In situ surface topography of laser powder bed fusion using fringe projection

Zhang, Bin; Ziegert, John C.; Farahi, Faramarz; Davies, Angela

doi:10.1016/j.addma.2016.08.001

Cited by 87 publications

(55 citation statements)

References 19 publications

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“…The sinking phenomenon is primarily due to the solidification process of the powder. Secondly, the amount of the surface sinking increases as the laser power increases and this is consistent with the literature [ 21 ]. Thirdly, one can calculate the surface roughness of the fusion area and the boundary of the fused area can be easily defined by the grooves between the fused and unfused regions.…”

Section: Methodssupporting

confidence: 91%

“…Mani et al [ 7 ] defined the quantities that can be determined during the PBF AM process as “process signatures” including dynamic characteristics of the manufacturing process. Among these process signatures, geometric signatures [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] are one of the main concerns in in situ monitoring techniques of the PBF AM process. In those early studies, several efforts used a monocular camera to implement 2D geometric measurement and defects detection [ 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, lack of enough correlation between the stereo image pairs should be reconsidered in the PBF AM process while it is practically impossible to prepare speckles over the powder bed. Zhang et al [ 20 , 21 ] proposed an in situ surface 3D topography of laser powder bed fusion using monocular fringe projection. They demonstrated that this system was suitable for measuring the height profile and surface pattern of each layer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods

Liu

Wen

et al. 2018

Sensors

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Lack of monitoring of the in situ process signatures is one of the challenges that has been restricting the improvement of Powder-Bed-Fusion Additive Manufacturing (PBF AM). Among various process signatures, the monitoring of the geometric signatures is of high importance. This paper presents the use of vision sensing methods as a non-destructive in situ 3D measurement technique to monitor two main categories of geometric signatures: 3D surface topography and 3D contour data of the fusion area. To increase the efficiency and accuracy, an enhanced phase measuring profilometry (EPMP) is proposed to monitor the 3D surface topography of the powder bed and the fusion area reliably and rapidly. A slice model assisted contour detection method is developed to extract the contours of fusion area. The performance of the techniques is demonstrated with some selected measurements. Experimental results indicate that the proposed method can reveal irregularities caused by various defects and inspect the contour accuracy and surface quality. It holds the potential to be a powerful in situ 3D monitoring tool for manufacturing process optimization, close-loop control, and data visualization.

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Section: Methodssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods

Liu

Wen

et al. 2018

Sensors

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“…Fig. 14 shows a typical setup of in-process fringe projection in laser powder bed fusion manufacturing (LPBF) [255]. The lateral resolution of the employed fringe projection system was 10 m achieved by using a digital camera with pixel numbers of 4096 × 2160 and a 50 mm lens.…”

Section: Active Stereo Vision (Fringe Projection)mentioning

confidence: 99%

On-machine and in-process surface metrology for precision manufacturing

et al. 2019

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On-machine and in-process surface metrology are important for quality control in manufacturing of precision surfaces. The classifications, requirements and tasks of on-machine and in-process surface metrology are addressed. The state-of-the-art on-machine and in-process measurement systems and sensor technologies are presented. Error separation algorithms for removing machine tool errors, which is specially required in on-machine and in-process surface metrology, are overviewed, followed by a discussion on calibration and traceability. Advanced techniques on sampling strategies, measurement systems-machine tools interface, data flow and analysis as well as feedbacks for compensation manufacturing are then demonstrated. Future challenges and developing trends are also discussed.

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“…Therefore, the geometric correction factor is always applied to the acquired images. [28][29][30] In our study, we will be postprocessing the data from the off-axis sCMOS camera.…”

Section: In Situ Monitoring Techniquesmentioning

confidence: 99%

Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

et al. 2020

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Direct metal laser sintering, an additive manufacturing technique, has a huge growing demand in industries like aerospace, biomedical, and tooling sector due to its capability to manufacture complex parts with ease. Despite many technological advancements, the reliability and repeatability of the process are still an issue. Therefore, there is a demand for inline automatic fault detection and postprocessing tools to analyze the acquired in situ monitoring data aiming to provide better-quality assurance to the user. Herein, the treatment of the data obtained using the EOSTATE optical tomography monitoring system is focused. A balanced dataset is obtained with the help of computer tomography of the certified part (Stainless Steel CX cylindrical samples), through which a feature matrix is prepared, and the layers of the part are classified either having "Drift" or "No-drift." The model is trained with the feature matrix and tested on benchmark parts (Maraging Steel) and on an industrial part (knuckle, automotive part) manufactured in AlSi10Mg. The proposed semisupervised approach shows promising results for presented case studies. Thus, the semisupervised machine learning approach, if adopted, could prove to be a cost effective and fast approach to postprocess the in situ monitoring data with much ease.

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In situ surface topography of laser powder bed fusion using fringe projection

Cited by 87 publications

References 19 publications

In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods

In Situ 3D Monitoring of Geometric Signatures in the Powder-Bed-Fusion Additive Manufacturing Process via Vision Sensing Methods

On-machine and in-process surface metrology for precision manufacturing

Inline Drift Detection Using Monitoring Systems and Machine Learning in Selective Laser Melting

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