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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
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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

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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
Journal Article

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

2018
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Overview
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.