In-situ point cloud fusion for layer-wise monitoring of additive manufacturing

2021 ◽  
Vol 61 ◽  
pp. 210-222
Author(s):  
Zehao Ye ◽  
Chenang Liu ◽  
Wenmeng Tian ◽  
Chen Kan
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud

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 Additive Manufacturing-Based In Situ Consolidation of Continuous Carbon Fibre-Reinforced Polycarbonate

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2450
Author(s):  
Andreas Borowski ◽  
Christian Vogel ◽  
Thomas Behnisch ◽  
Vinzenz Geske ◽  
Maik Gude ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Carbon Fibre ◽  
Bending Test ◽  
Thermoplastic Composites ◽  
Experimental Investigations ◽  
Material Structure ◽  
Three Point Bending ◽  
Continuous Fibre ◽  
Local Material

Continuous carbon fibre-reinforced thermoplastic composites have convincing anisotropic properties, which can be used to strengthen structural components in a local, variable and efficient way. In this study, an additive manufacturing (AM) process is introduced to fabricate in situ consolidated continuous fibre-reinforced polycarbonate. Specimens with three different nozzle temperatures were in situ consolidated and tested in a three-point bending test. Computed tomography (CT) is used for a detailed analysis of the local material structure and resulting material porosity, thus the results can be put into context with process parameters. In addition, a highly curved test structure was fabricated that demonstrates the limits of the process and dependent fibre strand folding behaviours. These experimental investigations present the potential and the challenges of additive manufacturing-based in situ consolidated continuous fibre-reinforced polycarbonate.

An investigation of in situ impregnation for additive manufacturing of thermoplastic composites

2021 ◽  
Vol 64 ◽  
pp. 972-981
Author(s):  
Daniel Kaczmarek ◽  
Daniel Walczyk ◽  
James Garofalo ◽  
Margaret Sobkowicz-Kline
Keyword(s):  
Additive Manufacturing ◽  
Thermoplastic Composites

scholarly journals OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1563
Author(s):  
Ruibing Wu ◽  
Ziping Yu ◽  
Donghong Ding ◽  
Qinghua Lu ◽  
Zengxi Pan ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Point Cloud ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Multiple Point ◽  
Icp Algorithm ◽  
Registration Algorithm ◽  
Cloud Models ◽  
Comparison Results ◽  
Wire Arc Additive Manufacturing

As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.

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
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