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Modeling and Experimental Validation of CFRP-Metal Joints Utilizing 3D Additively Manufactured Anchors 基于3D增材制造锚的cfrp -金属连接建模与实验验证
3区 工程技术 Q1 Engineering Pub Date : 2023-08-29 DOI: 10.1115/1.4063110
Giorgio De Pasquale, Antonio Coluccia
Abstract The joining techniques between carbon fiber reinforced polymer (CFRP) and metal are of great importance in many areas of structural mechanics where the optimization of weight, rigidity, and strength is a necessity (such as aeronautics, vehicles, energy generation, and biomechanics). As a result, several types of metal–composite joints have been manufactured using different methods, with the 3D metal anchor solution attracting significant attention. This study evaluates different anchor geometries applied to single lap joints through preliminary finite element method (FEM) simulations and experimental validation on joints between CFRP and Inconel 625 produced via a laser beam powder bed fusion (LB-PBF) additive process. The models proposed increase in complexity. The homogenization process is employed to determine the equivalent properties of the joint region that is occupied by metal anchors and CFRP. The model also supports topology parametrization to assess the impact of anchor geometry on structural properties. The study provides experimental validation of joint strength under tensile load for various anchoring surface topologies.
碳纤维增强聚合物(CFRP)与金属之间的连接技术在许多需要优化重量、刚度和强度的结构力学领域(如航空、汽车、能源发电和生物力学)具有重要意义。因此,使用不同的方法制造了几种类型的金属复合接头,其中3D金属锚解决方案引起了人们的广泛关注。本研究通过初步的有限元模拟(FEM)和实验验证,对CFRP和Inconel 625之间通过激光粉末床熔合(LB-PBF)添加剂工艺生产的接头进行了评估,评估了应用于单搭接接头的不同锚固几何形状。提出的模型增加了复杂性。采用均质化方法确定了金属锚杆与碳纤维布占据的节点区域的等效性能。该模型还支持拓扑参数化,以评估锚的几何形状对结构特性的影响。该研究为不同锚固面拓扑结构在拉伸荷载作用下的节点强度提供了实验验证。
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引用次数: 1
Turn-taking prediction for human-robot collaborative assembly considering human uncertainty 考虑人为不确定性的人-机器人协同装配转弯预测
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-23 DOI: 10.1115/1.4063231
Wenjun Xu, Siqi Feng, Bitao Yao, Zhenrui Ji, Zhihao Liu
Human-robot collaboration (HRC) combines the repeatability and strength of robots and human's ability of cognition and planning to enable a flexible and efficient production mode. The ideal HRC process is that robots can smoothly assist workers in complex environments. This means that robots need to know the process's turn-taking earlier, adapt to the operating habits of different workers, and make reasonable plans in advance to improve the fluency of HRC. However, many of the current HRC systems ignore the fluent turn-taking between robots and humans, which results in unsatisfactory HRC and affects productivity. Moreover, there are uncertainties in humans as different humans have different operating proficiency, resulting in different operating speeds. This requires the robots to be able to make early predictions of turn-taking even when human is uncertain. Therefore, in this paper, an early turn-taking prediction method in HRC assembly tasks with Izhi neuron model-based spiking neuron network (SNN) is proposed. On this basis, dynamic motion primitives (DMP) are used to establish trajectory templates at different operating speeds. The length of the sequence sent to the SNN network is judged by the matching degree between the observed data and the template, so as to adjust to human uncertainty. The proposed method is verified by the gear assembly case. The results show that our method can shorten the human-robot turn-taking recognition time under human uncertainty.
人机协作(HRC)结合了机器人的可重复性和强度以及人类的认知和规划能力,实现了灵活高效的生产模式。理想的HRC过程是机器人可以在复杂的环境中顺利地帮助工人。这意味着机器人需要更早地了解流程的轮次,适应不同工人的操作习惯,并提前制定合理的计划,以提高HRC的流畅性。然而,目前的许多HRC系统忽视了机器人和人类之间流畅的转弯,这导致了不令人满意的HRC并影响了生产力。此外,由于不同的人有不同的操作熟练度,导致不同的操作速度,因此人类也存在不确定性。这就要求机器人即使在人类不确定的情况下也能对转弯做出早期预测。因此,本文提出了一种基于Izhi神经元模型的尖峰神经元网络(SNN)的HRC装配任务早期转弯预测方法。在此基础上,使用动态运动基元(DMP)建立不同操作速度下的轨迹模板。发送到SNN网络的序列长度是根据观测数据与模板的匹配程度来判断的,以适应人类的不确定性。通过齿轮装配实例验证了该方法的有效性。结果表明,该方法能够在人类不确定的情况下缩短人机转弯识别时间。
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引用次数: 0
Exploring the effects of perceived complexity criteria on performance measures of human-robot collaborative assembly 探索感知复杂性标准对人机协同装配性能度量的影响
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-23 DOI: 10.1115/1.4063232
E. Verna, Stefano Puttero, G. Genta, M. Galetto
The use of Human-Robot Collaboration (HRC) in assembly tasks has gained increasing attention in recent years as it allows for the combination of the flexibility and dexterity of human operators with the repeatability of robots, thus meeting the demands of the current market. However, the performance of these collaborative systems is known to be influenced by various factors, including the complexity perceived by operators. This study aimed to investigate the effects of perceived complexity on the performance measures of HRC assembly. An experimental campaign was conducted in which a sample of skilled operators was instructed to perform six different variants of electronic boards and express a complexity assessment based on a set of assembly complexity criteria. Performance measures such as assembly time, in-process defects, quality control times, offline defects, total defects, and human stress response were monitored. The results of the study showed that the perceived complexity had a significant effect on assembly time, in-process and total defects, and human stress response, while no significant effect was found for offline defects and quality control times. Specifically, product variants perceived as more complex resulted in lower performance measures compared to products perceived as less complex. These findings hold important implications for the design and implementation of HRC assembly systems and suggest that perceived complexity should be taken into consideration to increase HRC performance.
近年来,在装配任务中使用人机协作(HRC)获得了越来越多的关注,因为它允许将人类操作员的灵活性和灵巧性与机器人的可重复性相结合,从而满足当前市场的需求。然而,众所周知,这些协作系统的性能受到各种因素的影响,包括操作员感知到的复杂性。本研究旨在探讨感知复杂性对HRC装配性能指标的影响。在一项实验活动中,一组熟练的操作人员被指示执行六种不同的电子电路板变体,并根据一套装配复杂性标准表达复杂性评估。性能度量,如装配时间,过程中缺陷,质量控制时间,离线缺陷,总缺陷,和人的压力反应被监控。研究结果表明,感知复杂性对装配时间、过程中缺陷和总缺陷以及人的应激反应有显著影响,而对离线缺陷和质量控制时间没有显著影响。具体来说,与被认为不那么复杂的产品相比,被认为更复杂的产品变体导致了更低的性能度量。这些发现对HRC装配系统的设计和实施具有重要意义,并建议应考虑感知复杂性以提高HRC性能。
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引用次数: 0
Li-ion Battery Electrode Manufacturing Control System in Winding Process: Tension Control in Industrial Complex Roll-to-roll Winding Machine via SMC-FLC Hybrid Control Method 卷绕过程中锂离子电池电极制造控制系统:基于SMC-FLC混合控制方法的工业复杂卷卷机张力控制
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-23 DOI: 10.1115/1.4063233
Haozhen Chen, J. Ni
This article introduces a new control method for web tension control on a complex roll-to-roll winding machine used in battery production. Traditional web tension control method cannot perform well enough under high winding speed: the parameter tuning process is time-consuming, and the disturbance rejection performance is not satisfying, and the control performance is not stable. A hybrid control method is proposed, and it is easy to be implemented on common programming platform for commercial winding machines with an easy tuning process, while providing superior control performance to traditional control method. The system modeling used in the control method is much simpler than the modeling in most of tension control research, providing better feasibility for industrial application.
本文介绍了一种新的控制方法,用于电池生产中复杂的卷对卷卷绕机的卷筒纸张力控制。传统的卷筒纸张力控制方法在高卷绕速度下不能很好地执行:参数调整过程耗时,抗扰性能不令人满意,控制性能不稳定。提出了一种混合控制方法,该方法易于在商用绕线机的通用编程平台上实现,调整过程简单,同时提供了优于传统控制方法的控制性能。该控制方法中使用的系统建模比大多数张力控制研究中的建模简单得多,为工业应用提供了更好的可行性。
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引用次数: 0
SEQUENTIAL MODELING AND KNOWLEDGE SOURCE INTEGRATION FOR IDENTIFYING THE STRUCTURE OF A BAYESIAN NETWORK FOR MULTISTAGE PROCESS MONITORING AND DIAGNOSIS 用于识别多级过程监测和诊断的贝叶斯网络结构的序列建模和知识源集成
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-23 DOI: 10.1115/1.4063235
Partha Protim Mondal, Placid Ferreira, S. Kapoor, Patrick N. Bless
As a popular applied artificial intelligence tool, Bayesian networks are increasingly being used to model multistage manufacturing processes for fault diagnosis purposes. However, the major issue limiting the practical adoption of Bayesian networks is the difficulty of learning the network structure for large multistage processes. Traditionally, Bayesian network structures are learned either with the help of domain experts or by utilizing data-driven structure learning algorithms through trial and error. Both approaches have their limitations. On one hand, expert-driven approach is costly, time-consuming, cumbersome for large networks, susceptible to errors in assessing probabilities and on the other hand, data-driven approaches suffer from noise, biases, inadequacy of training data and often fail to capture the physical causal structure of the data. Therefore, in this paper, we propose a Bayesian network structure learning approach where popular manufacturing knowledge sources like the Failure Mode and Effect Analysis (FMEA) and hierarchical variable ordering are used as structural priors to guide the data-driven structure learning process. In addition, to introduce modularity and flexibility into the learning process, we present a sequential modeling approach for structure learning so that large multistage networks can be learned stage by stage progressively. Furthermore, through simulation studies, we compare and analyze the performance of the knowledge source based structurally-biased networks in the context of multistage process fault diagnosis.
作为一种广泛应用的人工智能工具,贝叶斯网络越来越多地用于多阶段制造过程的建模和故障诊断。然而,限制贝叶斯网络实际应用的主要问题是学习大型多阶段过程的网络结构的困难。传统上,贝叶斯网络结构要么是在领域专家的帮助下学习,要么是通过反复试验利用数据驱动的结构学习算法来学习。这两种方法都有其局限性。一方面,专家驱动的方法对于大型网络来说是昂贵、耗时、繁琐的,在评估概率时容易出错;另一方面,数据驱动的方法受到噪声、偏差、训练数据不足的影响,并且经常无法捕获数据的物理因果结构。因此,在本文中,我们提出了一种贝叶斯网络结构学习方法,该方法使用失效模式和影响分析(FMEA)和分层变量排序等流行的制造业知识来源作为结构先验来指导数据驱动的结构学习过程。此外,为了在学习过程中引入模块化和灵活性,我们提出了一种用于结构学习的顺序建模方法,以便大型多阶段网络可以逐步学习。此外,通过仿真研究,比较分析了基于知识源的结构偏差网络在多阶段过程故障诊断中的性能。
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引用次数: 0
Recurrence Network based 3D Geometry Representation Learning for Quality Control in Additive Manufacturing of Metamaterials 基于递归网络的三维几何表示学习在超材料增材制造质量控制中的应用
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-23 DOI: 10.1115/1.4063236
Yujing Yang, Chen Kan
Metamaterials are designed with intrinsic geometries to deliver unique properties, and recent years have witnessed an upsurge in leveraging additive manufacturing (AM) to produce metamaterials. However, the frequent occurrence of geometric defects in AM poses a critical obstacle to realizing the desired properties of fabricated metamaterials. Advances in three-dimensional (3D) scanning technologies enable the capture of fine-grained 3D geometric patterns, thereby providing a great opportunity for detecting geometric defects in fabricated metamaterials for property-oriented quality assurance. Realizing the full potential of 3D scanning-based quality control hinges largely on devising effective approaches to process scanned point clouds and extract geometric-pertinent information. In this study, a novel framework is developed to integrate recurrence network-based 3D geometry profiling with deep one-class learning for geometric defect detection in AM of metamaterials. First, we extend existing recurrence network models that focus on image data to representing 3D point clouds, by designing a new mechanism that characterizes points' geometric pattern affinities and spatial proximities. Then, a one-class graph neural network (GNN) approach is tailored to uncover topological variations of the recurrence network and detect anomalies that associated with geometric defects in the fabricated metamaterial. The developed methodology is evaluated through comprehensive simulated and real-world case studies. Experimental results have highlighted the efficacy of the developed methodology in identifying both global and local geometric defects in AM-fabricated metamaterials.
超材料的设计具有固有的几何形状,以提供独特的性能,近年来,利用增材制造(AM)生产超材料的热潮正在兴起。然而,在增材制造中,几何缺陷的频繁出现对实现所制造的超材料的预期性能造成了严重的障碍。三维(3D)扫描技术的进步能够捕获细粒度的3D几何图案,从而为检测制造的超材料中的几何缺陷提供了很大的机会,以保证性能导向的质量。实现基于3D扫描的质量控制的全部潜力在很大程度上取决于设计有效的方法来处理扫描点云和提取几何相关信息。在本研究中,开发了一种新的框架,将基于递归网络的三维几何轮廓与深度单类学习相结合,用于超材料增材制造中的几何缺陷检测。首先,我们通过设计一种新的机制来表征点的几何模式亲和性和空间接近性,将现有的专注于图像数据的递归网络模型扩展到表示三维点云。然后,定制了一类图神经网络(GNN)方法来揭示递归网络的拓扑变化,并检测与制造的超材料中的几何缺陷相关的异常。开发的方法是通过全面的模拟和现实世界的案例研究进行评估。实验结果强调了开发的方法在识别am制造的超材料的全局和局部几何缺陷方面的有效性。
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引用次数: 0
Digital Twinning and Optimization of Manufacturing Process Flows 制造工艺流程的数字化结对与优化
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-23 DOI: 10.1115/1.4063234
Hankang Lee, Hui Yang
The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models - job flow graph and AGV traveling graph - to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories.
工业4.0的新浪潮正在将制造工厂转变为数据丰富的环境。这提供了一个前所未有的机会,将从实体工厂收集的大量传感数据输入到网络空间的数字孪生(DT)建设中。然而,在充分利用DT技术来提高中小型制造工厂的智能和自主水平方面,人们做得很少。事实上,只有一小部分中小型制造商(smm)考虑实施DT技术。目前迫切需要利用数据分析和模拟技术在先进制造领域的全部潜力。因此,本文提出了用于制造工艺流程仿真优化的DT模型的设计和开发。首先,我们开发了一个多智能体仿真模型,该模型描述了交互制造事物网络之间的非线性和随机动力学,包括客户、机器、自动导引车(agv)、队列和作业。其次,我们提出了一种统计元建模方法来设计顺序计算机实验,以优化不确定条件下AGV的利用率。第三,构建了两个新的图形模型——作业流图和AGV行进图,用于跟踪和监控制造车间的实时性能。提出的仿真支持的DT方法进行了评估和验证,并通过实验研究来表示现实世界的制造工厂。实验结果表明,所提出的方法有效地将制造车间转变为支持dt的新一代智能工厂。
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引用次数: 1
An incremental self-excitation method for effectively identifying low-frequency frequency response function of milling robots 一种有效识别铣削机器人低频频响函数的增量自激励方法
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-10 DOI: 10.1115/1.4063155
Jiawei Wu, X. Tang, Shihao Xin, Chenyang Wang, F. Peng, R. Yan, Xinyong Mao
Robotic machining efficiency and accuracy are limited by milling vibration and chatter. Robot dynamic characteristics are strongly dependent on the poses; therefore, acquiring the robot dynamic characteristics in any pose is important for vibration suppression and chatter avoidance in large-range machining. This paper proposes an incremental self-excitation method for effectively identifying low-frequency frequency response functions (FRF) of milling robots. A fully knowable and controllable excitation increment can be achieved by attaching a mass block at the robot end, which overcomes the shortcoming of the traditional self-excitation methods that cannot obtain the dynamic compliance magnitude. With appropriate trajectory programming, this method can be carried out automatically in the poses of interest without manual operations. First, the impulse (moment) of the incremental self-excitation is modeled based on momentum theorem, and the association model of the pulse response increment with the incremental self-excitation is established. For the problem that the FRF calculation process is sensitive to noise, the incremental self-excitation is assumed to be a Gaussian pulse, and its identification method is provided. Then, the dimensionality requirement for identifying the 9-item (direct and cross) FRFs is reduced using the modal directionality of milling robots, and the corresponding FRF calculation method is proposed. The rationality of the required simplifications and assumptions of this method is verified by experiments and calculations. The experimental results in several robot poses show that the proposed method can effectively identify all the direct and cross FRFs in the low-frequency band.
铣削振动和颤振限制了机器人的加工效率和精度。机器人的动态特性强烈依赖于姿态;因此,获取机器人在任意姿态下的动态特性,对于大范围加工中的振动抑制和颤振避免具有重要意义。针对铣削机器人低频频响函数的识别问题,提出了一种增量自激励方法。通过在机器人末端附加质量块,可以获得完全可知的可控激励增量,克服了传统自激励方法无法获得动态柔度大小的缺点。通过适当的轨迹规划,该方法可以在不需要人工操作的情况下自动完成感兴趣的姿态。首先,基于动量定理对增量自激的脉冲(力矩)进行建模,建立脉冲响应增量与增量自激的关联模型;针对频响计算过程对噪声敏感的问题,将增量自激假设为高斯脉冲,并给出了其识别方法。然后,利用铣削机器人的模态方向性降低识别9项(直接和交叉)频响的维数要求,并提出相应的频响计算方法;通过实验和计算验证了该方法所要求的简化和假设的合理性。在多个机器人姿态下的实验结果表明,该方法能有效地识别出低频段的所有直接频响和交叉频响。
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引用次数: 0
3D PRINTED DIFFRACTION GRATINGS DROP COATED BY DIFFERENT RESINS AND THEIR MECHANISM 三维打印衍射光栅滴涂不同树脂及其机理
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-09 DOI: 10.1115/1.4063137
Junyu Hua, Yujie Shan, Shaocheng Wu, Huachao Mao
3D-printed blocks with drop coating could work as diffraction gratings while the layer stepping serves as the grooves of the gratings. The paper reports 3D-printed diffraction gratings coated with different resins. A collimated laser with a wavelength of 520 nm passed through the gratings and generated diffraction patterns. Optical path differences and surface profiles of the samples were measured to analyze the mechanism of the diffraction phenomenon. The as-printed samples had a grating height of about 8 μm induced by layer stepping, which could not generate clear diffraction patterns because of too large optical path difference. After being coated with different resins on the surfaces, the printed samples generated diffraction patterns. We experimentally showed that the magnitude of optical path differences became close to the wavelength of the laser and that the diffraction phenomenon was mainly caused by the difference in the refractive indices between the as-printed part and the drop-coated part. This novel method enables low-cost 3D printers to fabricate diffractive optical elements for visible light.
采用水滴涂层的3d打印块可以作为衍射光栅,而分层步进则作为光栅的凹槽。这篇论文报道了涂有不同树脂的3d打印衍射光栅。波长为520nm的准直激光穿过光栅,产生衍射图样。测量了样品的光程差和表面轮廓,分析了衍射现象的机理。由于层进导致的光栅高度约为8 μm,光程差太大,无法产生清晰的衍射图样。在表面涂上不同的树脂后,打印的样品产生了衍射图案。实验表明,光程差的大小接近于激光的波长,衍射现象主要是由打印部分和滴涂部分的折射率差异引起的。这种新方法使低成本的3D打印机能够制造可见光的衍射光学元件。
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引用次数: 0
Experimental setup for in-process measurements and analysis of wear-dependent surface topographies 磨损相关表面形貌的过程测量和分析实验装置
IF 4 3区 工程技术 Q1 Engineering Pub Date : 2023-08-08 DOI: 10.1115/1.4063133
Nils Potthoff, J. Liss, P. Wiederkehr
High-strength and corrosion-resistant materials, such as the nickel-based superalloy Inconel 718, are widely used in the energy and aerospace industries. However, machining these materials results in high process forces and significant tool wear. This tool wear has a negative effect on the resulting surface topography. Nevertheless, the accuracy requirements for functional surfaces are extreme high. Simulation systems can be used to design these processes. However, time-consuming and cost-intensive experiments often have to be conducted to develop and parameterize the required models. To overcome this problem, an analogy test setup for in-process measurements of wear-dependent properties was developed, which allows a multi-level evaluation of the process. By combining different measurement techniques, wear-dependent process characteristics can be determined and analyzed and, thus significantly reducing the measurement effort typically required.
高强度和耐腐蚀材料,如镍基高温合金Inconel 718,广泛应用于能源和航空航天行业。然而,加工这些材料会导致高加工力和显著的工具磨损。这种刀具磨损对产生的表面形貌有负面影响。然而,功能表面的精度要求极高。仿真系统可用于设计这些过程。然而,为了开发所需的模型并将其参数化,通常必须进行耗时且成本密集的实验。为了克服这个问题,开发了一种用于磨损相关性能的过程中测量的模拟测试装置,该装置允许对过程进行多层次评估。通过结合不同的测量技术,可以确定和分析与磨损相关的工艺特性,从而显著减少通常需要的测量工作量。
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引用次数: 0
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Journal of Manufacturing Science and Engineering-transactions of The Asme
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