Research Progress on Online Monitoring Technology for Metal Laser Additive Manufacturing
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摘要: 金属激光增材制造技术具备实现复杂构件高效精密成形的核心能力, 目前已经在航空航天、国防、医疗等重要工业领域得到了成功应用, 然而由于难以避免的存在较多的孔隙、裂纹等制造缺陷阻碍了其在高端装备中的更广泛应用. 制造过程的在线监测显得尤为迫切, 而高温、高速等极端的制造环境导致对原位监测与质量控制提出了跨学科挑战. 本论文综述了多维度检测技术体系构建, 梳理关键测试方法、技术的进展: 尤其光学、声学等主流传感技术以及多传感技术结合先进智能算法实现表面缺陷动态识别与内部缺陷特征提取的先进方法, 以及高速同步辐射成像等新兴手段为缺陷萌生、演化机制研究与检测方法探索提供了创新思路. 此外,梳理了基于多源数据融合建立微观熔池行为与宏观几何精度的协同分析框架方法, 以及基于在线监测的质量控制方法等进展. 受限于多源噪声干扰、多物理场信息同步检测效率不足等问题, 缺陷在线检测精度、速率、效率亟待提高. 分析认为: 未来研究需聚焦多传感探测技术与机器学习的深度协同, 继续探索更高效的在线智能检测方法, 以及注重数字孪生驱动的全流程质量预测模型构建, 以期为解决增材制造过程中缺陷监测与成形精度、质量控制的共性问题提供可靠的解决途径.Abstract: Metal laser additive manufacturing technology exhibits the capability for precision forming of complex components in high-end fields such as the aerospace industry. However, defects including thermal accumulation pores and high-stress cracks—caused by variations in power density and cooling rate—pose interdisciplinary challenges to in-situ monitoring and quality control. This paper reviews the establishment of a multi-dimensional detection technology system and systematizes the advancements in key testing methods and technologies. Specifically, mainstream sensing technologies, including optical and acoustic sensing, when integrated with advanced intelligent algorithms, facilitate dynamic identification of surface defects and extraction of internal defect characteristics. Multi-source data fusion further establishes a collaborative analysis framework linking microscopic molten pool behavior to macroscopic geometric accuracy. Additionally, emerging techniques, such as high-speed synchrotron radiation imaging, offer accurate cross-scale online observation tools for investigating the initiation and evolution mechanisms of defects. Current technologies are constrained by challenges such as multi-source noise interference and low synchronization efficiency of multi-physics field data. Future research should focus on the in-depth integration of multi-sensor detection technology with machine learning, explore online intelligent detection approaches, and develop full-process quality prediction models driven by digital twins. This study intends to provide theoretical synthesis and technical pathway analysis to address the common challenges of defect monitoring and forming accuracy control in the additive manufacturing process.
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图 1 金属增材制造系统示意图(廉艳平 et al. 2021). (a)LPBF技术制造系统, (b)LDED技术制造系统
图 2 LDED工艺下的典型缺陷类型. (a)平面度缺陷(Chen et al. 2021a), (b)熔融塌陷缺陷, (c)变形(Qiu et al. 2015), (d)开裂
图 3 不同的相机监测视角示意图. (a) LDED工艺下相机位置固定的欧拉视角(Hao et al. 2020), (b) LDED工艺下相机跟随激光器运动的旁轴拉格朗日视角(Feng et al. 2022b)
图 4 (a)同轴相机的熔池监测装置及熔池几何特征(Da Silva et al. 2023), (b)熔池信息的处理流程: 基于深度学习的熔池分割(Yang et al. 2025)
图 5 基于光学检测的双波段比色测温法获取熔池温度场流程示意图.(a)双波段分光光路(Hao et al. 2020), (b)基于DIC的双波段图像匹配方法(Ma et al. 2022), (c)温度场测量结果(Hao et al. 2020)
图 6 (a)基于温度场相似性(TDSD)的缺陷原位检测(Feng et al. 2022b), (b)基于光电二极管的熔池温度时间序列检测及(c)基于深度学习的激光粉末床熔融中连续缺陷检测(Mao et al. 2023)
图 7 基于声发射传感器的检测设备. (a)用于LPBF工艺的装置及信号(Kononenko et al. 2023), (b)布置于舱室内的麦克风装置(Yamashita et al. 2025)
图 8 麦克风检测设备. (a)用于LDED工艺的麦克风检测示意图(Ansari et al. 2025), (b)用于LPBF的麦克风检测装置(Chen et al. 2023d)
图 9 多传感器检测系统检测流程示意图. (a)用于LDED工艺的多传感器监测系统: 同轴相机和麦克风监测(Ren et al. 2025), (b)(d)基于深度学习方法的熔池图像与声信号的融合,(c)用于LPBF工艺的麦克风、相机监测设备(McKinney et al. 2025)
图 10 在线质量监测与控制. (a)自适应控制算法及温度、冷却速率控制结果(van Blitterswijk et al. 2025), (b)高度偏差的在线监测及缺陷矫正(Feng et al. 2025b)
表 1 工艺特征及监测策略差异
对比项 激光粉末床熔融 (LPBF) 激光定向能量沉积 (LDED) 能量与热物
理特征能量密度与尺度 低功率 (<1 kW), 微细光斑 (70-200 μm), 高能量密度 (10^6-10^7 W/cm2) 高功率 (1-10 kW), 大光斑 (0.6-6 mm), 较低能量密度 (10^4-10^5 W/cm2) 热动力学行为 极快瞬态: 冷却速率极高 (10^5-10^7 K/s), 熔池寿命短, 易发生匙孔失稳 高热惯性: 冷却较慢 (10^2-10^5 K/s), 存在显著的层间热积累与宏观热变形 缺陷类型与机制 主要缺陷形式 微观随机缺陷: 匙孔气孔、球化、微裂纹 宏观几何缺陷: 未熔合、几何形貌塌陷、宏观裂纹与变形 监测信号策略与对应算法 光学视觉 双轨并行策略:
高频微观: 10 kHz 高速相机捕捉微秒级匙孔/飞溅动态 (Ren et al.);
低频层析: 光学层析 (OT) 长曝光记录全层热辐射历史 (Ero et al.)低频宏观监测:
100 Hz 即可满足熔池形貌与轨迹监测; 同轴监测需克服粉末流遮挡与喷嘴限制, 常需配合旁轴红外声学信号 高信噪比结构声: 环境相对安静, 利于捕捉裂纹萌生及匙孔破裂的高频应力波 强噪声环境: 受送粉气流及机械臂运动噪声干扰严重, 信号成分复杂, 需强鲁棒性降噪 
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表 2 数据驱动与物理信息人工智能方法在增材制造过程监测的应用对比
比较维度 纯数据驱动 AI 物理信息约束 AI 核心机制 建立输入 (传感器信号) 与输出 (缺陷/性能) 之间的非线性映射, 依赖统计关联. 将物理偏微分方程 (PDEs) 作为损失函数或特征约束, 引导网络学习符合物理规律的解. 数据需求 高: 依赖大量高质量、带标注的实验或仿真数据 . 低: 物理定律提供先验知识, 可实现小样本甚至无监督学习. 可解释性 弱: 难以解释特征背后的物理意义. 强: 输出满足能量/质量守恒, 可反演物理参数. 泛化能力 弱: 对训练集分布外的数据适应性差, 需迁移学习 . 强: 物理定律具有普适性, 在不同工艺窗口下仍能保持较高精度. 典型应用 快速分类与预测: 缺陷识别、熔池图像分类、实时热历史预测. 场重建与机理分析: 温度场/应力场重建、路径规划、未见工况推演. 代表性工作 (Liu et al. 2025c, Skiadopoulos et al. 2026) (Rashid et al. 2025, Sajadi et al. 2025, Sharma and Guo 2025)
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表 3 不同传感器的时空分辨率差异及应用场景
信号类型 传感器 空间分辨率
(缺陷尺度)时间分辨率
(数据频率)采集/处理延迟 应用场景/
可检测缺陷典型参考文献 光信号 高速相机 高
6–20 μm/pixel中/高
100 Hz – 100 kHz高
受限于海量图像数据处理, 难以实时闭环表面形态、球化、飞溅、铺粉异常 (Hooper 2018, Fischer et al. 2022, Zhang
et al. 2026a)光电二极管 无
单点积分信号, 无空间分辨能力极高
>100 kHz极低
微秒级响应, 适合实时控制熔池强度波动、过程失稳预警 (Mao et al. 2023, Webster et al. 2025) 热信号 基于比色法的相机 高
6–20 μm/pixel中/高
100 Hz – 100 kHz高
受限于数据采集和复杂的计算熔池温度场、熔池典型缺陷: 过熔、欠熔、飞溅 (Feng et al. 2022b, 2025a, b, Wang et al. 2026) 红外热像仪 中/高
像素级, 可识别 >25 μm 孔隙低/中
通常为低速, 少数能够高速采集中
需图像重建与特征提取熔池温度场分布、热积累、未熔合缺陷 (Estalaki et al. 2022) 声信号 麦克风 低
无空间信息, 需阵列定位高
20 Hz – 100 kHz低
数据量小, 处理快保护气流异常、裂纹形成、锁孔坍塌 (Gutknecht et al. 2021, Chen et al. 2023c) 声发射传感器 低
需接触式布置, 定位困难极高
MHz 级低 内部裂纹萌生、相变应力波、微孔隙 (Shevchik et al. 2018, Ito et al. 2021) X射线 同步辐射成像 极高
微米级极高
亚毫秒级极高
仅限离线分析, 数据量巨大熔池深处锁孔振荡、气泡演化机理研究 (Ren et al. 2023, Zhang et al. 2024)
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[1] 廉艳平, 王潘丁, 高杰, 刘继凯, 李取浩, 刘长猛, 贺小帆, 高亮, 李好, 雷红帅, 李会民, 肖登宝, 郭旭, 方岱宁. 2021. 金属增材制造若干关键力学问题研究进展. 力学进展, 51(3): 648-701 (Lian Y P, Wang P D, Gao J, Liu J K, Li Q H, Liu C M, He X F, Gao L, Li H, Lei H S, Li H M, Xiao D B, Guo X, Fang D N. 2021. Fundamental mechanics problems in metal additive manufacturing: A state-of-art review. Advances in Mechanics, 51(3): 648-701).Lian Y P, Wang P D, Gao J, Liu J K, Li Q H, Liu C M, He X F, Gao L, Li H, Lei H S, Li H M, Xiao D B, Guo X, Fang D N. 2021. Fundamental mechanics problems in metal additive manufacturing: A state-of-art review. Advances in Mechanics, 51(3): 648-701 [2] Abdelrahman M, Reutzel E W, Nassar A R, Starr T L. 2017. Flaw detection in powder bed fusion using optical imaging. Additive Manufacturing, 15: 1-11. doi: 10.1016/j.addma.2017.02.001 [3] Afazov S, Okioga A, Holloway A, Denmark W, Triantaphyllou A, Smith, S. -A. ; et al. 2017. A methodology for precision additive manufacturing through compensation. Precision Engineering, 50: 269–274. [4] Akbari M, Kovacevic, R. 2019. Closed loop control of melt pool width in robotized laser powder–directed energy deposition process. The International Journal of Advanced Manufacturing Technology, 104: 2887-2898. doi: 10.1007/s00170-019-04195-y [5] Aminzadeh M, Kurfess, T. R. 2019. Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. Journal of Intelligent Manufacturing, 30: 2505-2523. doi: 10.1007/s10845-018-1412-0 [6] Ansari M J, Roccisano A, Arcondoulis, E. J. G. ; Schulz C, Schläfer T, Hall, C. 2025. Relationship between associated acoustic emission and crack position during directed energy deposition of a metal matrix composite. Journal of Manufacturing Processes, 147: 177–190. [7] Bernauer C, Leitner P, Zapata A, Garkusha P, Grabmann S, Schmoeller M, et al. 2024. Segmentation-based closed-loop layer height control for enhancing stability and dimensional accuracy in wire-based laser metal deposition. Robotics and Computer-Integrated Manufacturing, 86: 102683. doi: 10.1016/j.rcim.2023.102683 [8] Bevans B, Ramalho A, Smoqi Z, Gaikwad A, Santos T G, Rao P, et al. 2023a. Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis. Materials & Design, 225: 111480. doi: 10.1016/j.matdes.2022.111480 [9] Bevans B, BAbdelrahman M, Reutzel E W, Nassar A R, Starr, T. L. 2017. Flaw detection in powder bed fusion using optical imaging. Additive Manufacturing, 15: 1-11. [10] Afazov S, Okioga A, Holloway A, Denmark W, Triantaphyllou A, Smith, S. -A. ; et al. 2017. A methodology for precision additive manufacturing through compensation. Precision Engineering, 50: 269–274. [11] Ahsan M M, Liu Y, Raman S, Siddique, Z. 2025. Digital Twins in Additive Manufacturing: A systematic review. Internet of Things, 33: 101692. doi: 10.1016/j.iot.2025.101692 [12] Akbari M, Kovacevic, R. 2019. Closed loop control of melt pool width in robotized laser powder–directed energy deposition process. The International Journal of Advanced Manufacturing Technology, 104: 2887-2898. doi: 10.1007/s00170-019-04195-y [13] Aminzadeh M, Kurfess, T. R. 2019. Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. Journal of Intelligent Manufacturing, 30: 2505-2523. doi: 10.1007/s10845-018-1412-0 [14] Ansari M J, Roccisano A, Arcondoulis, E. J. G. ; Schulz C, Schläfer T, Hall, C. 2025. Relationship between associated acoustic emission and crack position during directed energy deposition of a metal matrix composite. Journal of Manufacturing Processes, 147: 177–190. [15] Bernauer C, Leitner P, Zapata A, Garkusha P, Grabmann S, Schmoeller M, et al. 2024. Segmentation-based closed-loop layer height control for enhancing stability and dimensional accuracy in wire-based laser metal deposition. Robotics and Computer-Integrated Manufacturing, 86: 102683. doi: 10.1016/j.rcim.2023.102683 [16] Bevans B, Ramalho A, Smoqi Z, Gaikwad A, Santos T G, Rao P, et al. 2023a. Monitoring and flaw detection during wire-based directed energy deposition using in-situ acoustic sensing and wavelet graph signal analysis. Materials & Design, 225: 111480. doi: 10.1016/j.matdes.2022.111480 [17] Bevans B, Barrett C, Spears T, Gaikwad A, Riensche A, Smoqi Z, et al. 2023b. Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing. Virtual and Physical Prototyping. [18] Bevans B D, Carrington A, Riensche A, Tenequer A, Barrett C, Halliday, H. (Scott); et al. 2024. Digital twins for rapid in-situ qualification of part quality in laser powder bed fusion additive manufacturing. Additive Manufacturing, 93: 104415. doi: 10.1016/j.addma.2024.104415 [19] Bi G, Sun C N, Gasser, A. 2013. Study on influential factors for process monitoring and control in laser aided additive manufacturing. Journal of Materials Processing Technology, 213: 463-468. doi: 10.1016/j.jmatprotec.2012.10.006 [20] Bi G, Gasser A, Wissenbach K, Drenker A, Poprawe, R. 2006. Identification and qualification of temperature signal for monitoring and control in laser cladding. Optics and Lasers in Engineering, 44: 1348-1359. doi: 10.1016/j.optlaseng.2006.01.009 [21] Bi G, Schürmann B, Gasser A, Wissenbach K, Poprawe, R. 2007. Development and qualification of a novel laser-cladding head with integrated sensors. International Journal of Machine Tools and Manufacture, 47: 555-561. doi: 10.1016/j.ijmachtools.2006.05.010 [22] van Blitterswijk R H, Botelho L A, Khajepour, A. 2025. Real-time multivariable control of directed energy deposition via adaptive model predictive control. Additive Manufacturing, 111: 104941. doi: 10.1016/j.addma.2025.104941 [23] Chen L, Yao X, Moon, S. K. 2022a. In-situ acoustic monitoring of direct energy deposition process with deep learning-assisted signal denoising. Materials Today: Proceedings, 70: 136-142. doi: 10.1016/j.matpr.2022.09.008 [24] Chen L, Yao , Xiling; Xu , Peng; Moon , Seung Ki; and Bi, G. 2021a. Rapid surface defect identification for additive manufacturing with in-situ point cloud processing and machine learning. Virtual and Physical Prototyping, 16: 50–67. [25] Chen L, Yao X, Chew Y, Weng F, Moon S K, Bi, G. 2020. Data-Driven Adaptive Control for Laser-Based Additive Manufacturing with Automatic Controller Tuning. Applied Sciences, 10: 7967. doi: 10.3390/app10227967 [26] Chen L, Bi G, Yao X, Tan C, Su J, Ng, N. P. H. ; et al. 2023a. Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition. Robotics and Computer-Integrated Manufacturing, 84: 102581. [27] Chen L, Yao X, Tan C, He W, Su J, Weng F, et al. 2023b. In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning. Additive Manufacturing, 69: 103547. doi: 10.1016/j.addma.2023.103547 [28] Chen L, Bi G, Yao X, Tan C, Su J, Ng, N. P. H. ; et al. 2023c. Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition. Robotics and Computer-Integrated Manufacturing, 84: 102581. [29] Chen L, Bi G, Yao X, Su J, Tan C, Feng W, et al. 2024. In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: A critical review. Journal of Manufacturing Systems, 74: 527-574. doi: 10.1016/j.jmsy.2024.04.013 [30] Chen M, Lu Y, Wang Z, Lan H, Sun G, Ni, Z. 2021b. Melt pool evolution on inclined NV E690 steel plates during laser direct metal deposition. Optics & Laser Technology, 136: 106745. doi: 10.1016/j.optlastec.2020.106745 [31] Chen, M. -Z. ; Lu, M. -C. ; Wang, P. -N. ; Chiou, S. -J. 2022b. Experimental study of quality monitoring system integrated with a microphone array in laser microlap welding. The International Journal of Advanced Manufacturing Technology, 121: 2305–2316. [32] Chen R, Xue H, Zhong Q, Zhang C, Li S, Geng C, et al. 2025a. AM-FEMU: An optimization method for additive manufacturing simulation parameters based on finite element model updating, utilizing three-dimensional deformation and melt pool temperature fields. Thin-Walled Structures, 209: 112962. doi: 10.1016/j.tws.2025.112962 [33] Chen, Y. -P. ; Karkaria V, Tsai, Y. -K. ; Rolark F, Quispe D, Gao R X, et al. 2025b. Real-time decision-making for Digital Twin in additive manufacturing with Model Predictive Control using time-series deep neural networks. Journal of Manufacturing Systems, 80: 412–424. [34] Chen Z, Wang D, Zhang, Y. 2023d. Microphone signal specialities in laser powder bed fusion: single-track scan and multi-track scan. Journal of Materials Research and Technology, 24: 1344-1362. doi: 10.1016/j.jmrt.2023.03.091 [35] Criales L E, Arısoy Y M, Lane B, Moylan S, Donmez A, Özel, T. 2017. Laser powder bed fusion of nickel alloy, 625: Experimental investigations of effects of process parameters on melt pool size and shape with spatter analysis. International Journal of Machine Tools and Manufacture, 121: 22-36. doi: 10.1016/j.ijmachtools.2017.03.004 [36] Da Silva A, Frostevarg J, Kaplan, A. F. H. 2023. Melt pool monitoring and process optimisation of directed energy deposition via coaxial thermal imaging. Journal of Manufacturing Processes, 107: 126-133. doi: 10.1016/j.jmapro.2023.10.021 [37] Ding D, Zhao Z, Huang R, Dai C, Zhang X, Xu T, et al. 2021. Error Modeling and Path Planning for Freeform Surfaces by Laser Triangulation On-Machine Measurement. IEEE Transactions on Instrumentation and Measurement, 70: 1-11. doi: 10.1109/tim.2021.3063751 [38] Drissi-Daoudi R, Pandiyan , Vigneashwara; Logé , Roland; Shevchik , Sergey; Masinelli , Giulio; Ghasemi-Tabasi , Hossein; et al. 2022. Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning. Virtual and Physical Prototyping, 17: 181–204. [39] Du Y, Mukherjee T, Li R, Hou Z, Dutta S, Arnold C B, et al. 2026. A review of deep learning in metal additive manufacturing: Impact on process, structure, and properties. Progress in Materials Science, 157: 101587. doi: 10.1016/j.pmatsci.2025.101587 [40] Ero O, Taherkhani K, Hemmati Y, Toyserkani, E. 2024. An integrated fuzzy logic and machine learning platform for porosity detection using optical tomography imaging during laser powder bed fusion. International Journal of Extreme Manufacturing, 6: 065601. doi: 10.1088/2631-7990/ad65cd [41] Esmaeilizadeh R, Keshavarzkermani A, Ali U, Behravesh B, Bonakdar A, Jahed H, et al. 2021. On the effect of laser powder-bed fusion process parameters on quasi-static and fatigue behaviour of Hastelloy X: A microstructure/defect interaction study. Additive Manufacturing, 38: 101805. doi: 10.1016/j.addma.2020.101805 [42] Estalaki S M, Lough C S, Landers R G, Kinzel E C, Luo, T. 2022. Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning. Additive Manufacturing, 58: 103008. doi: 10.1016/j.addma.2022.103008 [43] Faegh M, Ghungrad S, Haghighi, A. 2025. A physics-informed neural network framework for decomposition and path planning in multi-laser additive manufacturing. Manufacturing Letters, 44: 1129-1138. doi: 10.1016/j.mfglet.2025.06.133 [44] Feng S C, Lu Y, Jones A T, Yang, Z. 2022a. Additive Manufacturing In Situ and Ex Situ Geometric Data Registration. Journal of Computing and Information Science in Engineering 22. [45] Feng W, Luo Z, Wang H, Liu Z, Xie, H. 2025a. Online multi-physics field measurement of melt pool temperature and flow fields during metal additive manufacturing. Optics and Lasers in Engineering, 186: 108806. doi: 10.1016/j.optlaseng.2024.108806 [46] Feng W, Mao Z, Yang Y, Ma H, Zhao K, Qi C, et al. 2022b. Online defect detection method and system based on similarity of the temperature field in the melt pool. Additive Manufacturing, 54: 102760. doi: 10.1016/j.addma.2022.102760 [47] Feng W, Mao Z, Ma H, Zhang H, Zhao Y, Zhao K, et al. 2025b. Online in situ detection of deposited height deviation during additive manufacturing. Optics & Laser Technology, 182: 112175. doi: 10.1016/j.optlastec.2024.112175 [48] Fischer F G, Zimmermann M G, Praetzsch N, Knaak, C. 2022. Monitoring of the powder bed quality in metal additive manufacturing using deep transfer learning. Materials & Design, 222: 111029. doi: 10.1016/j.matdes.2022.111029 [49] Freeman, F. S. H. B. ; Thomas B, Chechik L, Todd, I. 2022. Multi-faceted monitoring of powder flow rate variability in directed energy deposition. Additive Manufacturing Letters, 2: 100024. [50] Fu Y, Downey, A. R. J. ; Yuan L, Zhang T, Pratt A, Balogun, Y. 2022. Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review. Journal of Manufacturing Processes, 75: 693–710. [51] Gaikwad A, Williams R J, de Winton H, Bevans B D, Smoqi Z, Rao P, et al. 2022. Multi phenomena melt pool sensor data fusion for enhanced process monitoring of laser powder bed fusion additive manufacturing. Materials & Design, 221: 110919. doi: 10.1016/j.matdes.2022.110919 [52] Gaja H, Liou, F. 2017. Defects monitoring of laser metal deposition using acoustic emission sensor. The International Journal of Advanced Manufacturing Technology, 90: 561-574. doi: 10.1007/s00170-016-9366-x [53] Geng C, Zhang Z, Xie H, Liu, F. 2024. Effect of laser cladding layers on microstructure and mechanical properties of NiCoCrAlY bond coat. Materials Today Communications, 41: 110930. doi: 10.1016/j.mtcomm.2024.110930 [54] Geng C, Shi W, Liu Z, Xie H, He, W. 2022. Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography. Applied Sciences, 12: 5665. doi: 10.3390/app12115665 [55] Gharbi M, Peyre P, Gorny C, Carin M, Morville S, Le Masson P, et al. 2014. Influence of a pulsed laser regime on surface finish induced by the direct metal deposition process on a Ti64 alloy. Journal of Materials Processing Technology, 214: 485-495. doi: 10.1016/j.jmatprotec.2013.10.004 [56] Ghayoomi Mohammadi M, Mahmoud D, Elbestawi, M. 2021. On the application of machine learning for defect detection in L-PBF additive manufacturing. Optics & Laser Technology, 143: 107338. doi: 10.1016/j.optlastec.2021.107338 [57] Gómez Muñoz C Q, Arcos Jimenez A, García Marquez F P, Kogia M, Cheng L, Mohimi A, et al. 2018. Cracks and welds detection approach in solar receiver tubes employing electromagnetic acoustic transducers. Structural Health Monitoring, 17: 1046-1055. doi: 10.1177/1475921717734501 [58] Gonzalez-Val C, Pallas A, Panadeiro V, Rodriguez, A. 2020. A convolutional approach to quality monitoring for laser manufacturing. Journal of Intelligent Manufacturing, 31: 789-795. doi: 10.1007/s10845-019-01495-8 [59] Gu D, Shi X, Poprawe R, Bourell D L, Setchi R, Zhu, J. 2021. Material-structure-performance integrated laser-metal additive manufacturing. Science, 372: eabg1487. doi: 10.1126/science.abg1487 [60] Guo, W. “Grace”; Tian Q, Guo S, Guo, Y. 2020. A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition. CIRP Annals, 69: 205-208. doi: 10.1016/j.cirp.2020.04.049 [61] Gutknecht K, Cloots M, Sommerhuber R, Wegener, K. 2021. Mutual comparison of acoustic, pyrometric and thermographic laser powder bed fusion monitoring. Materials & Design, 210: 110036. doi: 10.1016/j.matdes.2021.110036 [62] Hao C, Liu Z, Xie H, Zhao K, Liu, S. 2020. Real-time measurement method of melt pool temperature in the directed energy deposition process. Applied Thermal Engineering, 177: 115475. doi: 10.1016/j.applthermaleng.2020.115475 [63] Hauser T, Reisch R T, Kamps T, Kaplan, A. F. H. ; Volpp, J. 2022. Acoustic emissions in directed energy deposition processes. The International Journal of Advanced Manufacturing Technology, 119: 3517–3532. [64] He W, Shi W, Li J, Xie, H. 2019. In-situ monitoring and deformation characterization by optical techniques; part I: Laser-aided direct metal deposition for additive manufacturing. Optics and Lasers in Engineering, 122: 74-88. doi: 10.1016/j.optlaseng.2019.05.020 [65] Hooper, P. A. 2018. Melt pool temperature and cooling rates in laser powder bed fusion. Additive Manufacturing, 22: 548-559. doi: 10.1016/j.addma.2018.05.032 [66] Hossain M S, Taheri, H. 2021. In-situ process monitoring for metal additive manufacturing through acoustic techniques using wavelet and convolutional neural network (CNN). The International Journal of Advanced Manufacturing Technology, 116: 3473-3488. doi: 10.1007/s00170-021-07721-z [67] Hua T, Jing C, Xin L, Fengying Z, Weidong, H. 2008. Research on molten pool temperature in the process of laser rapid forming. Journal of Materials Processing Technology, 198: 454-462. doi: 10.1016/j.jmatprotec.2007.06.090 [68] Huang G, Gu D, Liu H, Lin K, Wang J, Sun H, et al. 2025a. The effect of thermal history on elemental diffusion, phase evolution, and mechanical properties of laser powder bed fusion fabricated Ti6Al4V/AlMgScZr multi-material components. Additive Manufacturing, 107: 104821. doi: 10.1016/j.addma.2025.104821 [69] Huang X, Hou X, Wang P, Zhao D, Zhao J, Zhang H, et al. 2025b. A spatio-temporal fusion method for non-destructive testing using infrared thermography. Optics and Lasers in Engineering, 194: 109133. doi: 10.1016/j.optlaseng.2025.109133 [70] Iravani-Tabrizipour M, Toyserkani, E. 2007. An image-based feature tracking algorithm for real-time measurement of clad height. Machine Vision and Applications, 18: 343-354. doi: 10.1007/s00138-006-0066-7 [71] Ito K, Kusano M, Demura M, Watanabe, M. 2021. Detection and location of microdefects during selective laser melting by wireless acoustic emission measurement. Additive Manufacturing, 40: 101915. doi: 10.1016/j.addma.2021.101915 [72] Jamnikar N D, Liu S, Brice C, Zhang, X. 2022a. In situ microstructure property prediction by modeling molten pool-quality relations for wire-feed laser additive manufacturing. Journal of Manufacturing Processes, 79: 803-814. doi: 10.1016/j.jmapro.2022.05.013 [73] Jamnikar N D, Liu S, Brice C, Zhang, X. 2022b. In-process comprehensive prediction of bead geometry for laser wire-feed DED system using molten pool sensing data and multi-modality CNN. The International Journal of Advanced Manufacturing Technology, 121: 903-917. doi: 10.1007/s00170-022-09248-3 [74] Ji C, Li K, Liao R, Li Z, Yin B, Wen P, et al. 2025. Tensile creep mechanisms of laser powder bed fused WE43 alloy with heterogeneous microstructure: Evolution in dislocations and precipitates. Journal of Materials Science & Technology, 238: 209-229. doi: 10.1016/j.jmst.2025.02.063 [75] Jyeniskhan N, Shomenov K, Ali M H, Shehab, E. 2024. Exploring the integration of digital twin and additive manufacturing technologies. International Journal of Lightweight Materials and Manufacture, 7: 860-881. doi: 10.1016/j.ijlmm.2024.06.004 [76] Kasperovich G, Haubrich J, Gussone J, Requena, G. 2016. Correlation between porosity and processing parameters in TiAl6V4 produced by selective laser melting. Materials & Design, 105: 160-170. doi: 10.1016/j.matdes.2016.05.070 [77] Keist J S, Palmer, T. A. 2016. Role of geometry on properties of additively manufactured Ti-6Al-4V structures fabricated using laser based directed energy deposition. Materials & Design, 106: 482-494. doi: 10.1016/j.matdes.2016.05.045 [78] Khanzadeh M, Chowdhury S, Marufuzzaman M, Tschopp M A, Bian, L. 2018. Porosity prediction: Supervised-learning of thermal history for direct laser deposition. Journal of Manufacturing Systems, 47: 69-82. doi: 10.1016/j.jmsy.2018.04.001 [79] Khanzadeh M, Chowdhury , Sudipta; Tschopp , Mark A. ; Doude , Haley R. ; Marufuzzaman , Mohammad; and Bian, L. 2019. In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes. IISE Transactions, 51: 437–455. [80] Kim J, Yang Z, Ko H, Cho H, Lu, Y. 2023. Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing. Journal of Manufacturing Systems, 68: 117-129. doi: 10.1016/j.jmsy.2023.03.006 [81] Kong L, Peng X, Chen Y, Wang P, Xu, M. 2020. Multi-sensor measurement and data fusion technology for manufacturing process monitoring: a literature review. International Journal of Extreme Manufacturing, 2: 022001. doi: 10.1088/2631-7990/ab7ae6 [82] Kononenko D Y, Nikonova V, Seleznev M, van den Brink J, Chernyavsky, D. 2023. An in situ crack detection approach in additive manufacturing based on acoustic emission and machine learning. Additive Manufacturing Letters, 5: 100130. doi: 10.1016/j.addlet.2023.100130 [83] Kürnsteiner P, Wilms M B, Weisheit A, Gault B, Jägle E A, Raabe, D. 2020. High-strength Damascus steel by additive manufacturing. Nature, 582: 515-519. doi: 10.1038/s41586-020-2409-3 [84] Kwon O, Kim H G, Ham M J, Kim W, Kim, G. -H. ; Cho, J. -H. ; et al. 2020. A deep neural network for classification of melt-pool images in metal additive manufacturing. Journal of Intelligent Manufacturing, 31: 375–386. [85] Lee H, Heogh W, Yang J, Yoon J, Park J, Ji S, et al. 2022. Deep learning for in-situ powder stream fault detection in directed energy deposition process. Journal of Manufacturing Systems, 62: 575-587. doi: 10.1016/j.jmsy.2022.01.013 [86] Li B, Li K, Li Z, Chen X, Murr L E, Zhu L, et al. 2025a. CMT wire arc additive manufactured ultra-high strength WE43 alloy via in-situ cooling and precipitation regulating. Materials Research Letters, 13: 596-604. doi: 10.1080/21663831.2025.2483424 [87] Li C, Liu Z Y, Fang X Y, Guo, Y. B. 2018. Residual Stress in Metal Additive Manufacturing. Procedia CIRP, 71: 348-353. doi: 10.1016/j.procir.2018.05.039 [88] Li J, Duan Q, Hou J, Xie H, Liu, S. 2020a. In-situ monitoring of substrate deformation in directed energy deposition process using the coherent gradient sensing method. Additive Manufacturing, 36: 101547. doi: 10.1016/j.addma.2020.101547 [89] Li J, Cao L, Xu J, Wang S, Zhou, Q. 2022a. In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing. Measurement, 187: 110232. doi: 10.1016/j.measurement.2021.110232 [90] Li J, Zhang X, Zhou Q, Chan, F. T. S. ; Hu, Z. 2022b. A feature-level multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. Journal of Manufacturing Processes, 84: 913–926. [91] Li J, Zhou Q, Cao L, Wang Y, Hu, J. 2022c. A convolutional neural network-based multi-sensor fusion approach for in-situ quality monitoring of selective laser melting. Journal of Manufacturing Systems, 64: 429-442. doi: 10.1016/j.jmsy.2022.07.007 [92] Li X, Siahpour S, Lee J, Wang Y, Shi, J. 2020b. Deep Learning-Based Intelligent Process Monitoring of Directed Energy Deposition in Additive Manufacturing with Thermal Images. Procedia Manufacturing, 48: 643-649. doi: 10.1016/j.promfg.2020.05.093 [93] Li Y, Liu C, Gu J, Song, M. 2025b. Recent advances on defects, microstructures and mechanical properties of additively manufactured titanium alloys. Materials Today Communications, 42: 111312. doi: 10.1016/j.mtcomm.2024.111312 [94] Liao S, Webster S, Huang D, Council R, Ehmann K, Cao, J. 2022. Simulation-guided variable laser power design for melt pool depth control in directed energy deposition. Additive Manufacturing, 56: 102912. doi: 10.1016/j.addma.2022.102912 [95] Liu B, Lan S, Li J, Fang Q, Ren Y, He W, et al. 2024a. Digital image correlation in extreme conditions. Thin-Walled Structures, 205: 112589. doi: 10.1016/j.tws.2024.112589 [96] Liu C, Wang R R, Ho I, Kong Z J, Williams C, Babu S, et al. 2023. Toward online layer-wise surface morphology measurement in additive manufacturing using a deep learning-based approach. Journal of Intelligent Manufacturing, 34: 2673-2689. doi: 10.1007/s10845-022-01933-0 [97] Liu C, Yuan T, Shan H, Wang Y, Lai H, Chen, S. 2025a. A novel deep learning model for the real-time prediction of emissivity and thermal history in metal additive manufacturing processes. Journal of Manufacturing Processes, 135: 301-314. doi: 10.1016/j.jmapro.2025.01.028 [98] Liu H, Lin Y, Liu Y, Ye G, Cheng Z, Zeng L, et al. 2025b. Non-contact quantification of subsurface defects in additive manufacturing via laser ultrasonic Rayleigh wave polarity reversal. Optics & Laser Technology, 191: 113309. doi: 10.1016/j.optlastec.2025.113309 [99] Liu L, Ju F, Kim, S. 2025c. Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach. Additive Manufacturing, 109: 104882. doi: 10.1016/j.addma.2025.104882 [100] Liu M, Kumar A, Bukkapatnam S, Kuttolamadom, M. 2021. A Review of the Anomalies in Directed Energy Deposition (DED) Processes & Potential Solutions - Part Quality & Defects. Procedia Manufacturing, 53: 507-518. doi: 10.1016/j.promfg.2021.06.093 [101] Liu W, Hu P, Xiao J, Yin Q, Zhou X, Li H, et al. 2024b. High precision detection of artificial defects in additively manufactured Ti6Al4V alloy via laser ultrasonic testing. Journal of Materials Research and Technology, 30: 8740-8748. doi: 10.1016/j.jmrt.2024.05.140 [102] Liu Y, Peng Z, Liu S, Hu, P. 2022. Numerical simulation of laser ultrasonic detection of the surface microdefects on laser powder bed fusion additive manufactured 316L stainless steel. Theoretical and Applied Mechanics Letters, 12: 100398. doi: 10.1016/j.taml.2022.100398 [103] Lough C S, Liu T, Wang X, Brown B, Landers R G, Bristow D A, et al. 2022. Local prediction of Laser Powder Bed Fusion porosity by short-wave infrared imaging thermal feature porosity probability maps. Journal of Materials Processing Technology, 302: 117473. doi: 10.1016/j.jmatprotec.2021.117473 [104] Lu Q Y, Nguyen N V, Hum, A. J. W. ; Tran T, Wong, C. H. 2019a. Optical in-situ monitoring and correlation of density and mechanical properties of stainless steel parts produced by selective laser melting process based on varied energy density. Journal of Materials Processing Technology, 271: 520–531. [105] Lu X, Lin X, Chiumenti M, Cervera M, Hu Y, Ji X, et al. 2019b. Residual stress and distortion of rectangular and S-shaped Ti-6Al-4V parts by Directed Energy Deposition: Modelling and experimental calibration. Additive Manufacturing, 26: 166-179. doi: 10.1016/j.addma.2019.02.001 [106] Lyu J, and Manoochehri, S. 2021. Online Convolutional Neural Network-based anomaly detection and quality control for Fused Filament Fabrication process. Virtual and Physical Prototyping, 16: 160-177. doi: 10.1080/17452759.2021.1905858 [107] Ma H, Mao Z, Feng W, Yang Y, Hao C, Zhou J, et al. 2022. Online in-situ monitoring of melt pool characteristic based on a single high-speed camera in laser powder bed fusion process. Applied Thermal Engineering, 211: 118515. doi: 10.1016/j.applthermaleng.2022.118515 [108] Ma H Y, Wang J C, Qin P, Liu Y J, Chen L Y, Wang L Q, et al. 2024. Advances in additively manufactured titanium alloys by powder bed fusion and directed energy deposition: Microstructure, defects, and mechanical behavior. Journal of Materials Science & Technology, 183: 32-62. doi: 10.1016/j.jmst.2023.11.003 [109] Mao Z, Feng W, Hao C, Liu, Z. 2022. Simulation and experimental analysis of melt pool evolution in laser engineered net shaping. Theoretical and Applied Mechanics Letters, 12: 100366. doi: 10.1016/j.taml.2022.100366 [110] Mao Z, Feng W, Ma H, Yang Y, Zhou J, Liu S, et al. 2023. Continuous online flaws detection with photodiode signal and melt pool temperature based on deep learning in laser powder bed fusion. Optics & Laser Technology, 158: 108877. doi: 10.1016/j.optlastec.2022.108877 [111] Mao Z, Feng W, Han X, Ma H, Hao C, Liu C, et al. 2025. Development of a melt pool characteristics detection platform based on multi-information fusion of temperature fields and photodiode signals in plasma arc welding. Journal of Intelligent Manufacturing, 36: 2017-2037. doi: 10.1007/s10845-024-02342-1 [112] McCann R, Obeidi M A, Hughes C, McCarthy É, Egan D S, Vijayaraghavan R K, et al. 2021. In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review. Additive Manufacturing, 45: 102058. doi: 10.1016/j.addma.2021.102058 [113] McKinney M, Garland A, Cillessen D, Adamczyk J, Bolintineanu D, Heiden M, et al. 2025. Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring. Journal of Manufacturing Systems, 78: 271-282. doi: 10.1016/j.jmsy.2024.12.003 [114] Meng C, Porter C, Estalaki S M, Yang S, Mathesen G, Cao, J. 2025. Part-scale keyhole pore detection in laser powder bed fusion using coaxial photodiodes. Additive Manufacturing, 114: 105031. doi: 10.1016/j.addma.2025.105031 [115] Mi J, Zhang Y, Li H, Shen S, Yang Y, Song C, et al. 2023. In-situ monitoring laser based directed energy deposition process with deep convolutional neural network. Journal of Intelligent Manufacturing, 34: 683-693. doi: 10.1007/s10845-021-01820-0 [116] Min Y, Shen S, Li H, Liu S, Mi J, Zhou J, et al. 2022. Online monitoring of an additive manufacturing environment using a time-of-flight mass spectrometer. Measurement, 189: 110473. doi: 10.1016/j.measurement.2021.110473 [117] Moralejo S, Penaranda X, Nieto S, Barrios A, Arrizubieta I, Tabernero I, et al. 2017. A feedforward controller for tuning laser cladding melt pool geometry in real time. The International Journal of Advanced Manufacturing Technology, 89: 821-831. doi: 10.1007/s00170-016-9138-7 [118] Nguyen N V, Hum , Allen Jun Wee; Do , Truong; and Tran, T. 2023. Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion. Virtual and Physical Prototyping, 18: e2129396. [119] Ni, C. -Y. ; Shuai T, Sun, Y. -C. ; Ying, K. -N. ; Dai, G. -Q. ; Sun, Z. -G. ; et al. 2026. Hardness detection in Friction Stir Additive Manufacturing (FSAM) based on the CEEMDAN algorithm and laser ultrasonic Spatially Resolved Acoustic Spectroscopy (SRAS). NDT & E International, 157: 103515. [120] Niu K, Yin M, Xie L, Ding X, Zhang Y, Peng K, et al. 2025. Local defects prediction in laser additive manufacturing via multisensor monitoring strategy and multi-feature fusion convolutional neural network. Optics & Laser Technology, 188: 113005. doi: 10.1016/j.optlastec.2025.113005 [121] Ocylok S, Alexeev E, Mann S, Weisheit A, Wissenbach K, Kelbassa, I. 2014. Correlations of Melt Pool Geometry and Process Parameters During Laser Metal Deposition by Coaxial Process Monitoring. Physics Procedia, 56: 228-238. doi: 10.1016/j.phpro.2014.08.167 [122] Özel, T. 2025. Deep learning-based applications in metal additive manufacturing processes: Challenges and opportunities–A review. International Journal of Lightweight Materials and Manufacture, 8: 453-468. doi: 10.1016/j.ijlmm.2025.04.001 [123] Pandiyan V, Drissi-Daoudi , Rita; Shevchik , Sergey; Masinelli , Giulio; Le-Quang , Tri; Logé , Roland; et al. 2021. Semi-supervised Monitoring of Laser powder bed fusion process based on acoustic emissions. Virtual and Physical Prototyping, 16: 481–497. [124] Pandiyan V, Drissi-Daoudi R, Shevchik S, Masinelli G, Le-Quang T, Logé R, et al. 2022a. Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process. Journal of Materials Processing Technology, 303: 117531. doi: 10.1016/j.jmatprotec.2022.117531 [125] Pandiyan V, Masinelli G, Claire N, Le-Quang T, Hamidi-Nasab M, de Formanoir C, et al. 2022b. Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance. Additive Manufacturing, 58: 103007. doi: 10.1016/j.addma.2022.103007 [126] Perani M, Baraldo S, Decker M, Vandone A, Valente A, Paoli, B. 2023. Track geometry prediction for Laser Metal Deposition based on on-line artificial vision and deep neural networks. Robotics and Computer-Integrated Manufacturing, 79: 102445. doi: 10.1016/j.rcim.2022.102445 [127] Petrich J, Snow Z, Corbin D, Reutzel, E. W. 2021. Multi-modal sensor fusion with machine learning for data-driven process monitoring for additive manufacturing. Additive Manufacturing, 48: 102364. doi: 10.1016/j.addma.2021.102364 [128] Qin L, Wang K, Li X, Zhou S, Yang, G. 2022. Review of the Formation Mechanisms and Control Methods of Geometrical Defects in Laser Deposition Manufacturing. Chinese Journal of Mechanical Engineering: Additive Manufacturing Frontiers, 1: 100052. doi: 10.1016/j.cjmeam.2022.100052 [129] Qiu C, Ravi G A, Dance C, Ranson A, Dilworth S, Attallah, M. M. 2015. Fabrication of large Ti–6Al–4V structures by direct laser deposition. Journal of Alloys and Compounds, 629: 351-361. doi: 10.1016/j.jallcom.2014.12.234 [130] Ramalho A, Santos T G, Bevans B, Smoqi Z, Rao P, Oliveira, J. P. 2022. Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel. Additive Manufacturing, 51: 102585. doi: 10.1016/j.addma.2021.102585 [131] Rashid A, Vatandoust F, Kota A, Melkote, S. N. 2025. Bead geometry prediction in wire arc directed energy deposition using physics-informed machine learning and low-fidelity data. Additive Manufacturing, 109: 104881. doi: 10.1016/j.addma.2025.104881 [132] Ren H, Zhang Y, Li H, Long, Y. 2025. Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool. Applied Sciences, 15: 4850. doi: 10.3390/app15094850 [133] Ren Z, Gao L, Clark S J, Fezzaa K, Shevchenko P, Choi A, et al. 2023. Machine learning–aided real-time detection of keyhole pore generation in laser powder bed fusion. Science, 379: 89-94. doi: 10.1126/science.add4667 [134] Ruiz de la Hermosa González-Carrato R, García Márquez F P, Dimlaye V, Ruiz-Hernández, D. 2014. Pattern recognition by wavelet transforms using macro fibre composites transducers. Mechanical Systems and Signal Processing, 48: 339-350. doi: 10.1016/j.ymssp.2014.04.002 [135] Sajadi P, Dehaghani M R, Tang Y, Wang, G. G. 2025. Two-dimensional temperature field prediction with in-situ data in metal additive manufacturing using physics-informed neural networks. Engineering Applications of Artificial Intelligence, 150: 110636. doi: 10.1016/j.engappai.2025.110636 [136] Sammons P M, Gegel M L, Bristow D A, Landers, R. G. 2019. Repetitive Process Control of Additive Manufacturing With Application to Laser Metal Deposition. IEEE Transactions on Control Systems Technology, 27: 566-575. doi: 10.1109/tcst.2017.2781653 [137] Schwerz C, Raza A, Lei X, Nyborg L, Hryha E, Wirdelius, H. 2021. In-situ detection of redeposited spatter and its influence on the formation of internal flaws in laser powder bed fusion. Additive Manufacturing, 47: 102370. doi: 10.1016/j.addma.2021.102370 [138] Scime L, Beuth, J. 2018. A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 24: 273-286. doi: 10.1016/j.addma.2018.09.034 [139] Scime L, Beuth, J. 2019. Using machine learning to identify in-situ melt pool signatures indicative of flaw formation in a laser powder bed fusion additive manufacturing process. Additive Manufacturing, 25: 151-165. doi: 10.1016/j.addma.2018.11.010 [140] Segovia Ramírez I, Das B, García Márquez, F. P. 2022. Fault detection and diagnosis in photovoltaic panels by radiometric sensors embedded in unmanned aerial vehicles. Progress in Photovoltaics: Research and Applications, 30: 240-256. doi: 10.1002/pip.3479 [141] Sharma R, Guo, Y. B. 2025. Thermo-mechanical physics-informed deep learning for prediction of thermal stress evolution in laser metal deposition. Engineering Applications of Artificial Intelligence, 157: 111554. doi: 10.1016/j.engappai.2025.111554 [142] Shevchik S A, Kenel C, Leinenbach C, Wasmer, K. 2018. Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks. Additive Manufacturing, 21: 598-604. doi: 10.1016/j.addma.2017.11.012 [143] Shevchik S A, Masinelli G, Kenel C, Leinenbach C, Wasmer, K. 2019. Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission. IEEE Transactions on Industrial Informatics, 15: 5194-5203. doi: 10.1109/TII.2019.2910524 [144] Shi B, Chen, Z. 2021. A layer-wise multi-defect detection system for powder bed monitoring: Lighting strategy for imaging, adaptive segmentation and classification. Materials & Design, 210: 110035. doi: 10.1016/j.matdes.2021.110035 [145] Shi T, Shi J, Xia Z, Lu B, Shi S, Fu, G. 2020. Precise control of variable-height laser metal deposition using a height memory strategy. Journal of Manufacturing Processes, 57: 222-232. doi: 10.1016/j.jmapro.2020.05.026 [146] Shi W, Zhang C, Chen R, Xie H, Liu, F. 2023. Multiphysics coupling in situ measurement method for laser repair of the Inconel 718 alloy structure. Science China Technological Sciences, 66: 2986-2995. doi: 10.1007/s11431-022-2419-7 [147] Siva Prasad H, Brueckner F, Kaplan, A. F. H. 2020. Powder incorporation and spatter formation in high deposition rate blown powder directed energy deposition. Additive Manufacturing, 35: 101413. doi: 10.1016/j.addma.2020.101413 [148] Skiadopoulos M, Kifer D, Shokouhi, P. 2026. A transfer learning approach to the prediction of porosity in additively manufactured metallic components. NDT & E International, 157: 103531. doi: 10.1016/j.ndteint.2025.103531 [149] Snow Z, Diehl B, Reutzel E W, Nassar, A. 2021. Toward in-situ flaw detection in laser powder bed fusion additive manufacturing through layerwise imagery and machine learning. Journal of Manufacturing Systems, 59: 12-26. doi: 10.1016/j.jmsy.2021.01.008 [150] Song L, Bagavath-Singh V, Dutta B, Mazumder, J. 2012. Control of melt pool temperature and deposition height during direct metal deposition process. The International Journal of Advanced Manufacturing Technology, 58: 247-256. doi: 10.1007/s00170-011-3395-2 [151] Su Y, Xu G, Xu X, Luo K, Lu, J. 2024a. In-situ thermal control-assisted laser directed energy deposition of curved-surface thin-walled parts. Additive Manufacturing, 83: 104061. doi: 10.1016/j.addma.2024.104061 [152] Su Y, Xu G, Xu X, Zhang H, Luo K, Lu, J. 2024b. Melt pool control-assisted additive manufacturing of thin-walled parts. International Journal of Mechanical Sciences, 280: 109519. doi: 10.1016/j.ijmecsci.2024.109519 [153] Surovi N A, Soh, G. S. 2023. Acoustic feature based geometric defect identification in wire arc additive manufacturing. Virtual and Physical Prototyping. [154] Taheri H, Koester L W, Bigelow T A, Faierson E J, Bond, L. J. 2019. In Situ Additive Manufacturing Process Monitoring With an Acoustic Technique: Clustering Performance Evaluation Using K-Means Algorithm. Journal of Manufacturing Science and Engineering 141. [155] Tang Z, Liu W, Zhu L, Liu Z, Yan Z, Lin D, et al. 2021. Investigation on coaxial visual characteristics of molten pool in laser-based directed energy deposition of AISI 316L steel. Journal of Materials Processing Technology, 290: 116996. doi: 10.1016/j.jmatprotec.2020.116996 [156] Tempelman J R, Wachtor A J, Flynn E B, Depond P J, Forien, J. -B. ; Guss G M, et al. 2022. Detection of keyhole pore formations in laser powder-bed fusion using acoustic process monitoring measurements. Additive Manufacturing, 55: 102735. [157] Thompson S M, Bian L, Shamsaei N, Yadollahi, A. 2015. An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics. Additive Manufacturing, 8: 36-62. doi: 10.1016/j.addma.2015.07.001 [158] Vandone A, Baraldo S, Valente, A. 2018. Multisensor Data Fusion for Additive Manufacturing Process Control. IEEE Robotics and Automation Letters, 3: 3279-3284. doi: 10.1109/LRA.2018.2851792 [159] Wang P, Feng W, Jiang C, Long Z, Zhao J, Zhang Y, et al. 2026. Multi-physics characterization of molten pool via an online in situ monitoring system in metal additive manufacturing. Journal of Manufacturing Processes, 157: 352-370. doi: 10.1016/j.jmapro.2025.11.059 [160] Wang R, Cheung, C. F. 2022. CenterNet-based defect detection for additive manufacturing. Expert Systems with Applications, 188: 116000. doi: 10.1016/j.eswa.2021.116000 [161] Wang R, Standfield B, Dou C, Law A C, Kong, Z. J. 2023. Real-time process monitoring and closed-loop control on laser power via a customized laser powder bed fusion platform. Additive Manufacturing, 66: 103449. doi: 10.1016/j.addma.2023.103449 [162] Wasmer K, Le-Quang T, Meylan B, Shevchik, S. A. 2019. In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach. Journal of Materials Engineering and Performance, 28: 666-672. doi: 10.1007/s11665-018-3690-2 [163] Webster S, Jeong J, Mogonye, J. -E. ; Zuback J, Liao S, Rocher J, et al. 2025. Effect of closed-loop coaxial melt pool temperature control on thermal history and microstructure of nickel alloy 718 in directed energy deposition. Journal of Materials Processing Technology, 337: 118725. [164] Whiting J, Springer A, Sciammarella, F. 2018. Real-time acoustic emission monitoring of powder mass flow rate for directed energy deposition. Additive Manufacturing, 23: 312-318. doi: 10.1016/j.addma.2018.08.015 [165] Wolff S J, Wu H, Parab N, Zhao C, Ehmann K F, Sun T, et al. 2019. In-situ high-speed X-ray imaging of piezo-driven directed energy deposition additive manufacturing. Scientific Reports, 9: 962. doi: 10.1038/s41598-018-36678-5 [166] Wu Y, Fang J, Wu C, Li C, Sun G, Li, Q. 2023a. Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption. International Journal of Mechanical Sciences, 246: 108102. doi: 10.1016/j.ijmecsci.2023.108102 [167] Wu Z, Tang G, Clark S J, Meshkov A, Roychowdhury S, Gould B, et al. 2023b. High frequency beam oscillation keyhole dynamics in laser melting revealed by in-situ x-ray imaging. Communications Materials, 4: 5. doi: 10.1038/s43246-023-00332-z [168] Xia C, Pan Z, Li Y, Chen J, Li, H. 2022. Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method. The International Journal of Advanced Manufacturing Technology, 120: 551-562. doi: 10.1007/s00170-022-08811-2 [169] Xie X, Bennett J, Saha S, Lu Y, Cao J, Liu W K, et al. 2021. Mechanistic data-driven prediction of as-built mechanical properties in metal additive manufacturing. Npj Computational Materials, 7: 1-12. doi: 10.1038/s41524-021-00555-z [170] Xu P, Yao X, Chen L, Zhao C, Liu K, Moon S K, et al. 2022. In-process adaptive dimension correction strategy for laser aided additive manufacturing using laser line scanning. Journal of Materials Processing Technology, 303: 117544. doi: 10.1016/j.jmatprotec.2022.117544 [171] Xu Y, Weaver J B, Healy D M, Lu, J. 1994. Wavelet transform domain filters: a spatially selective noise filtration technique. IEEE Transactions on Image Processing, 3: 747-758. doi: 10.1109/83.336245 [172] Xu Z, Tian Q, Hu P, Li H, Shen, S. 2024. Laser ultrasonic detection of submillimeter artificial holes in laser powder bed fusion manufactured alloys. Optics & Laser Technology, 169: 110030. doi: 10.1016/j.optlastec.2023.110030 [173] Xue H W, Li S Q, Zhang Z F, Xie, H. M. 2025. Integrating photoluminescence with DIC for three-dimensional temperature and deformation field measurements in cryogenic environments. Optics and Lasers in Engineering, 186: 108759. doi: 10.1016/j.optlaseng.2024.108759 [174] Yamashita Y, Kawabata K, Mori H, Ose E, Kunimine, T. 2025. Influence of Laser Energy Variation on the Composition and Properties of Gradient-Structured Cemented Carbide Layers Produced by LP-DED. Journal of Manufacturing and Materials Processing, 9: 218. doi: 10.3390/jmmp9070218 [175] Yang J, Liu G, Zhu W, Zhang Y, Zhou W, Liu D, et al. 2025. High-Precision and Ultraspeed Monitoring of Melt-Pool Morphology in Laser-Directed Energy Deposition Using Deep Learning. Additive Manufacturing Frontiers, 4: 200199. doi: 10.1016/j.amf.2025.200199 [176] Yang S, Tang Z, Ye Y, Kumar M, Wang F, Peng, S. 2026. A PINN–EWMA framework with uncertainty quantification for high-fidelity temperature field modeling and early defect warning in directed energy deposition of thin-walled structures. Reliability Engineering & System Safety, 267: 111834. doi: 10.1016/j.ress.2025.111834 [177] Ye D, Hong G S, Zhang Y, Zhu K, Fuh, J. Y. H. 2018. Defect detection in selective laser melting technology by acoustic signals with deep belief networks. The International Journal of Advanced Manufacturing Technology, 96: 2791-2801. doi: 10.1007/s00170-018-1728-0 [178] Ye L, Xue H, Li Z, Zhou Y, Chen G, Xu F, et al. 2025. Review of online quality control for laser directed energy deposition (LDED) additive manufacturing. International Journal of Extreme Manufacturing 7. [179] Yin Q, Hu P, Xu Z, Li H, Li H, Shen, S. 2024. Influence of surface roughness on laser ultrasonic detection for laser powder bed fusion manufactured 316L stainless steel. Journal of Materials Research and Technology, 28: 605-614. doi: 10.1016/j.jmrt.2023.12.004 [180] Zeng G Z, Zu R L, Wu D L, Shi W X, Zhou J F, Zhao J Y, et al. 2021. A Hybrid Method for the Online Evaluation of Stress Fields in Metal Additive Manufacturing. Experimental Mechanics, 61: 1261-1270. doi: 10.1007/s11340-021-00735-4 [181] Zhan J, Li K, Ma R, Zhu L, Fang J, Cao H, et al. 2024. Achieving fine tailoring of elastocaloric properties of laser powder bed-fused NiTi alloy via laser beam manipulation. International Journal of Machine Tools and Manufacture, 202: 104210. doi: 10.1016/j.ijmachtools.2024.104210 [182] Zhang B, Liu S, Shin, Y. C. 2019. In-Process monitoring of porosity during laser additive manufacturing process. Additive Manufacturing, 28: 497-505. doi: 10.1016/j.addma.2019.05.030 [183] Zhang C, Zhang Z, Zheng B, Zhong Q, Zhou F, Xie H, et al. 2026a. Probing melt pool dynamics in laser-directed energy deposition via optical metrology: Simultaneous mapping of 3D morphology and surface flow. Additive Manufacturing, 115: 105053. doi: 10.1016/j.addma.2025.105053 [184] Zhang K, Chen Y, Marussi S, Fan X, Fitzpatrick M, Bhagavath S, et al. 2024. Pore evolution mechanisms during directed energy deposition additive manufacturing. Nature Communications, 15: 1715. doi: 10.1038/s41467-024-45913-9 [185] Zhang Z, Zhang Y, Gao F, Du X, Chen L, Li, B. 2026b. High-resolution 3-D characterization and detection of subsurface defects in metal additive manufacturing using laser ultrasonic. Ultrasonics, 157: 107797. doi: 10.1016/j.ultras.2025.107797 [186] Zheng H, Peng J F, Sun X, Xie H M, Ding Y H, He, W. 2023. Distribution of microstructure, elastic modulus and residual stress near the interface in laser repaired GH4169 superalloy. Journal of Alloys and Compounds, 966: 171625. doi: 10.1016/j.jallcom.2023.171625 [187] Zhou H, Yang Y, Trofimov V, Li H, Liu Z, Xiao Y, et al. 2025. In-Situ Quality Monitoring in LPBF via Melt-Pool Radiation: Compressive Sampling and Deep Feature Extraction. Additive Manufacturing Frontiers: 200269. [188] Zou Z, Zhang K, Chen R, Liu T, Li J, Liao, W. 2025a. Defect prediction of localized regions in laser powder bed fusion based on multi-layer photodiode image fusion. Measurement, 252: 117413. doi: 10.1016/j.measurement.2025.117413 [189] Zou Z, Zhang K, Liu T, Wei H, Chen R, Liao, W. 2025b. In-situ monitoring of porous struts via photodiode signals in laser powder bed fusion lattice structures. Journal of Manufacturing Processes, 156: 142-162. doi: 10.1016/j.jmapro.2025.10.100 [190] Zu R L, Liang Wu D, Fan Zhou J, Wei Liu Z, Xie H M, Liu, S. 2023. Advances in Online Detection Technology for Laser Additive Manufacturing: A Review. 3D Printing and Additive Manufacturing, 10: 467-489. doi: 10.1089/3dp.2021.0049 -
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