Towards process consistency and in-situ evaluation of porosity during laser powder bed additive manufacturing

2020 ◽  
Vol 25 (8) ◽  
pp. 679-689
Author(s):  
J. Raplee ◽  
J. Gockel ◽  
F. List ◽  
K. Carver ◽  
S. Foster ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed

scholarly journals Correlation between surface texture and internal defects in laser powder-bed fusion additive manufacturing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makiko Yonehara ◽  
Chika Kato ◽  
Toshi-Taka Ikeshoji ◽  
Koki Takeshita ◽  
Hideki Kyogoku
Keyword(s):  
Additive Manufacturing ◽  
Surface Texture ◽  
In Situ Monitoring ◽  
Mean Square ◽  
Powder Bed Fusion ◽  
Internal Defects ◽  
Powder Bed ◽  
Texture Parameters ◽  
Areal Surface

AbstractThe availability of an in-situ monitoring and feedback control system during the implementation of metal additive manufacturing technology ensures that high-quality finished parts are manufactured. This study aims to investigate the correlation between the surface texture and internal defects or density of laser-beam powder-bed fusion (LB-PBF) parts. In this study, 120 cubic specimens were fabricated via application of the LB-PBF process to the IN 718 Ni alloy powder. The density and 35 areal surface-texture parameters of manufactured specimens were determined based on the ISO 25,178–2 standard. Using a statistical method, a strong correlation was observed between the areal surface-texture parameters and density or internal defects within specimens. In particular, the areal surface-texture parameters of reduced dale height, core height, root-mean-square height, and root-mean-square gradient demonstrate a strong correlation with specimen density. Therefore, in-situ monitoring of these areal surface-texture parameters can facilitate their use as control variables in the feedback system.

scholarly journals In-situ characterization of laser-powder interaction and cooling rates through high-speed imaging of powder bed fusion additive manufacturing

2017 ◽  
Vol 135 ◽  
pp. 385-396 ◽  
Author(s):  
Umberto Scipioni Bertoli ◽  
Gabe Guss ◽  
Sheldon Wu ◽  
Manyalibo J. Matthews ◽  
Julie M. Schoenung
Keyword(s):  
Additive Manufacturing ◽  
High Speed ◽  
Powder Bed Fusion ◽  
In Situ Characterization ◽  
High Speed Imaging ◽  
Cooling Rates ◽  
Powder Bed

scholarly journals In-situ Synchrotron imaging of keyhole mode multi-layer laser powder bed fusion additive manufacturing

2020 ◽  
Vol 20 ◽  
pp. 100650 ◽  
Author(s):  
Yunhui Chen ◽  
Samuel J. Clark ◽  
Chu Lun Alex Leung ◽  
Lorna Sinclair ◽  
Sebastian Marussi ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Synchrotron Imaging

Ultrasonics for monitoring melt pool dynamics and in situ sensing of microstructure during powder bed fusion additive manufacturing

2021 ◽  
Vol 150 (4) ◽  
pp. A307-A307
Author(s):  
Christopher M. Kube ◽  
Nathan Kizer ◽  
Abdalla Nassar ◽  
Edward Reutzel ◽  
Haifeng Zhang ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool ◽  
In Situ Sensing

Fundamental Requirements for Data Representations in Laser-Based Powder Bed Fusion

2015 ◽  
Author(s):  
Shaw C. Feng ◽  
Paul W. Witherell ◽  
Gaurav Ameta ◽  
Duck Bong Kim
Keyword(s):  
Additive Manufacturing ◽  
Heterogeneous Systems ◽  
Powder Bed Fusion ◽  
Process Data ◽  
Data Interoperability ◽  
Powder Bed ◽  
Related Data ◽  
Data Representations ◽  
Am Processes

Additive Manufacturing (AM) processes intertwine aspects of many different engineering-related disciplines, such as material metrology, design, in-situ and off-line measurements, and controls. Due to the increasing complexity of AM systems and processes, data cannot be shared among heterogeneous systems because of a lack of a common vocabulary and data interoperability methods. This paper aims to address insufficiencies in laser-based Powder Bed Fusion (PBF), a specific AM process, data representations to improve data management and reuse in PBF. Our approach is to formally decompose the processes and align PBF process-specifics with information elements as fundamental requirements for representing process-related data. The paper defines the organization and flow of process information. After modeling selected PBF processes and sub-processes as activities, we discuss requirements for the development of more advanced process data models that provide common terminology and process knowledge for managing data from various stages in AM.

Layerwise Automated Visual Inspection in Laser Powder-Bed Additive Manufacturing

2015 ◽  
Author(s):  
Masoumeh Aminzadeh ◽  
Thomas Kurfess
Keyword(s):  
Additive Manufacturing ◽  
Defect Detection ◽  
Visual Inspection ◽  
Object Segmentation ◽  
Detection System ◽  
Automated Visual Inspection ◽  
Powder Bed ◽  
Geometric Objects ◽  
Powder Fusion

Laser powder-bed fusion (L-PBF) is an additive manufacturing (AM) process that enables fabrication of functional metal parts with near-net-shape geometries. The drawback to L-PBF is its lack of precision as well as the formation of defects due to process randomness and irregularities associated with laser powder fusion. Over the past two decades much research has been conducted to control laser powder fusion in order to provide parts of higher quality. This paper addresses online quality monitoring in AM by in-situ automated visual inspection of each layer which is aimed to geometric objects and defects from high-resolution visual images. A scheme for online defect detection system is presented that consists of three levels of processing: low-level, intermediate-level, and high-level processing. Each level is described and appropriately divided to several stages, when insightful. Techniques that are feasible in each level for successful defect detection and classification are identified and described. Requirements and specifications of the measurement data to achieve desired performance of the online defect detection system are stated. Image processing algorithms are developed for first level of processing and implemented for segmentation of geometric objects. Due to the large variation of intensities within the powder region and fused regions, and also the non-multi-modal nature of the image, the basic segmentation algorithms such as thresholding do not produce appropriate results. In this work, morphological operations are effectively designed and implemented following thresholding to achieve the desired object segmentation. Examples of implementations are given. The paper provides the results of object segmentation which is the initial stage of development of an in-situ automated visual inspection for L-PBF process.

Vision-Based Inspection System for Dimensional Accuracy in Powder-Bed Additive Manufacturing

2016 ◽  
Author(s):  
Masoumeh Aminzadeh ◽  
Thomas Kurfess
Keyword(s):  
Additive Manufacturing ◽  
Dimensional Accuracy ◽  
Powder Layer ◽  
Inspection System ◽  
Geometric Errors ◽  
Low Contrast ◽  
Powder Bed ◽  
Dimensional Precision ◽  
Geometric Objects

Laser powder-bed fusion (L-PBF) is an additive manufacturing (AM) process that enables fabrication of functional metal parts with near-net-shape geometries. The drawback to L-PBF is its lack of dimensional precision and accuracy. The efficiency of powder fusion process in powder-bed AM processes is highly affected by process errors, powder irregularities as well as geometric factors. Formation of defects such as lack of fusion and over-fusion due to the aforementioned factors causes dimensional errors that significantly damage the precision. This paper addresses the development of an automated in-situ inspection system for powder-bed additive manufacturing processes based on machine vision. The results of the in-situ automated inspection of dimensional accuracy allows for early identification of faulty parts or alternatively in-situ correction of geometric errors by taking appropriate corrective actions. In this inspection system, 2D optical images captured from each layer of the AM part during the build are analyzed and the geometric errors and defects impairing the dimensional accuracy are detected in each layer. To successfully detect geometric errors, fused geometric objects must be detected in the powder layer. Image processing algorithms are effectively designed to detect the geometric objects from images of low contrast captured during the build inside the chamber. The developed algorithms are implemented to a large number of test images and their performance and precision are evaluated quantitatively. The failure probabilities for the algorithms are also determined statistically.

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