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Human-robot interaction for extraction of robotic disassembly information 基于人机交互的机器人拆卸信息提取
3区 工程技术 Q1 Engineering Pub Date : 2023-09-21 DOI: 10.1080/0951192x.2023.2257667
Joao Paulo Jacomini Prioli, Jeremy L. Rickli
ABSTRACTDisassembly of end-of-use products is critical to the economic feasibility of circular feedstock, reusing, recycling and remanufacturing loops. High-volume disassembly operations are constrained by disassembly complexity and product variability. The capability to buffer against timing, quantity and quality uncertainties of end-of-use products impacts the efficiency and profitability of demanufacturing systems. To achieve a competitive operation in the manufacturing life-cycle, disassembly systems need automated lines, however, the unpredictability of core supply challenges automation adaptability. Disassembly robot trajectories that are programmed manually or controlled by vision systems can be time intensive and subject to variability in lighting conditions and image recognition models. Alternatively, this paper presents a novel human-robot disassembly framework to systematically extract and generate robot trajectories derived from human-collaborative robot (cobot) disassembly. The collaborative training station proposed classifies trajectory segments and then adjusts trajectories to station-specific robots in a high-volume disassembly line. Virtual and physical collaborative disassembly case studies are presented and discussed. Results demonstrate the effectiveness of the disassembly data extraction method but indicate a disparity between the expected and ideal disassembly trajectories due to variability from human handling, which is further discussed in this paper.KEYWORDS: Disassemblycollaborative robotsremanufacturingsystem framework Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis work was supported by the Critical Materials Institute in collaboration with Oak Ridge National Laboratory [FA-3.3.11].
摘要报废产品的拆解对循环原料循环、再利用循环、再制造循环的经济可行性至关重要。大批量拆卸操作受到拆卸复杂性和产品可变性的限制。对时间、数量和质量的不确定性的缓冲能力影响着拆解系统的效率和盈利能力。为了在制造生命周期中实现竞争性操作,拆卸系统需要自动化生产线,然而,核心供应的不可预测性对自动化适应性提出了挑战。手动编程或由视觉系统控制的拆卸机器人轨迹可能是时间密集型的,并且受到照明条件和图像识别模型的变化的影响。另外,本文提出了一种新的人机拆卸框架,用于系统地提取和生成由人机协作机器人(cobot)拆卸导出的机器人轨迹。提出的协同训练站对轨迹段进行分类,然后对大批量拆解线上特定工位的机器人调整轨迹。提出并讨论了虚拟和物理协同拆卸的案例研究。结果证明了拆卸数据提取方法的有效性,但由于人为操作的可变性,期望和理想的拆卸轨迹之间存在差异,本文将进一步讨论这一点。关键词:拆卸协作机器人再制造系统框架披露声明作者未报告潜在利益冲突。本研究由关键材料研究所与橡树岭国家实验室合作支持[FA-3.3.11]。
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引用次数: 0
Unlocking factors of digital twins for smart manufacturing: a case of emerging economy 为智能制造解锁数字孪生要素:以新兴经济体为例
3区 工程技术 Q1 Engineering Pub Date : 2023-09-21 DOI: 10.1080/0951192x.2023.2257655
Bhaskar B. Gardas, Angappa Gunasekaran, Vaibhav S. Narwane
ABSTRACTThe Industry 4.0/smart manufacturing paradigm has significantly changed the activities and processes of organizations. Emergent smart manufacturing technology called a ‘Digital Twin’ (DT) aids organizations in enhancing overall performance by creating a virtual prototype of a real system. However, DT technology adoption in emerging economies is in the nascent stage. This research aims to identify the determinants affecting the adoption of DT technology in Indian manufacturing firms. Based on an extensive literature survey and experts’ opinions, 14 determinants were identified, and these determinants were analyzed using a hybrid multi-attribute decision-making approach to understand the contextual relationship and to identify the cause–effect relationship amongst them. Based on these results, the most critical determinants were explored, namely ‘Real-time system operations and tracking’, ‘Integration, the convergence of systems, processes & resources and enterprise collaboration’, ‘Information and Data management within or between the systems’. The manufacturing organizations of emerging economies need to consider these determinants for the effective adoption of DT technology, and policymakers can use the findings of this study to develop appropriate strategies.KEYWORDS: Information managementdigital twinsemerging economiesmanufacturing firmstechnology adoptiondecision-making Disclosure statementNo potential conflict of interest was reported by the author(s).
摘要工业4.0/智能制造范式极大地改变了组织的活动和流程。被称为“数字孪生”(DT)的新兴智能制造技术通过创建真实系统的虚拟原型来帮助组织提高整体绩效。然而,DT技术在新兴经济体的采用尚处于起步阶段。本研究旨在确定影响印度制造企业采用DT技术的决定因素。在广泛的文献调查和专家意见的基础上,确定了14个决定因素,并使用混合多属性决策方法对这些决定因素进行分析,以了解上下文关系并确定它们之间的因果关系。基于这些结果,我们探讨了最关键的决定因素,即“实时系统操作和跟踪”、“系统、流程和资源的集成、融合以及企业协作”、“系统内部或系统之间的信息和数据管理”。新兴经济体的制造业组织需要考虑这些决定因素,以有效采用DT技术,政策制定者可以利用本研究的结果制定适当的策略。关键词:信息管理数字孪生新兴经济体制造企业技术采用决策披露声明作者未报告潜在的利益冲突。
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引用次数: 1
A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis 基于人机共生的工业5.0智能制造系统设计框架
3区 工程技术 Q1 Engineering Pub Date : 2023-09-20 DOI: 10.1080/0951192x.2023.2257634
Margherita Peruzzini, Elisa Prati, Marcello Pelicciari
ABSTRACTThe concept of Industry 5.0 (I5.0) promotes the human-centricity as the core value behind the evolution of smart manufacturing systems (SMSs), based on a novel use of digital technologies in the design and management of modern industrial systems to take up the socio-technical challenges. In this context, the paper proposes a Smart Manufacturing Systems Design (SMSD) framework enabling I5.0, based on the human-automation symbiosis. Thanks to an ‘Augmented Digital Twin’ (ADT) able to integrate and digitize all the entities of the factory (i.e. machines, robots, environments, interfaces, people), AI-driven applications can be built to support the user domain and make people and machines co-evolve thanks to a systematic data sharing between physical and digital assets (e.g. digital twin, virtual mock-ups, human-machine interfaces), optimizing factory productivity and workers wellbeing. In this framework, machines and humans can both generate knowledge and learn from each other, generating a virtuous co-evolution, supporting the understanding of the human-machine interplay and the creation of an effective collaboration between people and SMSs. The framework was conceived and validated involving four industrial companies, belonging to diverse sectors, interested in overcoming the current limits of I4.0 lines by including the human factors for future SMS management.KEYWORDS: Industry 5.0Operator 4.0Operator 5.0augmented digital twinsmart manufacturing systemshuman-automation symbiosis AcknowledgementsThis research is funded by the European Community under two HORIZON 2020 programmes, grant agreement No. 958303 (PeneloPe) https://penelope-project.eu/ and grant agreement No. 101091780 (DaCapo) https://www.dacapo-project.eu/.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the H2020 Industrial Leadership [958303].
摘要工业5.0 (I5.0)的概念将以人为中心作为智能制造系统(sms)发展背后的核心价值,基于在现代工业系统的设计和管理中新颖地使用数字技术来应对社会技术挑战。在此背景下,本文提出了一个基于人机共生的智能制造系统设计(SMSD)框架,支持I5.0。由于“增强数字孪生体”(ADT)能够集成和数字化工厂的所有实体(即机器,机器人,环境,界面,人员),人工智能驱动的应用程序可以构建支持用户领域,并使人和机器共同进化,这要归功于物理和数字资产之间的系统数据共享(例如数字孪生体,虚拟样机,人机界面),优化工厂生产力和工人福利。在这个框架中,机器和人类都可以产生知识并相互学习,从而产生良性的共同进化,支持对人机相互作用的理解,并在人和短信之间建立有效的协作。该框架的构思和验证涉及四家工业公司,它们属于不同的行业,有兴趣通过将人为因素纳入未来的SMS管理来克服当前I4.0线的限制。关键字:工业5.0操作员4.0操作员5.0增强数字孪生智能制造系统人机共生致谢本研究由欧洲共同体在两个HORIZON 2020计划下资助,资助协议号为958303 (PeneloPe) https://penelope-project.eu/和资助协议号为101091780 (DaCapo) https://www.dacapo-project.eu/.Disclosure声明作者未报告潜在的利益冲突。本研究得到了H2020工业领导[958303]的支持。
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引用次数: 0
Evaluation of the reconfigurability of manufacturing systems based on fuzzy logic taking into account the links between the characteristics of the RMS 基于模糊逻辑的制造系统可重构性评价,考虑了RMS特性之间的联系
3区 工程技术 Q1 Engineering Pub Date : 2023-09-19 DOI: 10.1080/0951192x.2023.2257632
Kombaya Touckia Jesus
ABSTRACTToday, faced with the global COVID-19 crisis, manufacturing systems are subject to constraints caused by an uncertain and changing environment dominated by strong international competition. In this context, many indicators have been proposed to evaluate the responsiveness and flexibility of production systems. The literature review shows that some research streams have received positive attention from the research community, these streams include RMS characteristics analysis, RMS performance analysis and applied research and field applications, while other streams such as the reconfigurability level assessment and reconfigurability towards Industry 4.0, still need further research. This paper shows the need for more rigorous analytical measures to assess the level of reconfigurability, as there are still no accurate and quantitative RMS reconfigurability indices. There is a need for successful case studies detailing best practices to effectively guide the transition of modern industrial enterprises towards reconfigurable manufacturing. This paper proposes, a decision support tool to help manufacturers evaluate reconfigurability according to its characteristics (modularity, scalability, integrability, convertibility, diagnosability and customization) using fuzzy logic.KEYWORDS: Reconfigurable manufacturing system (RMS)decision-makingfuzzy logicDEMATELMAUT Disclosure statementNo potential conflict of interest was reported by the author(s).Availability of data and materialThe authors confirm that the data and material supporting the findings of this work are available within the article. The raw data that support the findings of this study are available from the corresponding author, upon a reasonable request.Ethical approvalThe authors declare compliance with ethical standards.Additional informationFundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
【摘要】面对全球新冠肺炎危机的今天,制造系统受到了由激烈的国际竞争主导的不确定和不断变化的环境所带来的约束。在这方面,提出了许多指标来评价生产系统的反应能力和灵活性。文献综述表明,RMS特性分析、RMS性能分析以及应用研究和现场应用等研究方向得到了学术界的积极关注,而面向工业4.0的可重构性水平评估和可重构性等研究方向仍有待进一步研究。由于目前还没有准确定量的均方根可重构性指标,因此需要更严格的分析方法来评估均方根可重构性水平。有必要进行成功的案例研究,详细说明最佳做法,以有效地指导现代工业企业向可重构制造过渡。本文提出了一种基于模糊逻辑的决策支持工具,帮助制造商根据可重构性的模块化、可扩展性、可积性、可转换性、可诊断性和可定制性等特征对可重构性进行评估。关键词:可重构制造系统(RMS)决策模糊逻辑dematelaut披露声明作者未报告潜在利益冲突。数据和材料的可获得性作者确认,在文章中可以获得支持本研究结果的数据和材料。支持本研究结果的原始数据可在合理要求下从通讯作者处获得。伦理批准作者声明符合伦理标准。本研究没有从公共、商业或非营利部门的资助机构获得任何特定的资助。
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引用次数: 0
A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach 用于uv固化热固性材料的机器人3D打印机:使用数据驱动方法进行尺寸预测
3区 工程技术 Q1 Engineering Pub Date : 2023-09-18 DOI: 10.1080/0951192x.2023.2257652
Luis Velazquez, Genevieve Palardy, Corina Barbalata
ABSTRACTThis paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning. Experimental results demonstrate the capabilities of the robotic 3D printer and the accuracy of the predictive approach.KEYWORDS: UV-curable thermosetsrobotic systemadditive manufacturingmachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Louisiana Board of Regents [LEQSF-EPS(2022)-LAMDASeed-Track1B-11]; Louisiana Board of Regents [LEQSF-EPS(2021)-LAMDASeed-Track1B-01]; Office of Integrative Activities [OIA1946231].
摘要本文介绍了一种专门用于紫外线固化热固性材料的机器人3D打印机,该打印机的打印参数可通过预测建模策略进行选择。设计了专用挤出头,并与UR5e机械臂集成。开发了软件包,使挤出机头和机械臂之间的通信,并实施了控制系统来调节打印过程。提出了一种基于前馈神经网络(FNN)或卷积神经网络(CNN)的预测方法,用于基于工艺参数估计未来打印件的尺寸。这样可以为高质量的打印选择适当的参数。该策略旨在减少昂贵的材料和打印参数调整的试错活动。实验结果验证了机器人3D打印机的性能和预测方法的准确性。关键词:紫外光固化热固性机器人系统增材制造机器学习披露声明作者未报告潜在利益冲突。本研究得到了路易斯安那州校董会的支持[LEQSF-EPS(2022)-LAMDASeed-Track1B-11];路易斯安那州董事会[LEQSF-EPS(2021)-LAMDASeed-Track1B-01];综合活动办公室[OIA1946231]。
{"title":"A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach","authors":"Luis Velazquez, Genevieve Palardy, Corina Barbalata","doi":"10.1080/0951192x.2023.2257652","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257652","url":null,"abstract":"ABSTRACTThis paper presents a robotic 3D printer specifically designed for ultraviolet (UV)-curable thermosets, whose printing parameters can be selected by using a predictive modeling strategy. A specialized extruder head was designed and integrated with a UR5e robotic arm. Software packages were developed to enable the communication between the extruder head and the robotic arm, and control systems were implemented to regulate the printing process. A predictive approach using either a feedforward neural network (FNN) or convolution neural network (CNN) is proposed for estimating the dimensions of future prints based on the process parameters. This enables selection of the appropriate parameters for high-quality prints. This strategy aims to decrease expensive trial-and-error campaigns for material and printing parameter tuning. Experimental results demonstrate the capabilities of the robotic 3D printer and the accuracy of the predictive approach.KEYWORDS: UV-curable thermosetsrobotic systemadditive manufacturingmachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Louisiana Board of Regents [LEQSF-EPS(2022)-LAMDASeed-Track1B-11]; Louisiana Board of Regents [LEQSF-EPS(2021)-LAMDASeed-Track1B-01]; Office of Integrative Activities [OIA1946231].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135148674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data- driven and knowledge- guided prediction model of milling tool life grade 数据驱动和知识指导的铣刀寿命等级预测模型
3区 工程技术 Q1 Engineering Pub Date : 2023-09-18 DOI: 10.1080/0951192x.2023.2257620
Fuqiang Zhang, Fengli Xu, Xueliang Zhou, Kai Ding, Shujun Shao, Chao Du, Jiewu Leng
ABSTRACTModels that predict tool life based on wear mechanism knowledge are typically inaccurate, as the use of simplified model parameters can have a significant effect on this prediction. While a tool life prediction model based on sample cutting data is limited to specific working conditions, which makes tool life prediction difficult to generalize, and needs a large amount of historical data as support. In this paper, the empirical formula of tool life based on wear mechanism knowledge was combined with a neural network, which can significantly improve prediction accuracy. Firstly, a concept of tool life grade is proposed, and its classification standard is outlined. Secondly, a prediction model based on the empirical life formula and experimental data was established. Thirdly, a tool wear prediction model based on a convolutional neural network (CNN) was established through the real-time tool condition data, and the corresponding life compensation strategy can be determined by comparing this with the historical data. Finally, the empirical life grade was adjusted to obtain the real-time tool life grade. A case example shows that the data-driven knowledge-guided prediction model can significantly improve the recognition accuracy of tool life grade.KEYWORDS: Milling tool life gradewear mechanism knowledgecondition dataconvolutional neural networkreal time prediction AcknowledgementsThis work was supported in part by the National Key R&D Program of China (2021YFB3301702), Major Special Science and Technology Project of Shaanxi Province, China (No.2018zdzx01-01-01), and the Natural Science Foundation of Shaanxi Province, China (No. 2021JM-173).Disclosure statementNo potential conflict of interest was reported by the authors.Contribution StatementFuqiang Zhang provided the research idea; Fengli Xu wrote the paper and developed a software testing system; Xueliang Zhou and Jiew Leng conducted review and editing; Kai Ding provided the funding acquisition; Shujun Shao and Chao Du provided the data set.Additional informationFundingThe work was supported by the National Key R&D Program of China [2021YFB3301702]; Natural Science Foundation of Shaanxi Province, China [2021JM-173]; Major Special Science and Technology Project of Shaanxi Province, China [2018zdzx01-01-01].
摘要基于磨损机理知识预测刀具寿命的模型通常是不准确的,因为使用简化的模型参数会对这种预测产生重大影响。而基于样本切削数据的刀具寿命预测模型受限于特定工况,使得刀具寿命预测难以泛化,需要大量的历史数据作为支持。将基于磨损机理知识的刀具寿命经验公式与神经网络相结合,可显著提高预测精度。首先,提出了刀具寿命等级的概念,并概述了刀具寿命等级的分类标准。其次,建立了基于经验寿命公式和实验数据的预测模型;第三,通过实时刀具状态数据,建立基于卷积神经网络(CNN)的刀具磨损预测模型,并与历史数据进行对比,确定相应的寿命补偿策略;最后对经验寿命等级进行调整,得到实时刀具寿命等级。实例表明,数据驱动的知识引导预测模型能显著提高刀具寿命等级的识别精度。关键词:铣刀寿命分级磨损机理知识状态数据卷积神经网络实时预测致谢国家重点研发计划项目(2021YFB3301702)、陕西省重大科技专项项目(No.2018zdzx01-01-01)和陕西省自然科学基金项目(No. 2021JM-173)资助。披露声明作者未报告潜在的利益冲突。张富强提供了研究思路;徐凤丽撰写论文,开发软件测试系统;周学良、冷洁主编;凯鼎提供融资收购;邵树军和杜超提供了数据集。基金资助:国家重点研发计划[2021YFB3301702];陕西省自然科学基金资助项目[2021JM-173];陕西省重大科技专项项目[2018zdzx01-01-01]。
{"title":"<sup>Data-</sup> <sup>driven</sup> <sup>and</sup> <sup>knowledge-</sup> <sup>guided prediction model of milling tool life</sup> <sup>grade</sup>","authors":"Fuqiang Zhang, Fengli Xu, Xueliang Zhou, Kai Ding, Shujun Shao, Chao Du, Jiewu Leng","doi":"10.1080/0951192x.2023.2257620","DOIUrl":"https://doi.org/10.1080/0951192x.2023.2257620","url":null,"abstract":"ABSTRACTModels that predict tool life based on wear mechanism knowledge are typically inaccurate, as the use of simplified model parameters can have a significant effect on this prediction. While a tool life prediction model based on sample cutting data is limited to specific working conditions, which makes tool life prediction difficult to generalize, and needs a large amount of historical data as support. In this paper, the empirical formula of tool life based on wear mechanism knowledge was combined with a neural network, which can significantly improve prediction accuracy. Firstly, a concept of tool life grade is proposed, and its classification standard is outlined. Secondly, a prediction model based on the empirical life formula and experimental data was established. Thirdly, a tool wear prediction model based on a convolutional neural network (CNN) was established through the real-time tool condition data, and the corresponding life compensation strategy can be determined by comparing this with the historical data. Finally, the empirical life grade was adjusted to obtain the real-time tool life grade. A case example shows that the data-driven knowledge-guided prediction model can significantly improve the recognition accuracy of tool life grade.KEYWORDS: Milling tool life gradewear mechanism knowledgecondition dataconvolutional neural networkreal time prediction AcknowledgementsThis work was supported in part by the National Key R&D Program of China (2021YFB3301702), Major Special Science and Technology Project of Shaanxi Province, China (No.2018zdzx01-01-01), and the Natural Science Foundation of Shaanxi Province, China (No. 2021JM-173).Disclosure statementNo potential conflict of interest was reported by the authors.Contribution StatementFuqiang Zhang provided the research idea; Fengli Xu wrote the paper and developed a software testing system; Xueliang Zhou and Jiew Leng conducted review and editing; Kai Ding provided the funding acquisition; Shujun Shao and Chao Du provided the data set.Additional informationFundingThe work was supported by the National Key R&D Program of China [2021YFB3301702]; Natural Science Foundation of Shaanxi Province, China [2021JM-173]; Major Special Science and Technology Project of Shaanxi Province, China [2018zdzx01-01-01].","PeriodicalId":13907,"journal":{"name":"International Journal of Computer Integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using machine learning for cutting tool condition monitoring and prediction during machining of tungsten 利用机器学习技术对钨加工过程中的刀具状态进行监测和预测
3区 工程技术 Q1 Engineering Pub Date : 2023-09-15 DOI: 10.1080/0951192x.2023.2257648
Samuel Omole, Hakan Dogan, Alexander J G Lunt, Simon Kirk, Alborz Shokrani
Machining of single-phase tungsten, used as a plasma facing material in fusion energy reactors, is commonly associated with rapid tool wear and short tool life. Conventional methods of monitoring tool wear or changing cutting tools after a predetermined period are inefficient and can lead to unnecessary tool change or risk damaging the workpiece. Tool wear can adversely affect the surface finish and dimensional tolerances of machined parts. Predicting its onset can avoid this critical damage whilst ensuring maximum tool life is utilised. In this paper, firstly the tool life results in end milling single-phase tungsten using different cutting tool geometries and cutting speeds are provided for the first time. A novel method is proposed by combining sensor signal prediction and classification machine learning models. It works by forecasting the cutting tool bending moment signal which is then used for predicting future cutting tool condition in end milling of pure dense tungsten. A series of machining experiments, covering the whole life of a cutting tool, were performed to collect the sensor signals. The current time series signal from the sensory tool holder is employed to forecast the future signal by training a 1D convolutional neural network (1D CNN) and an artificial neural network (ANN). The forecasted signal is then used to predict the state of the cutting tool in the future. Machine learning classifiers namely, random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) supervised learning models were trained and validated on actual sensor signals to correlate the tool conditions with specific sensor signal features. The investigations revealed that the 1D CNN performed best in forecasting the time series sensor signal whilst achieving a mean absolute error of 3.37. In addition, the RF, when trained on Wavelet Scattering features, resulted in the most accurate classification of sensor signals for tool condition detection. The analysis showed that the combination of 1D CNN signal forecasting, feature extraction through statistical analyses and RF classifier performs best in predicting the state of a cutting tool in near future. Using this method allows for decision making for changing the tool whilst ensuring that the maximum useful life of a cutting tool is utilised. It also enables preventing undesired damage to the machined surface due to late detection of tool wear or delays in taking appropriate actions. The application of this method can reliably reduce the manufacturing costs and resource consumption associated with cutting tools for machining tungsten and minimise tool wear induced damage to the workpiece.
在聚变能反应堆中用作等离子体表面材料的单相钨的加工通常与刀具磨损快和刀具寿命短有关。监测刀具磨损或在预定周期后更换刀具的传统方法效率低下,可能导致不必要的刀具更换或有损坏工件的风险。刀具磨损会对加工零件的表面光洁度和尺寸公差产生不利影响。预测其开始可以避免这种严重的损害,同时确保最大限度地利用工具寿命。本文首次给出了不同刀具几何形状和切削速度对单相钨立铣削刀具寿命的影响。提出了一种将传感器信号预测与分类机器学习模型相结合的新方法。该方法通过对纯密钨立铣削过程中刀具弯矩信号的预测来预测未来刀具状态。在刀具的整个使用寿命期间进行一系列加工实验,采集传感器信号。通过训练一维卷积神经网络(1D CNN)和人工神经网络(ANN),利用来自感官刀柄的当前时间序列信号预测未来信号。然后用预测的信号来预测刀具未来的状态。机器学习分类器即随机森林(RF),支持向量机(SVM)和极端梯度增强(XGBoost)监督学习模型在实际传感器信号上进行训练和验证,以将工具条件与特定传感器信号特征关联起来。研究表明,一维CNN在预测时间序列传感器信号方面表现最好,平均绝对误差为3.37。此外,当对小波散射特征进行训练时,RF可以对传感器信号进行最准确的分类,用于工具状态检测。分析表明,结合1D CNN信号预测、统计分析特征提取和RF分类器对刀具近期状态的预测效果最好。使用这种方法可以在确保切削刀具最大使用寿命的同时做出更换刀具的决策。它还可以防止由于刀具磨损检测晚或采取适当措施的延迟而对加工表面造成的意外损坏。该方法的应用可以可靠地降低钨加工刀具的制造成本和资源消耗,并最大限度地减少刀具磨损对工件的损伤。
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引用次数: 0
The deep learning-based equipment health monitoring model adopting subject matter expert 采用主题专家的基于深度学习的设备健康监测模型
3区 工程技术 Q1 Engineering Pub Date : 2023-09-15 DOI: 10.1080/0951192x.2023.2257665
Jr-Fong Dang
ABSTRACTThe emergence of Industry 4.0 has led to the development of modern production machines that are usually equipped with advanced sensors to collect data for further analysis. This study proposes a deep learning-based framework to perform equipment health monitoring (EHM) and further broadens its applicability through the integration of subject matter expert (SME) knowledge. A sliding window strategy was adopted to perform EHM in real time. Moreover, an autocorrelation function (ACF) and a partial autocorrelation function (PACF) were employed to determine the optimal window size based on the elbow method. An empirical study was conducted to demonstrate the effectiveness and practicality of the proposed framework. Furthermore, to provide better prediction results, an optimal combination of hyperparameters that minimized the loss function and further validated the window size obtained by the ACF and PACF was determined and used. The results showed that the proposed algorithm outperformed other representative machine learning models. Finally, a general framework was adopted to maintain equipment performance.KEYWORDS: Deep learningequipment health monitoringsliding windowautocorrelation function (ACF)partial autocorrelation function (PACF)subject matter expert (SME) AcknowledgementsThe author would like to acknowledge a very good collection of data from Hsiang-Po Tsai and the support from Hong-Yi Huang.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Ministry of Science and Technology of Taiwan under Grant 109-2222-E-035-007- and 110-2221-E-005-087-.
摘要工业4.0的出现导致了现代生产机器的发展,这些机器通常配备了先进的传感器来收集数据以进行进一步分析。本研究提出了一个基于深度学习的框架来执行设备健康监测(EHM),并通过整合主题专家(SME)知识进一步扩大其适用性。采用滑动窗口策略实时执行EHM。在此基础上,利用自相关函数(ACF)和部分自相关函数(PACF)确定了基于肘形法的最佳窗口大小。实证研究证明了该框架的有效性和实用性。此外,为了提供更好的预测结果,确定并使用了最小化损失函数并进一步验证ACF和PACF获得的窗口大小的超参数的最优组合。结果表明,该算法优于其他具有代表性的机器学习模型。最后,采用了维护设备性能的总体框架。关键词:深度学习;设备健康监测;滑动窗口自相关函数(ACF);偏自相关函数(PACF);主题专家(SME)致谢作者要感谢蔡香波(Hsiang-Po Tsai)提供的非常好的数据收集和黄弘毅(Hong-Yi Huang)的支持。披露声明作者未报告潜在的利益冲突。本研究由台湾科学技术部资助,项目为109-2222-E-035-007-和110-2221-E-005-087-。
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引用次数: 0
Research on dynamic scheduling and perception method of assembly resources based on digital twin 基于数字孪生的装配资源动态调度与感知方法研究
3区 工程技术 Q1 Engineering Pub Date : 2023-09-14 DOI: 10.1080/0951192x.2023.2257650
Yunrui Wang, Yao Wang, Wengzhe Ren, Zhengli Wu, Juan Li
ABSTRACTThe uncertainty and dynamic changes in assembly resources can seriously affect the normal operation of the assembly plant. In response to the problems of incomprehensive resource control mechanism, poor timeliness of monitoring data, and low level of scheduling intelligence in the assembly plant, a dynamic scheduling and perception method of assembly resources based on digital twin is proposed so that the uncertainties in the assembly process can be monitored and dealt with in time. In this paper, a dynamic scheduling model of assembly resources based on a digital twin is constructed, and the operation mechanism of assembly resources in the constructed digital twin model is expounded. And the dynamic perception method of assembly resources based on the Petri network is studied in detail, and the perception and interaction models of four assembly resources in the product assembly process are constructed: workpiece, handling equipment, assembly center, and storage area. Finally, combined with the assembly workshop of enterprise A’s frame factory, the Petri network model is simulated with the help of the CPN Tools simulation tool to obtain real-time and simulation data such as assembly resources and workstation operation time are obtained, which provides a scientific basis for the smooth implementation of enterprise assembly plan and dynamic scheduling of assembly resources.KEYWORDS: Petri netdigital twinassembly resourcesdynamic perception Disclosure statementNo potential conflict of interest was reported by the author(s).
摘要装配资源的不确定性和动态变化会严重影响装配工厂的正常运行。针对装配厂资源控制机制不完善、监控数据时效性差、调度智能水平低等问题,提出了一种基于数字孪生的装配资源动态调度与感知方法,以便及时监控和处理装配过程中的不确定性。构建了基于数字孪生的装配资源动态调度模型,阐述了构建的数字孪生模型中装配资源的运行机制。详细研究了基于Petri网络的装配资源动态感知方法,构建了产品装配过程中工件、搬运设备、装配中心和存储区域四种装配资源的感知与交互模型。最后,结合A企业车架厂装配车间,借助CPN Tools仿真工具对Petri网络模型进行仿真,获得装配资源和工作站运行时间等实时仿真数据,为企业装配计划的顺利实施和装配资源的动态调度提供科学依据。关键词:Petri网数字双组合资源动态感知披露声明作者未报告潜在利益冲突。
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引用次数: 0
Proposal of a maturity model for additive manufacturing: theoretical development and case study in automotive industry 增材制造成熟度模型的提出:汽车行业增材制造的理论发展与案例研究
3区 工程技术 Q1 Engineering Pub Date : 2023-09-14 DOI: 10.1080/0951192x.2023.2257668
Elisa Reboredo, Pedro Espadinha-Cruz
ABSTRACTCurrently, organizations in the manufacturing sector are exposed to high levels of competition and constant changes in consumer requirements. To survive in an increasingly competitive environment, it is essential to adopt new technologies to ensure success in a sector that is currently going through the fourth industrial revolution, associated with Industry 4.0 (I4.0). Additive manufacturing (AM) is one of I4.0’s technologies, which can produce products layer by layer, playing a key role in the innovation of business models. In this manner, organizations must integrate AM starting by understanding their maturity level, which allows them to reflect on weaknesses and strengths as well as opportunities for improvement. However, literature is currently lacking maturity models for AM. This paper proposes a maturity model for AM, which aims to help organizations in the manufacturing sector in determining their maturity level regarding the implementation of the AM. The model developed was validated theoretically. Also, a case study was conducted on an automaker, where it was possible to conclude that, despite the analyzed company has a high level of maturity regarding the technological deployment of AM, an AM’s strategy definition is presently missing.KEYWORDS: Additive manufacturing3D printingindustry 4.0maturity model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Fundação para a Ciência e a Tecnologia [UIDB/00667/2020 (UNIDEMI)].
摘要当前,制造业组织面临着高水平的竞争和消费者需求的不断变化。为了在竞争日益激烈的环境中生存,必须采用新技术,以确保在目前正在经历与工业4.0相关的第四次工业革命的行业中取得成功。增材制造(AM)是工业4.0的技术之一,它可以逐层生产产品,在商业模式创新中发挥着关键作用。以这种方式,组织必须从理解他们的成熟度水平开始集成AM,这允许他们反思弱点和优势以及改进的机会。然而,文献目前缺乏AM的成熟度模型。本文提出了AM的成熟度模型,其目的是帮助制造部门的组织确定其关于AM实施的成熟度水平。所建立的模型在理论上得到了验证。此外,对一家汽车制造商进行了案例研究,可以得出这样的结论:尽管所分析的公司在增材制造的技术部署方面具有很高的成熟度,但增材制造的战略定义目前缺失。关键词:增材制造3d打印产业4.0成熟度模型披露声明作者未发现潜在利益冲突。这项工作得到了联合国 技术与发展基金会(unidb /00667/2020 (UNIDEMI))的支持。
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引用次数: 0
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International Journal of Computer Integrated Manufacturing
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