In situ Infrared Temperature Sensing for Real-Time Defect Detection in Additive Manufacturing

2021 ◽  
pp. 102328
Author(s):  
Rifat-E-Nur Hossain ◽  
Jerald Lewis ◽  
Arden L. Moore
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Defect Detection ◽  
Temperature Sensing

scholarly journals Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

2021 ◽  
Author(s):  
Matteo Bugatti ◽  
Bianca Maria Colosimo
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Defect Detection ◽  
High Speed ◽  
Data Extraction ◽  
In Situ Monitoring ◽  
Imaging Data ◽  
Computational Speed

AbstractThe increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection.

In situ real time defect detection of 3D printed parts

2017 ◽  
Vol 17 ◽  
pp. 135-142 ◽  
Author(s):  
Oliver Holzmond ◽  
Xiaodong Li
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
3D Printed

scholarly journals Analytical modelling of in situ layer-wise defect detection in 3D-printed parts: additive manufacturing

2020 ◽  
Vol 111 (7-8) ◽  
pp. 2311-2321
Author(s):  
Oluwole K. Bowoto ◽  
Bankole I. Oladapo ◽  
S. A. Zahedi ◽  
Francis T. Omigbodun ◽  
Omonigho P. Emenuvwe
Keyword(s):  
Additive Manufacturing ◽  
Defect Detection ◽  
Analytical Modelling ◽  
3D Printed

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.

Atmospheric Probe for Real Time Weather Monitoring

2017 ◽  
Vol 2017 (1) ◽  
pp. 000280-000285
Author(s):  
J. Craig Prather ◽  
Michael Bolt ◽  
Haley Harrell ◽  
Tyler Horton ◽  
John Manobianco ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Relative Humidity ◽  
Real Time ◽  
Base Station ◽  
Gps Receiver ◽  
Time Analysis ◽  
Atmospheric Measurements ◽  
Real Time Analysis ◽  
Manufacturing Techniques

Abstract This work outlines the development and testing of novel in-situ atmospheric sensors. The system is designed for deployment of the sensors en masse to increase the geospatial density of atmospheric measurements. This sensor system is being designed to replace the costly, larger atmospheric sensors currently in use. Improvement in sensor technologies, substrates, and additive manufacturing techniques have allowed for such a device to be realizable. The devices contain a sensor suite that gathers temperature, relative humidity, and pressure from microsensors as well as position, velocity, and heading from a compact GPS receiver. This data is then sent to a base station for analysis by atmospheric scientists, with the potential for real time analysis.

scholarly journals Real-time Process Authentication for Additive Manufacturing Processes based on In-situ Video Analysis

2021 ◽  
Vol 53 ◽  
pp. 697-704
Author(s):  
Abdullah Al Mamun ◽  
Chenang Liu ◽  
Chen Kan ◽  
Wenmeng Tian
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Video Analysis ◽  
Manufacturing Processes ◽  
Time Process
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