An Integrated Approach to Additive Manufacturing Simulations Using Physics Based, Coupled Multiscale Process Modeling

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Deepankar Pal ◽  
Nachiket Patil ◽  
Kai Zeng ◽  
Brent Stucker
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Integrated Approach ◽  
Geometric Optimization ◽  
Material Development ◽  
In Situ Experiments ◽  
Software Modules ◽  
Modeling Software ◽  
Experimental Process

The complexity of local and dynamic thermal transformations in additive manufacturing (AM) processes makes it difficult to track in situ thermomechanical changes at different length scales within a part using experimental process monitoring equipment. In addition, in situ process monitoring is limited to providing information only at the exposed surface of a layer being built. As a result, an understanding of the bulk microstructural transformations and the resulting behavior of a part requires rigorous postprocess microscopy and mechanical testing. In order to circumvent the limited feedback obtained from in situ experiments and to better understand material response, a novel 3D dislocation density based thermomechanical finite element framework has been developed. This framework solves for the in situ response much faster than currently used state-of-the-art modeling software since it has been specifically designed for AM platforms. This modeling infrastructure can predict the anisotropic performance of AM-produced components before they are built, can serve as a method to enable in situ closed-loop process control and as a method to predict residual stress and distortion in parts and thus enable support structure optimization. This manuscript provides an overview of these software modules which together form a robust and reliable AM software suite to address future needs for machine development, material development, and geometric optimization.

scholarly journals Process monitoring for material extrusion additive manufacturing: a state-of-the-art review

2021 ◽  
Author(s):  
Alexander Oleff ◽  
Benjamin Küster ◽  
Malte Stonis ◽  
Ludger Overmeyer
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
State Of The Art ◽  
Monitoring Systems ◽  
Research Gaps ◽  
Research Activity ◽  
Material Extrusion ◽  
Industrial Material ◽  
The Subject

AbstractQualitative uncertainties are a key challenge for the further industrialization of additive manufacturing. To solve this challenge, methods for measuring the process states and properties of parts during additive manufacturing are essential. The subject of this review is in-situ process monitoring for material extrusion additive manufacturing. The objectives are, first, to quantify the research activity on this topic, second, to analyze the utilized technologies, and finally, to identify research gaps. Various databases were systematically searched for relevant publications and a total of 221 publications were analyzed in detail. The study demonstrated that the research activity in this field has been gaining importance. Numerous sensor technologies and analysis algorithms have been identified. Nonetheless, research gaps exist in topics such as optimized monitoring systems for industrial material extrusion facilities, inspection capabilities for additional quality characteristics, and standardization aspects. This literature review is the first to address process monitoring for material extrusion using a systematic and comprehensive approach.

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.

scholarly journals Application of the STRAY statistical learning algorithm for the evaluation of in-situ process monitoring data during L-PBF additive manufacturing.

2021 ◽  
Vol 54 ◽  
pp. 250-256
Author(s):  
Aoife C. Doyle ◽  
Darragh S. Egan ◽  
Caitríona M. Ryan ◽  
Andrew C. Parnell ◽  
Denis P. Dowling
Keyword(s):  
Additive Manufacturing ◽  
Statistical Learning ◽  
Process Monitoring ◽  
Learning Algorithm ◽  
Monitoring Data

In-Situ Laser-Based Process Monitoring and In-Plane Surface Anomaly Identification for Additive Manufacturing Using Point Cloud and Machine Learning

2021 ◽  
Author(s):  
Jiaqi Lyu ◽  
Javid Akhavan Taheri Boroujeni ◽  
Souran Manoochehri
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Data Processing ◽  
Process Monitoring ◽  
Point Cloud ◽  
Plane Surface ◽  
Laser Scanner ◽  
Classification Model ◽  
Depth Image

Abstract Additive Manufacturing (AM) is a trending technology with great potential in manufacturing. In-situ process monitoring is a critical part of quality assurance for AM process. Anomalies need to be identified early to avoid further deterioration of the part quality. This paper presents an in-situ laser-based process monitoring and anomaly identification system to assure fabrication quality of Fused Filament Fabrication (FFF) machine. The proposed data processing and communication architecture of the monitoring system establishes the data transformation between workstation, FFF machine, and laser scanner control system. The data processing performs calibration, filtering, and segmentation for the point cloud of each layer acquired from a 3D laser scanner during the fabrication process. The point cloud dataset with in-plane surface depth information is converted into a 2D depth image. Each depth image is discretized into 100 equal regions of interest and then labeled accordingly. Using the image dataset, four Machine Learning (ML) classification models are trained and compared, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Hybrid Convolution AutoEncoder (HCAE). The HCAE classification model shows the best performance based on F-scores to effectively classify the in-plane anomalies into four categories, namely empty region, normal region, bulge region, and dent region.

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.

scholarly journals Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing

2016 ◽  
Vol 95 ◽  
pp. 431-445 ◽  
Author(s):  
Sarah K. Everton ◽  
Matthias Hirsch ◽  
Petros Stravroulakis ◽  
Richard K. Leach ◽  
Adam T. Clare
Keyword(s):  
Additive Manufacturing ◽  
Process Monitoring ◽  
Metal Additive Manufacturing ◽  
In Situ Metrology ◽  
Metal Additive
Sign in / Sign up

Export Citation Format

Share Document