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

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

Additive Manufacturing of Polymer Nanocomposites With In-Situ Strain Sensing Capability

2018 ◽  
Author(s):  
Mohammad Abshirini ◽  
Mohammad Charara ◽  
Yingtao Liu ◽  
Mrinal C. Saha ◽  
M. Cengiz Altan
Keyword(s):  
Additive Manufacturing ◽  
Cyclic Loading ◽  
Flexible Structures ◽  
Strain Sensing ◽  
Load Sensing ◽  
Additional Layer ◽  
Sensing Applications ◽  
3D Printed ◽  
High Flexibility

This paper presents the additive manufacturing of electrically conductive polydimethylsiloxane (PDMS) nanocomposites for in-situ strain sensing applications. A straight line of pristine PDMS was first 3D printed on a thin PDMS substrate using an in-house modified 3D printer. Carbon nanotubes (CNTs) were uniformly sprayed on top of uncured PDMS lines. An additional layer of PDMS was then applied on top of CNTs to form a thin protective coating. The 3D printed PDMS/CNT nanocomposites were characterized using a scanning electron microscope (SEM) to validate the thickness, CNT distribution, and microstructural features of the sensor cross-section. The strain sensing capability of the nanocomposites was investigated under tensile cyclic loading at different strain rates and maximum strains. Sensing experiments indicate that under cyclic loading, the changes in piezo resistivity mimic, both, the changes in the applied load and the measured material strain with high fidelity. Due to the high flexibility of PDMS, the 3D printed sensors have potential applications in real-time load sensing and structural health monitoring of complex flexible structures.

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.

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 defect detection of metal Additive Manufacturing: an integrated framework

2021 ◽  
pp. 1-1
Author(s):  
Davide Cannizzaro ◽  
Antonio Giuseppe Varrella ◽  
Stefano Paradiso ◽  
Roberta Sampieri ◽  
Yukai Chen ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Defect Detection ◽  
Metal Additive Manufacturing ◽  
Integrated Framework ◽  
Metal Additive

scholarly journals Process and Evaluation of Automated Robotic Fabrication System for In-Situ Structure Confinement

2021 ◽  
pp. 368-379
Author(s):  
B. Bala Murali Kumar ◽  
Yun Chung Hsueh ◽  
Zhuoyang Xin ◽  
Dan Luo
Keyword(s):  
Additive Manufacturing ◽  
Large Scale ◽  
Rapid Progression ◽  
Additive Process ◽  
Manufacturing Method ◽  
Robotic Fabrication ◽  
Fibre Reinforced Polymer ◽  
3D Printed ◽  
Fabrication Parameters

AbstractThe additive manufacturing process is gaining momentum in the construction industry with the rapid progression of large-scale 3D printed technologies. An established method of increasing the structural performance of concrete is by wrapping it with Fibre Reinforced Polymer (FRP). This paper proposes a novel additive process to fabricate a FRP formwork by dynamic layer winding of the FRP fabric with epoxy resin paired with an industrial scale robotic arm. A range of prototypes were fabricated to explore and study the fabrication parameters. Based on the systemic exploration, the limitations, the scope, and the feasibility of the proposed additive manufacturing method is studied for large scale customisable structural formworks.

scholarly journals The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2575
Author(s):  
Hao Wen ◽  
Chang Huang ◽  
Shengmin Guo
Keyword(s):  
Image Analysis ◽  
Defect Detection ◽  
Original Video ◽  
Sem Images ◽  
Single Defect ◽  
Defect Image ◽  
3D Printed ◽  
Metallic Additive ◽  
Almost All

Cracks and pores are two common defects in metallic additive manufacturing (AM) parts. In this paper, deep learning-based image analysis is performed for defect (cracks and pores) classification/detection based on SEM images of metallic AM parts. Three different levels of complexities, namely, defect classification, defect detection and defect image segmentation, are successfully achieved using a simple CNN model, the YOLOv4 model and the Detectron2 object detection library, respectively. The tuned CNN model can classify any single defect as either a crack or pore at almost 100% accuracy. The other two models can identify more than 90% of the cracks and pores in the testing images. In addition to the application of static image analysis, defect detection is also successfully applied on a video which mimics the AM process control images. The trained Detectron2 model can identify almost all the pores and cracks that exist in the original video. This study lays a foundation for future in situ process monitoring of the 3D printing process.

AM-Serienproduktion für die Automobilindustrie/AM series production for the automotive industry

2020 ◽  
Vol 110 (11-12) ◽  
pp. 752-757
Author(s):  
Lukas Weiser ◽  
Marco Batschkowski ◽  
Niclas Eschner ◽  
Benjamin Häfner ◽  
Ingo Neubauer ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Automotive Industry ◽  
Series Production ◽  
Production Scale ◽  
Additive Fertigung ◽  
Small Series ◽  
Start Up ◽  
3D Printed ◽  
Component Quality

Die additive Fertigung schafft neue Gestaltungsfreiheiten. Im Rahmen des Prototypenbaus und der Kleinserienproduktion kann das Verfahren des selektiven Laserschmelzens genutzt werden. Die Verwendung in der Serienproduktion ist bisher aufgrund unzureichender Bauteilqualität, langen Anlaufzeiten sowie mangelnder Automatisierung nicht im wirtschaftlichen Rahmen möglich. Das Projekt „ReAddi“ möchte eine erste prototypische Serienfertigung entwickeln, mit der additiv gefertigte Bauteile für die Automobilindustrie wirtschaftlich produziert werden können. Additive manufacturing (AM) offers new freedom of design. The selective laser-powderbed fusion (L-PBF) process can be used for prototyping and small series production. So far, it has not been economical to use it on a production scale due to insufficient component quality, long start-up times and a lack of automation. The project ReAddi aims to develop a first prototype series production to cost-effectively manufacture 3D-printed components for the automotive industry.

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