练数据。然而，面对不断发展的人工智能技术迅速而彻底地改变着产品设计方式，目前仍缺乏对人工智能产品设计现阶段的系统总结。此外，虽然目前的人工智能产品设计在明确的任务上表现出色，但其他需要认知能力的高级设计任务，目前的产品设计系统仍然很难令人满意地完成。本研究系统地回顾了有关人工智能产品设计的文献，以了解其发展和现状。为了弥合人与系统之间的语义鸿沟，提出了一种新的认知智能产品设计（CIPD）框架，其中认知智能是关键的使能器。CIPD的关键方面，包括其系统架构、类人能力、使能技术和潜在应用，也进行了系统的讨论。希望本研究能对未来产品设计领域的发展方向有所贡献，并对从业者和研究者在产品设计过程中提供有见地的指导。

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引用次数: 0

Energy-resilient closed-loop supply chain design managed by the 3PL provider: A pick-up strategy and data envelopment analysis 由第三方物流供应商管理的能源弹性闭环供应链设计：拾取策略和数据包络分析

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

Journal of Industrial Information Integration

Pub Date : 2024-12-31 DOI: 10.1016/j.jii.2024.100763

Beheshteh Moghadaspoor , Reza Tavakkoli-Moghaddam , Ali Bozorgi-Amiri , Tofigh Allahviranloo

Population growth and the development of transportation networks have caused the world to face a larger volume of scrap tires, which can cause critical environmental challenges if they are not properly disposed of after being ultimately used. Thus, implementing appropriate recovery practices has developed. The existing challenges in the forward and reverse integration flow motivate leaders to submit a third-party logistics service provider (3PL) as an appropriate option for outsourcing activities. As a result, an inventive closed-loop supply chain (CLSC) network is necessary. A multiple objective, product, and period mathematical model is proposed to develop the CLSC under 3PL management in the tire industry. The data envelopment analysis (DEA) method is applied to choose a better set of manufacturers to coordinate with 3PL. The motivating pricing approach is also considered for appropriate recovery practices, and resiliency was investigated against disruption at crucial levels. This model aims to minimize the costs of diverse processes over scrap products and energy consumption and reach a sufficient level of responsiveness to customers. For solving the multi-objective model, the augmented ε-constraint (AUGMECON2) method leads to Pareto-optimal solutions. The results show that 3PLs improve the supply chain (SC) procedure and increase the responsiveness to customer demand. Also, by planning to increase product recycling, it is possible to save money when purchasing raw materials from suppliers.

人口增长和交通网络的发展使世界面临着大量的废轮胎，如果它们在最终使用后没有得到妥善处理，可能会造成严重的环境挑战。因此，实施适当的恢复实践得到了发展。正向和反向集成流程中存在的挑战促使领导者提交第三方物流服务提供商（3PL）作为外包活动的适当选择。因此，一个创新的闭环供应链（CLSC）网络是必要的。提出了一个多目标、多产品、多周期的数学模型，用于轮胎行业在第三方物流管理下发展CLSC。运用数据包络分析（DEA）方法，选择一组较好的厂商与第三方物流合作。在适当的恢复实践中也考虑了激励定价方法，并在关键水平上调查了针对中断的弹性。该模型旨在最大限度地减少各种过程的成本，而不是废料和能源消耗，并达到对客户的充分响应水平。对于多目标模型，增广ε约束（AUGMECON2）方法可得到pareto最优解。研究结果表明，第三方物流改善了供应链流程，提高了对客户需求的响应能力。此外，通过计划增加产品回收，从供应商购买原材料时可以节省资金。

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引用次数: 0

A novel Pythagorean fuzzy correlation coefficient based on Spearman’s technique of correlation coefficient with applications in supplier selection process 基于Spearman相关系数技术的一种新的毕达哥拉斯模糊相关系数在供应商选择中的应用

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

Journal of Industrial Information Integration

Pub Date : 2024-12-30 DOI: 10.1016/j.jii.2024.100762

Paul Augustine Ejegwa , Nasreen Kausar , Nezir Aydin , Muhammet Deveci

A Pythagorean fuzzy correlation coefficient (PFCC) is a reliable approach for eliminating ambiguity during the measure of relationships. Numerous Pythagorean fuzzy correlation coefficient methods (PFCCMs) have been constructed using Pearson’s correlation coefficient technique. In this study, a new PFCCM is constructed based on Spearman’s correlation coefficient to eliminate all possible uncertainties that may impede decision-makers from making a dependable selection. To validate the construction of a new PFCCM, we examine the existing PFCCMs and pinpoint their inadequacies. Among the extant PFCCMs, one approach was constructed through Spearman’s correlation coefficient but it does not takes into cognizance the properties of the PFSs. In addition, it sometimes fails the axiomatic conditions of the PFCC, and yields invalid result for PFSs that are defined on a singleton set. These setbacks justify the construction of a new Spearman’s correlation coefficient-like PFCCM, which is shown to overcome the limitations of the extant PFCCMs. Equally, the strength of the new PFCCM is verified by some theoretical results, and it fulfills the conditions of PFCC. Additionally, the use of the novel PFCCM is discussed in the solution of supplier selection problems to eliminate supplier selection ambiguity through the multiple criteria decision-making (MCDM) approach. To unarguably show the intrinsic worth of the new PFCCM, the effectiveness of the new PFCCM is compared with the existing PFCCMs and it is observed that the new PFCCM is reliable, consistent and precise, and in the same way satisfies the axioms of the PFCC. In particular, the existing Spearman’s PFCCM yields

\infty

in Example 4, while the new PFCCM produces 0.7603, which justifies the construction of a new Spearman’s PFCCM. Finally, it is found that the new approach can suitably handle the hesitancies associated with the art of selection.

毕达哥拉斯模糊相关系数（PFCC）是一种消除关系测量过程中模糊性的可靠方法。利用Pearson相关系数技术建立了许多毕达哥拉斯模糊相关系数法（PFCCMs）。本研究基于Spearman相关系数构建了一个新的PFCCM，以消除所有可能阻碍决策者做出可靠选择的不确定性。为了验证新的PFCCM的构建，我们检查了现有的PFCCM并指出了它们的不足之处。在现有的pfccm中，有一种方法是通过Spearman相关系数构建的，但它没有考虑到pfcs的特性。此外，它有时不满足PFCC的公理条件，并且对于在单例集上定义的pfs产生无效的结果。这些挫折证明了一种新的类似Spearman相关系数的PFCCM的构建是正确的，它被证明可以克服现有PFCCM的局限性。理论结果验证了新型PFCCM的强度，满足了PFCC的要求。此外，本文还讨论了在解决供应商选择问题时如何利用新的PFCCM，通过多准则决策（MCDM）方法消除供应商选择的模糊性。为了证明新PFCCM的内在价值，将新PFCCM的有效性与现有的PFCCM进行了比较，发现新PFCCM具有可靠、一致和精确的特点，并且同样满足PFCCM的公理。特别是，在例4中，现有的Spearman’s PFCCM产生∞，而新的PFCCM产生0.7603，这证明了新的Spearman’s PFCCM的构造是合理的。最后，发现新方法可以很好地处理与选择艺术相关的犹豫。

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引用次数: 0

The impact of generative AI on management innovation 生成式人工智能对管理创新的影响

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

Journal of Industrial Information Integration

Pub Date : 2024-12-26 DOI: 10.1016/j.jii.2024.100767

Caiming Zhang , Hui Zhang

Generative Artificial Intelligence (GAI) demonstrates significant potential in the application of management and organizational innovation. This paper systematically investigates the multifaceted impacts of GAI on management decision-making, management algorithms, information integration, and various specific domains. GAI significantly enhances the accuracy of management decisions through its robust data analysis and predictive capabilities. By effectively integrating internal and external information, it reduces information asymmetry and improves both information transparency and the quality of decisions. In terms of specific application areas, GAI shows broad prospects in multiple fields, including business, education, healthcare, content creation, and game development. As GAI technology continues to advance, it will become more intelligent and adaptive. However, further research and the establishment of relevant ethical guidelines and legal frameworks are necessary to ensure its safety and reliability.

生成式人工智能（GAI）在管理和组织创新方面的应用显示出巨大的潜力。本文系统地探讨了GAI对管理决策、管理算法、信息集成以及各个特定领域的多方面影响。GAI通过其强大的数据分析和预测能力显著提高了管理决策的准确性。通过有效整合内部和外部信息，减少了信息不对称，提高了信息透明度和决策质量。就具体应用领域而言，GAI在商业、教育、医疗保健、内容创建和游戏开发等多个领域显示出广阔的前景。随着GAI技术的不断进步，它将变得更加智能和自适应。然而，进一步的研究和建立相关的伦理准则和法律框架是必要的，以确保其安全性和可靠性。

引用次数: 0

Practical implementation based on histogram of oriented gradient descriptor combined with deep learning: Towards intelligent monitoring of a photovoltaic power plant with robust faults predictions 基于定向梯度描述子直方图结合深度学习的实际实现——基于鲁棒故障预测的光伏电站智能监测

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

Journal of Industrial Information Integration

Pub Date : 2024-12-26 DOI: 10.1016/j.jii.2024.100760

Nadji Hadroug , Amel Sabrine Amari , Walaa Alayed , Abdelhamid Iratni , Ahmed Hafaifa , Ilhami Colak

The increasing complexity of photovoltaic (PV) system monitoring underscores the importance of precise fault detection and energy loss prediction. This paper proposes a deep learning-based framework that integrates multiple advanced techniques to accurately detect, localize, and predict faults in PV panels. A pre-trained Convolutional Neural Network (CNN), based on the AlexNet architecture, processes thermal imaging data for precise fault extraction. This facilitates the classification of faults, contributing to improved decision-making in PV system management.

To further enhance real-time monitoring, the framework integrates the Histogram of Oriented Gradients (HoG) descriptor with Support Vector Machine (SVM) models, enabling efficient detection and localization of hotspots across the panels. Additionally, the system leverages Long Short-Term Memory (LSTM) networks combined with fuzzy logic to predict panel performance degradation and quantify energy losses caused by detected faults. The learning process relies on the Long-Term Recurrent Convolutional Network (LRCN) to accurately forecast defects by analyzing power efficiency loss rates.

Experimental results confirm the effectiveness and reliability of the proposed framework. Achieving an accuracy of 95.45%, with a true positive rate of 91.67% and a true negative rate of 100%, the system demonstrates robust fault detection capabilities. These results highlight the framework’s potential to mitigate power losses, ensuring optimal operation of PV systems. This intelligent solution offers a significant advancement in PV system maintenance and monitoring, providing a scalable approach for real-world applications.

随着光伏系统监测的日益复杂，精确的故障检测和能量损失预测显得尤为重要。本文提出了一种基于深度学习的框架，该框架集成了多种先进技术，可以准确地检测、定位和预测光伏板故障。基于AlexNet架构的预训练卷积神经网络（CNN）处理热成像数据以精确提取故障。这有助于对故障进行分类，有助于改进光伏系统管理的决策。

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引用次数: 0

Blockchain-enabled federated learning-based privacy preservation framework for secure IoT in precision agriculture 基于区块链的联邦学习隐私保护框架，用于精准农业中的安全物联网

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

Journal of Industrial Information Integration

Pub Date : 2024-12-25 DOI: 10.1016/j.jii.2024.100765

Ishu Sharma , Vikas Khullar

The aim of this paper is to establish a secure and privacy preserved IoT communication in precision agriculture. For achieving security and privacy, federated learning system have been deployed on blockchain ecosystem to classify IoT communication attacks in precision agriculture. This paper has utilized recent ‘CICIoT2023’ database to automate identification of prominent cyber-attacks in IoT. Sharing data between devices raised privacy concerns but without sharing data knowledge also getting limited for classification of diverse attacks. So, we have deployed federated learning ecosystem over Ethereum block chain to achieve collaborative learning with privacy preserving communication. In methodology, initially recent dataset about cyber-attacks classification have been collected, pre-processed and distributed for multiple devices. The integration of the Ethereum blockchain with IPFS decentralized file storage for transmitting the learning model from client device to server and vice versa enhances the overall security and trust of the system. Initially basic machine learning algorithms have been employed in standard single machine environment to establish benchmark results. Then a deep neural network has been deployed in blockchain based federated learning environment to analyse the outcome using identical and non-identical data distributions. In results significant outcomes have been achieved in terms of privacy and security with high accuracy, precision, recall, etc., while training deep neural network. This paper has worked for number of subset data classifications to propose and analyze overall view for securing IoT communication from cyber-attacks in precision agriculture.

本文的目的是在精准农业中建立一个安全且保密的物联网通信。为了实现安全和隐私，在区块链生态系统上部署了联邦学习系统，对精准农业中的物联网通信攻击进行分类。本文利用最新的“CICIoT2023”数据库自动识别物联网中突出的网络攻击。在设备之间共享数据引起了隐私问题，但如果不共享数据知识，也会限制对各种攻击的分类。因此，我们在以太坊区块链上部署了联邦学习生态系统，以实现具有隐私保护通信的协作学习。在方法上，最初收集了关于网络攻击分类的最新数据集，对其进行了预处理并分发给多个设备。以太坊区块链与IPFS去中心化文件存储的集成，将学习模型从客户端设备传输到服务器，反之亦然，增强了系统的整体安全性和信任度。最初，基本的机器学习算法已在标准的单机环境中使用，以建立基准结果。然后在基于b区块链的联邦学习环境中部署深度神经网络，对相同和非相同数据分布的结果进行分析。结果表明，在训练深度神经网络的同时，在隐私和安全方面取得了显著的成果，具有较高的准确率、精密度、召回率等。本文对多个子集数据分类进行了研究，提出并分析了在精准农业中保护物联网通信免受网络攻击的总体观点。

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引用次数: 0

Bottom-up green manufacturing strategy in the wire and cable industry: A Z-DEMATEL approach for identifying critical success criteria 电线电缆行业自下而上的绿色制造战略：确定关键成功标准的Z-DEMATEL方法

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

Journal of Industrial Information Integration

Pub Date : 2024-12-25 DOI: 10.1016/j.jii.2024.100761

Huai-Wei Lo , Sheng-Wei Lin

Global warming and environmental degradation are prompting enterprises to consider environmentally conscious manufacturing as a strategic imperative. Consequently, the Wire and Cable Industry (W&CI) must urgently transition to Green Manufacturing (GM) to address these environmental challenges and achieve sustainable development. Numerous studies have focused on GM in large-scale enterprises, particularly within the context of Green Supply Chain Management (GSCM). However, most perspectives are top-down, with few studies examining GM strategies from a bottom-up approach, especially regarding the supply chain's crucial role in addressing manufacturers’ pressing needs. This study employs a modified Z-DEMATEL (Z number-based Decision Making and Trial Evaluation Laboratory) technique to evaluate and determine strategic dimensions and criteria. The Z-number enhances conventional fuzzy numbers, increasing the reliability of expert evaluations and reflecting the confidence of the evaluation environment under uncertainty. The findings indicate that “managerial support,” “corporate image,” “legal compliance,” “green design”, and “liability risk” are the top five important criteria for GM. On other hand, the modified Z-DEMATEL technique demonstrates that an Influential Network Relation Map (INRM) provides decision-makers with a quick understanding of the causality between criteria, offering insights into GM strategy. By evaluating different manufacturing practices in the wire and cable industry using a hybrid MCDM model, companies can identify improvement opportunities and make informed decisions about the most sustainable practices from a holistic perspective.

全球变暖和环境恶化促使企业将环保制造视为一项战略要务。因此，电线电缆行业（W&；CI）必须紧急过渡到绿色制造（GM），以应对这些环境挑战并实现可持续发展。许多研究都集中在大型企业中的转基因，特别是在绿色供应链管理（GSCM）的背景下。然而，大多数观点都是自上而下的，很少有研究从自下而上的角度来审视转基因战略，特别是关于供应链在解决制造商迫切需求方面的关键作用。本研究采用改良的Z- dematel（基于Z数的决策和试验评估实验室）技术来评估和确定战略维度和标准。z数增强了传统模糊数，提高了专家评价的可靠性，反映了不确定条件下评价环境的置信度。研究结果表明，“管理支持”、“企业形象”、“法律合规”、“绿色设计”和“责任风险”是通用汽车的五大重要标准。另一方面，改进的Z-DEMATEL技术表明，影响网络关系图（INRM）使决策者能够快速了解标准之间的因果关系，从而为通用汽车战略提供见解。通过使用混合MCDM模型评估电线电缆行业的不同制造实践，公司可以识别改进机会，并从整体角度做出最可持续的决策。

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引用次数: 0

Digital twin-enabled multi-robot system for collaborative assembly of unorganized parts 用于无组织零件协同装配的数字双体多机器人系统

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

Journal of Industrial Information Integration

Pub Date : 2024-12-25 DOI: 10.1016/j.jii.2024.100764

Kyaw Htet Oo, Pisut Koomsap, Duangthida Hussadintorn Na Ayutthaya

Agility and flexibility in production are essential to meet the challenge of the demand surge for diverse personalized products that come in small volumes. Automatic assembly is one critical process at stake due to the inherent complexity of the number of parts, their shapes, and the randomness of their initial orientations and positions upon arrival at the station. This research introduces a digital twin framework with an information integration layer designed to enhance the flexibility and efficiency of multi-robot operating systems by collaborating with operator skills, addressing the demand for personalized production. The digital twin model allows operators to remotely implant their skills to oversee the assembly process and to control and train the multi-robot operations through an immersive virtual reality interface upon the arrival of new orders without disrupting the current operations of the physical system. The virtual world allows simulations of the algorithms for collision-free movement and task optimization among multiple robots. Besides, image processing techniques are employed to identify parts arriving in random orientations at the station, providing flexibility during the physical assembly phase. According to the investigation, integrating virtual reality and real-time control challenges data processing and requires robust networking and computational resources. This study contributes to multi-robot operations by providing a scalable and adaptable solution by collaborating with operator skills that enhance both the planning and execution phases of complex assembly processes to new product specifications and changes in design. The methodology presented is foreseen to apply to other manufacturing processes.

生产中的敏捷性和灵活性对于满足小批量多样化个性化产品需求激增的挑战至关重要。自动装配是一个关键的过程，因为零件的数量、形状和到达车站时初始方向和位置的随机性具有固有的复杂性。本研究引入了一个带有信息集成层的数字孪生框架，旨在通过与操作员技能的协作来提高多机器人操作系统的灵活性和效率，以满足个性化生产的需求。数字孪生模型允许操作员远程植入他们的技能来监督装配过程，并在新订单到来时通过沉浸式虚拟现实界面控制和训练多机器人操作，而不会中断当前物理系统的操作。虚拟世界允许对多个机器人之间的无碰撞运动和任务优化算法进行模拟。此外，采用图像处理技术来识别以随机方向到达车站的部件，从而在物理组装阶段提供灵活性。根据调查，整合虚拟现实和实时控制挑战数据处理，需要强大的网络和计算资源。本研究为多机器人操作提供了一种可扩展和可适应的解决方案，通过与操作员的技能协作，提高了复杂装配过程的规划和执行阶段，以适应新产品规格和设计变化。所提出的方法预计将适用于其他制造过程。

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引用次数: 0