An investigation of data fusion for defects inspection in additive manufacturing

2020 ◽  
Author(s):  
Xing Peng ◽  
Lingbao Kong
Keyword(s):  
Additive Manufacturing ◽  
Data Fusion ◽  
Defects Inspection

In-Process Data Fusion for Process Monitoring and Control of Metal Additive Manufacturing

2021 ◽  
Author(s):  
Zhuo Yang ◽  
Yan Lu ◽  
Simin Li ◽  
Jennifer Li ◽  
Yande Ndiaye ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Control ◽  
Data Fusion ◽  
Real Time ◽  
Process Data ◽  
Multi Scale ◽  
And Control ◽  
Fusion Framework ◽  
Scale Data ◽  
Metal Additive

Abstract To accelerate the adoption of Metal Additive Manufacturing (MAM) for production, an understanding of MAM process-structure-property (PSP) relationships is indispensable for quality control. A multitude of physical phenomena involved in MAM necessitates the use of multi-modal and in-process sensing techniques to model, monitor and control the process. The data generated from these sensors and process actuators are fused in various ways to advance our understanding of the process and to estimate both process status and part-in-progress states. This paper presents a hierarchical in-process data fusion framework for MAM, consisting of pointwise, trackwise, layerwise and partwise data analytics. Data fusion can be performed at raw data, feature, decision or mixed levels. The multi-scale data fusion framework is illustrated in detail using a laser powder bed fusion process for anomaly detection, material defect isolation, and part quality prediction. The multi-scale data fusion can be generally applied and integrated with real-time MAM process control, near-real-time layerwise repairing and buildwise decision making. The framework can be utilized by the AM research and standards community to rapidly develop and deploy interoperable tools and standards to analyze, process and exploit two or more different types of AM data. Common engineering standards for AM data fusion systems will dramatically improve the ability to detect, identify and locate part flaws, and then derive optimal policies for process control.

scholarly journals FPGA-Based Acceleration on Additive Manufacturing Defects Inspection

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2123
Author(s):  
Yawen Luo ◽  
Yuhua Chen
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Real Time ◽  
Scale Up ◽  
Logic Gates ◽  
Manufacturing Defects ◽  
Defects Inspection ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Defects Classification

Additive manufacturing (AM) has gained increasing attention over the past years due to its fast prototype, easier modification, and possibility for complex internal texture devices when compared to traditional manufacture processing. However, potential internal defects are occurring during AM processes, and it requires real-time inspections to minimize the costs by either aborting the processing or repairing the defect. In order to perform the defects inspection, first the defects database NEU-DET is used for training. Then, a convolution neural network (CNN) is applied to perform defects classification. For real-time purposes, Field Programmable Gate Arrays (FPGAs) are utilized for acceleration. A binarized neural network (BNN) is proposed to best fit the FPGA bit operations. Finally, for the image labeled with defects, the selective search and non-maximum algorithms are implemented to help locate the coordinates of defects. Experiments show that the BNN model on NEU-DET can achieve 97.9% accuracy in identifying whether the image is defective or defect-free. As for the image classification speed, the FPGA-based BNN module can process one image within 0.5 s. The BNN design is modularized and can be duplicated in parallel to fully utilize logic gates and memory resources in FPGAs. It is clear that the proposed FPGA-based BNN can perform real-time defects inspection with high accuracy and it can easily scale up to larger FPGA implementations.

Deep Learning-Based Data Fusion Method for In Situ Porosity Detection in Laser-Based Additive Manufacturing

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Qi Tian ◽  
Shenghan Guo ◽  
Erika Melder ◽  
Linkan Bian ◽  
Weihong “Grace” Guo
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Additive Manufacturing ◽  
Data Fusion ◽  
Infrared Camera ◽  
Fusion Method ◽  
Thermal Images ◽  
Melt Pool ◽  
Porosity Detection

Abstract Laser-based additive manufacturing (LBAM) provides unrivalled design freedom with the ability to manufacture complicated parts for a wide range of engineering applications. Melt pool is one of the most important signatures in LBAM and is indicative of process anomalies and part defects. High-speed thermal images of the melt pool captured during LBAM make it possible for in situ melt pool monitoring and porosity prediction. This paper aims to broaden current knowledge of the underlying relationship between process and porosity in LBAM and provide new possibilities for efficient and accurate porosity prediction. We present a deep learning-based data fusion method to predict porosity in LBAM parts by leveraging the measured melt pool thermal history and two newly created deep learning neural networks. A PyroNet, based on Convolutional Neural Networks, is developed to correlate in-process pyrometry images with layer-wise porosity; an IRNet, based on Long-term Recurrent Convolutional Networks, is developed to correlate sequential thermal images from an infrared camera with layer-wise porosity. Predictions from PyroNet and IRNet are fused at the decision-level to obtain a more accurate prediction of layer-wise porosity. The model fidelity is validated with LBAM Ti–6Al–4V thin-wall structure. This is the first work that manages to fuse pyrometer data and infrared camera data for metal additive manufacturing (AM). The case study results based on benchmark datasets show that our method can achieve high accuracy with relatively high efficiency, demonstrating the applicability of the method for in situ porosity detection in LBAM.

Theory and Implementation of NDT Data Fusion

1995 ◽  
Vol 6 (1) ◽  
pp. 227-236 ◽  
Author(s):  
X. E. Gros ◽  
P. Strachan ◽  
D. W. Lowden
Keyword(s):  
Data Fusion
Sign in / Sign up

Export Citation Format

Share Document