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.

A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large Scale Additive Manufacturing

2020 ◽  
Author(s):  
Sepehr Fathizadan ◽  
Feng Ju ◽  
Kyle Rowe ◽  
Alex Fiechter ◽  
Nils Hofmann
Keyword(s):  
Additive Manufacturing ◽  
Surface Temperature ◽  
Real Time ◽  
Large Scale ◽  
Production Efficiency ◽  
Control Method ◽  
Data Extraction ◽  
Critical Role ◽  
Imaging Data ◽  
Time Data

Abstract Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this paper, we propose a framework for real-time data extraction from thermal images as well as a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large-Scale Additive Manufacturing

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Sepehr Fathizadan ◽  
Feng Ju ◽  
Kyle Rowe ◽  
Alex Fiechter ◽  
Nils Hofmann
Keyword(s):  
Additive Manufacturing ◽  
Surface Temperature ◽  
Real Time ◽  
Large Scale ◽  
Production Efficiency ◽  
Control Method ◽  
Data Extraction ◽  
Critical Role ◽  
Imaging Data ◽  
Time Data

Abstract Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large-scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this article, we propose a framework for real-time data extraction from thermal images and a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature, while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

scholarly journals Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): “Three-Dimensional Scan Strategies”

2019 ◽  
Vol 124 ◽  
Author(s):  
Brandon Lane ◽  
Ho Yeung
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Three Dimensional ◽  
Monitoring Data ◽  
Real Time Monitoring ◽  
3D Scan ◽  
Input Command

This document provides details on the files available in the dataset "20180708-HY-3D Scan Strategies" pertaining to a 3D additive manufacturing build performed on the Additive Manufacturing Metrology Testbed (AMMT)by Ho Yeung on July 8, 2018. The files include the input command files and in-situ process monitoring data, and metadata. This data is the first of future planned "AMMT Process Monitoring Reference Datasets," as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project.

Uniaxial High-Speed Micro-Scale 3D Surface Topographical Measurements Using Fringe Projection

2020 ◽  
Author(s):  
Yi Zheng ◽  
Beiwen Li
Keyword(s):  
Real Time ◽  
High Speed ◽  
Structured Light ◽  
3D Scanning ◽  
In Situ Monitoring ◽  
Physical Contact ◽  
Fringe Projection ◽  
Depth Information ◽  
Projection Technique

Abstract In-situ inspection has drawn many attentions in manufacturing due to the importance of quality assurance. With the rapid growth of additive manufacturing technology, the importance of in-line/in-situ inspections has been raised to a higher level due to many uncertainties that could occur during an additive printing process. Given this, having accurate and robust in-situ monitoring can assist corrective actions for a closed-loop control of a manufacturing process. Contact 3D profilometers such as stylus profilometers or coordinate measuring machines can achieve very high accuracies. However, due to the requirement for physical contact, such methods have limited measurement speeds and may cause damage to the tested surface. Thus, contact methods are not quite suitable for real-time in-situ metrology. Non-contact methods include both passive and active methods. Passive methods (e.g., focus variation or stereo vision) hinges on image-based depth analysis, yet the accuracies of passive methods may be impacted by light conditions of the environment and the texture quality of the surface. Active 3D scanning methods such as laser scanning or structured light are suitable for instant quality inspection due to their ability to conduct a quick non-contact 3D scan of the entire surface of a workpiece. Specifically, the fringe projection technique, as a variation of the structured light technique, has demonstrated significant potential for real-time in-situ monitoring and inspection given its merits of conducting simultaneous high-speed (from 30 Hz real-time to kilohertz high speeds) and high accuracy (tens of μm) measurements. However, high-speed 3D scanning methods like fringe projection technique are typically based on triangulation principle, meaning that the depth information is retrieved by analyzing the triangulation relationship between the light emitter (i.e., projector), the image receiver (i.e., camera) and the tested sample surface. Such measurement scheme cannot reconstruct 3D surfaces where large geometrical variations are present, such as a deep-hole or a stair geometry. This is because large geometrical variations will block the auxiliary light used in the triangulation based methods, which will resultantly cause a shadowed area to occur. In this paper, we propose a uniaxial fringe projection technique to address such limitation. We measured a stair model using both conventional triangulation based fringe projection technique and the proposed method for comparison. Our experiment demonstrates that the proposed uniaxial fringe projection technique can perform high-speed 3D scanning without shadows appearing in the scene. Quantitative testing shows that an accuracy of 35 μm can be obtained by measuring a step-height object using the proposed uniaxial fringe projection system.

In Situ Additive Manufacturing Process Monitoring With an Acoustic Technique: Clustering Performance Evaluation Using K-Means Algorithm

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Hossein Taheri ◽  
Lucas W. Koester ◽  
Timothy A. Bigelow ◽  
Eric J. Faierson ◽  
Leonard J. Bond
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Clustering Algorithm ◽  
In Situ Monitoring ◽  
Process Conditions ◽  
Layer By Layer ◽  
Acoustic Signatures ◽  
Acoustic Technique

Additive manufacturing (AM) is based on layer-by-layer addition of materials. It gives design flexibility and potential to decrease costs and manufacturing lead time. Because the AM process involves incremental deposition of materials, it provides unique opportunities to investigate the material quality as it is deposited. Development of in situ monitoring methodologies is a vital part of the assessment of process performance and understanding of defects formation. In situ process monitoring provides the capability for early detection of process faults and defects. Due to the sensitivity of AM processes to different factors such as laser and material properties, any changes in aspects of the process can potentially have an impact on the part quality. As a result, in-process monitoring of AM is crucial to assure the quality, integrity, and safety of AM parts. There are various sensors and techniques that have been used for in situ process monitoring. In this work, acoustic signatures were used for in situ monitoring of the metal direct energy deposition (DED) AM process operating under different process conditions. Correlations were demonstrated between metrics and various process conditions. Demonstrated correlation between the acoustic signatures and the manufacturing process conditions shows the capability of acoustic technique for in situ monitoring of the additive manufacturing process. To identify the different process conditions, a new approach of K-means statistical clustering algorithm is used for the classification of different process conditions, and quantitative evaluation of the classification performance in terms of cohesion and isolation of the clusters. The identified acoustic signatures, quantitative clustering approach, and the achieved classification efficiency demonstrate potential for use in in situ acoustic monitoring and quality control for the additive manufacturing process.

scholarly journals Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): Overhang Part X4

2020 ◽  
Vol 125 ◽  
Author(s):  
Brandon Lane ◽  
Ho Yeung
Keyword(s):  
Computed Tomography ◽  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Three Dimensional ◽  
Ex Situ ◽  
Web Page ◽  
X Ray ◽  
X Ray Computed

This document provides details on the files available in the dataset “Overhang Part X4” pertaining to a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on June 28, 2019. The files include the input command files, materials data, in-situ process monitoring data, and metadata. This data is one of a set of “AMMT Process Monitoring Datasets”, as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (NIST). Ex-situ part characterization data, including X-ray computed tomography (XCT) measurements, will be provided as it is made available. Readers should refer to the AMMT datasets web page for updates.

scholarly journals A sample cell for the study of enzyme-induced carbonate precipitation at the grain-scale and its implications for biocementation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jennifer Zehner ◽  
Anja Røyne ◽  
Pawel Sikorski
Keyword(s):  
Real Time ◽  
Laser Scanning Microscopy ◽  
In Situ Monitoring ◽  
Confocal Laser ◽  
Precipitation Process ◽  
Successful Implementation ◽  
Detailed Knowledge ◽  
Carbonate Precipitation ◽  
Sample Cell

AbstractBiocementation is commonly based on microbial-induced carbonate precipitation (MICP) or enzyme-induced carbonate precipitation (EICP), where biomineralization of $$\text {CaCO}_{3}$$ CaCO 3 in a granular medium is used to produce a sustainable, consolidated porous material. The successful implementation of biocementation in large-scale applications requires detailed knowledge about the micro-scale processes of $$\text {CaCO}_{3}$$ CaCO 3 precipitation and grain consolidation. For this purpose, we present a microscopy sample cell that enables real time and in situ observations of the precipitation of $$\text {CaCO}_{3}$$ CaCO 3 in the presence of sand grains and calcite seeds. In this study, the sample cell is used in combination with confocal laser scanning microscopy (CLSM) which allows the monitoring in situ of local pH during the reaction. The sample cell can be disassembled at the end of the experiment, so that the precipitated crystals can be characterized with Raman microspectroscopy and scanning electron microscopy (SEM) without disturbing the sample. The combination of the real time and in situ monitoring of the precipitation process with the possibility to characterize the precipitated crystals without further sample processing, offers a powerful tool for knowledge-based improvements of biocementation.

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