High resolution-optical tomography for in-process layerwise monitoring of a laser-powder bed fusion technology

被引:14
|
作者
Guerra, Maria Grazia [1 ]
Errico, Vito [1 ]
Fusco, Andrea [1 ]
Lavecchia, Fulvio [1 ]
Campanelli, Sabina Luisa [1 ]
Galantucci, Luigi Maria [1 ]
机构
[1] Politecn Bari, Dept Mech Math & Management, Via Orabona 4, I-70125 Bari, Italy
关键词
Laser-powder bed fusion; AISI; 316L; In-process monitoring; High resolution-optical tomography; Photogrammetry; MECHANICAL-PROPERTIES; PROCESS PARAMETERS; SURFACE-ROUGHNESS; SITU MEASUREMENTS; MICROSTRUCTURE; STRATEGIES; DEPOSITION; SLM;
D O I
10.1016/j.addma.2022.102850
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With the implementation of Laser Powder Bed Fusion technologies for industrial production, the need for in-process monitoring has emerged to ensure stable conditions and the process to succeed. Indeed, despite significant technological advances, the rejected parts are still too high if compared to the more conventional manufacturing techniques. Several defects are known to affect the process, more likely when complex geometries are fabricated. Among these, the production of inclined thin walls without support structures is still critical and, in most cases, geometric distortions are observed on the overhanging surfaces. In order to overcome the above mentioned problems and improve the quality of these critical structures, an high resolution monitoring system based on Optical Tomography (OT) was proposed, called High Resolution-Optical Tomography (HR-OT). It uses a very high-resolution sensor, operating in the visible spectrum of light, on a large area of the surface layers comprising the entire printing platform. This very recent technique, typically applied for the detection of volumetric defects, such as porosity or lack of fusion, was successfully applied, in this paper, for the detection of geometric distortions, allowing to avoid onerous pre-processing phases in the image processing workflow. Specific geometric indexes were selected as monitoring outputs and were used for the construction of appropriate control charts in order to carry out statistical process monitoring. Finally, an off-line 3D reconstruction, by means of digital close range photogrammetry, was used to verify the effectiveness of the proposed monitoring solution when applied for in-process detection of geometric distortions.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges
    Zhang, Yingjie
    Yan, Wentao
    JOURNAL OF INTELLIGENT MANUFACTURING, 2023, 34 (06) : 2557 - 2580
  • [42] Applications of machine learning in metal powder-bed fusion in-process monitoring and control: status and challenges
    Yingjie Zhang
    Wentao Yan
    Journal of Intelligent Manufacturing, 2023, 34 : 2557 - 2580
  • [43] Gaussian process classification of melt pool motion for laser powder bed fusion process monitoring
    Wang, Qisheng
    Lin, Xin
    Duan, Xianyin
    Yan, Ruqiang
    Fuh, Jerry Ying Hsi
    Zhu, Kunpeng
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 198
  • [44] Quantification and Analysis of Residual Stresses in Braking Pedal Produced via Laser-Powder Bed Fusion Additive Manufacturing Technology
    Fojtik, Frantisek
    Potrok, Roman
    Hajnys, Jiri
    Ma, Quoc-Phu
    Kudrna, Lukas
    Mesicek, Jakub
    MATERIALS, 2023, 16 (17)
  • [45] Effects of Laser-Powder Bed Fusion Process Parameters on the Microstructure and Corrosion Properties of AlSi10Mg Alloy
    Rafieazad, Mehran
    Fathi, Parisa
    Mohammadi, Mohsen
    Nasiri, Ali
    JOURNAL OF THE ELECTROCHEMICAL SOCIETY, 2021, 168 (02)
  • [46] Investigation into the optical emission of features for powder-bed fusion AM process monitoring
    Yingjie Zhang
    Wentao Yan
    Xiaojun Peng
    Zhangdong Chen
    Zimeng Jiang
    Di Wang
    The International Journal of Advanced Manufacturing Technology, 2022, 121 : 2291 - 2303
  • [47] Investigation into the optical emission of features for powder-bed fusion AM process monitoring
    Zhang, Yingjie
    Yan, Wentao
    Peng, Xiaojun
    Chen, Zhangdong
    Jiang, Zimeng
    Wang, Di
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 121 (3-4): : 2291 - 2303
  • [48] Dark field optical observation of polymer powder bed fusion for process monitoring and control
    Black, Derek
    Henderson, Jacob
    Klocke, Philip
    Shumway, Landon
    Crane, Nathan B.
    ADDITIVE MANUFACTURING, 2023, 74
  • [49] Quantified Approach for Evaluation of Geometry Visibility of Optical-Based Process Monitoring System for Laser Powder Bed Fusion
    Zhang, Song
    Adjei-Kyeremeh, Frank
    Wang, Hui
    Kolter, Moritz
    Raffeis, Iris
    Schleifenbaum, Johannes Henrich
    Buehrig-Polaczek, Andreas
    METALS, 2023, 13 (01)
  • [50] A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring
    Hermann Baumgartl
    Josef Tomas
    Ricardo Buettner
    Markus Merkel
    Progress in Additive Manufacturing, 2020, 5 : 277 - 285