【标题】High-temperature tribological properties of coatings repaired by laser additive manufacturing on railway wheel tread damage
【参考中译】车轮踏面损伤激光添加剂修复涂层的高温摩擦学性能
【类型】 期刊
【关键词】 Wheel material; Laser additive manufacturing; Partial repair coating; Self-fluxing powder; High-temperature wear
【参考中译】 车轮材料；激光添加剂制造；局部修复涂层；自熔性粉末；高温磨损
【作者】 Qian Xiao; Shiyu Li; Wenbin Yang; Chunhui Yang; Daoyun Chen; Haohao Ding; Wenjian Wang
【摘要】 Heavy-duty train wheels can easily cause local damage. Laser additive manufacturing technology has obvious advantages in repairing local damage to train wheels. The high temperature sliding condition caused by tread braking is a severe test for the coating of damaged wheels repaired by laser additive. In this study, three self-fluxing alloy powders, Fe-, Ni-, and Co-based, which are the most widely used in laser cladding, were selected as repair materials. The sliding friction wear performance at high temperatures was evaluated with an HT-1000 ball-disk wear tester, and the wear mechanisms of the repair area, substrate, and counterpart surfaces of the specimens were analyzed. The results show that the microstructure of the coating after the surface damage of the wheel is repaired by laser additive is mainly composed of dendritic structure and eutectic structure. Compared with the base material, the hardness of Fe-, Ni-, and Co-based alloy cladding coatings is significantly improved. At the same time, the wear resistance of the repaired wheel steel samples was improved to different degrees. The wear mechanism of the repaired area of the Fe-based alloy sample is mainly adhesive wear, and the wear mechanism of the repaired area of the Ni-based alloy sample is adhesive wear and oxidation wear. However, the surface of the wear scar of the Co-based alloy repaired sample is relatively smooth, the damage is slight, and the friction reduction effect is the best.
【参考中译】 重型火车车轮很容易造成局部损坏。激光附加制造技术在修复火车车轮局部损伤方面具有明显的优势。由于踏面制动引起的高温滑动条件对激光修补剂修复破损车轮的涂层提出了严峻的考验。本研究选择了激光熔覆中应用最广泛的三种自熔性合金粉末Fe、Ni和Co作为修复材料。利用HT-1000型球盘磨损试验机对其高温滑动摩擦磨损性能进行了评价，并对试件的修复区、衬底和副表面的磨损机理进行了分析。结果表明，用激光添加剂修复车轮表面损伤后，涂层组织主要由枝晶组织和共晶组织组成。与母材相比，Fe、Ni、Co基合金熔覆层的硬度显著提高。同时，修复后的车轮钢试件的耐磨性也有不同程度的提高。铁基合金试件修复区的磨损机制主要为粘着磨损，镍基合金试件的修复区磨损机制主要为粘着磨损和氧化磨损。而钴基合金修复后的试件磨痕表面较为光滑，损伤轻微，减摩效果最好。
【来源】 Wear 2023, vol.520/521
【入库时间】 2023/6/29
【标题】A framework driven by physics-guided machine learning for process-structure-property causal analytics in additive manufacturing
【参考中译】物理引导的机器学习驱动的加法制造过程-结构-性能因果分析框架
【关键词】 Additive manufacturing; Data-Driven; Machine learning; Physics-Informed; Process-Structure-Property
【参考中译】 加性制造；数据驱动；机器学习；物理信息；过程-结构-特性
【作者】 Hyunwoong Ko; Yan Lu; Zhuo Yang; Ndeye Y. Ndiaye; Paul Witherell
【摘要】 Data analytics with Machine Learning (ML) using physics knowledge and big data offers high potential to continuously transform raw data to newfound knowledge of Process-Structure-Property (PSP) causal relationships. In Additive Manufacturing (AM), however, realizing the potential is still limited largely due to the lack of a systematic way to learn the PSP relationships for various AM processes. To address the limitation, this paper proposes a novel framework driven by physics-guided ML, which consists of three tiers: (1) knowledge of predictive PSP models and physics, (2) PSP features of interest, and (3) raw AM data. The framework defines a PSP-learning process with two sub-processes. The first uses a knowledge-graph-guided top-down approach to generate the requirements for predictive analytics and data acquisition. The second uses a data-driven bottom-up approach to construct and model new PSP knowledge. Together, these processes connect the proposed framework to decision-making and control activities and physical and virtual AM systems, respectively. The paper includes a case study based on Laser Powder Bed Fusion processes including AM Metrology Testbed at the National Institute of Standards and Technology (NIST). The case study introduces predictive ML models and PSP knowledge extracted from the models. We also demonstrate the framework using an ML-Integrated Knowledge Extraction module called MIKE in NIST's collaborative AM Material Database. The framework newly enables a systematic physics-guided data-driven approach for PSP in AM that can couple physics knowledge with the versatility of data-driven ML models. Using the approach, the framework continuously updates the models (1) to improve the understanding of dynamically generated AM data and (2) to link sub-models into coupled PSP models. Based on the improved understanding, the framework also facilitates decision-making and control activities for AM at multiple scales.
【参考中译】 使用物理知识和大数据的机器学习(ML)的数据分析提供了将原始数据持续转换为新发现的过程-结构-属性(PSP)因果关系知识的高潜力。然而，在添加制造(AM)中，实现潜力仍然有限，这在很大程度上是因为缺乏一种系统的方法来学习各种AM过程的PSP关系。针对这一局限性，本文提出了一种由物理制导的ML驱动的新框架，该框架由三层组成：(1)预测PSP模型和物理知识，(2)感兴趣的PSP特征，(3)原始AM数据。该框架定义了一个PSP学习过程，包括两个子过程。第一种方法使用知识图谱引导的自上而下的方法来生成预测性分析和数据获取的需求。第二种方法使用数据驱动的自下而上的方法来构建和建模新的PSP知识。这些流程一起将建议的框架分别连接到决策和控制活动以及物理和虚拟AM系统。本文包括一个基于激光粉床融合过程的案例研究，其中包括美国国家标准与技术研究所(NIST)的AM计量试验台。案例研究介绍了预测最大似然模型和从模型中提取的PSP知识。我们还使用了美国国家标准研究院S协作型AM素材数据库中的ML集成知识提取模块Mike演示了该框架。该框架为AM中的PSP提供了一种系统的物理制导的数据驱动方法，可以将物理知识与数据驱动的ML模型的通用性结合起来。使用该方法，框架不断更新模型(1)以提高对动态生成的AM数据的理解，(2)将子模型链接到耦合的PSP模型。基于改进的理解，该框架还为AM在多个尺度上的决策和控制活动提供了便利。
【来源】 Journal of Manufacturing Systems 2023, vol.67
【标题】Toward a smart wire arc additive manufacturing system: A review on current developments and a framework of digital twin
【参考中译】迈向智能焊丝添加剂制造系统：现状回顾和数字孪生系统框架
【关键词】 WAAM; Additive manufacturing; Digital twin; Process planning; Monitoring; Control; Simulation
【参考中译】 WAAM；添加制造；数字孪生；工艺规划；监测；控制；仿真
【作者】 Haochen Mu; Fengyang He; Lei Yuan; Philip Commins; Hongmin Wang; Zengxi Pan
【摘要】 In recent years, Wire Arc Additive Manufacturing (WAAM) has attracted increasing scientific attention. With the rise of Industry 4.0 and smart manufacturing, Digital Twin (DT) has become an emerging technology that is finding increased acceptance in Additive Manufacturing (AM) processes. This paper aims to provide a systematic review of current developments of DT in AM processes and then derive a suitable DT for the WAAM system. Firstly, DT developments in AM processes are introduced from supervisory, control, and predictive aspects. This provides a reference and inspiration for designing process DTs by reviewing their structures, algorithms, and methodologies. Secondly, the current research on process planning, monitoring, modeling, online control, and simulation in WAAM is reviewed. Particular attention is given to intelligent algorithms, such as machine learning. Thirdly, the challenges to building a WAAM-DT are introduced step-by-step. Finally, the paper concludes by proposing a framework of WAAM-DT as a hybrid and intelligent solution for monitoring, modeling, control, and simulation.
【参考中译】 近年来，电弧添加剂制造(WAAM)引起了越来越多的科学关注。随着工业4.0和智能制造的兴起，数字孪生(DT)已成为一项新兴技术，正在被越来越多的加法制造(AM)工艺所接受。本文的目的是系统地回顾AM过程中DT的发展现状，从而得出一种适合于WAAM系统的DT。首先，从监督、控制和预测三个方面介绍了动态链接法在AM过程中的发展。这为通过回顾过程DTD的结构、算法和方法来设计过程DT提供了参考和启发。其次，对广域空管中工艺规划、监控、建模、在线控制和仿真等方面的研究现状进行了综述。特别关注智能算法，如机器学习。第三，逐步介绍了建设WAAM-DT所面临的挑战。最后，本文提出了WAAM-DT的框架，作为一种用于监测、建模、控制和仿真的混合智能解决方案。