Integrated numerical modelling and deep learning for multi-layer cube deposition planning in laser aided additive manufacturing

Layer-wise 3D surface morphology information is critical for the quality monitoring and control of additive manufacturing (AM) processes. However, most of the existing 3D scan technologies are either contact or time consuming, which are not capable of obtaining the 3D surface morphology data in a real-time manner during the process. Therefore, the objective of this study is to achieve real-time 3D surface data acquisition in AM, which is achieved by a supervised deep learning-based image analysis approach. The key idea of this proposed method is to capture the correlation between 2D image and 3D point cloud, and then quantify this relationship by using a deep learning algorithm, namely, convolutional neural network (CNN). To validate the effectiveness and efficiency of the proposed method, both simulation and real-world case studies were performed. The results demonstrate that this method has strong potential to be applied for real-time surface morphology measurement in AM, as well as other advanced manufacturing processes.

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Numerical Modelling of Heat Transfer and Material Flow During Wire-Based Electron Beam Additive Manufacturing

Russian Internet Journal of Industrial Engineering ◽

10.24892/rijie/20190109 ◽

2019 ◽

Vol 7 (1) ◽

pp. 46-52

Keyword(s):

Heat Transfer ◽

Electron Beam ◽

Additive Manufacturing ◽

Numerical Modelling ◽

Material Flow

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Vision-Based Melt Pool Monitoring for Wire-Arc Additive Manufacturing Using Deep Learning Method

10.21203/rs.3.rs-1126853/v1 ◽

2021 ◽

Author(s):

Chunyang Xia ◽

Zengxi Pan ◽

Yuxing Li ◽

Huijun Li

Keyword(s):

Deep Learning ◽

Additive Manufacturing ◽

Large Scale ◽

Deposition Process ◽

Utilization Rate ◽

High Deposition Rate ◽

Promising Alternative ◽

Visual Monitoring ◽

Melt Pool ◽

Wire Arc Additive Manufacturing

Abstract Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, and robot suspend. this study proposed to apply Deep Learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM. Our study is applicable beyond WAAM and should benefit other additive manufacturing or arc welding techniques.

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Detection of Material Extrusion In-Process Failures via Deep Learning

Inventions ◽

10.3390/inventions5030025 ◽

2020 ◽

Vol 5 (3) ◽

pp. 25 ◽

Cited By ~ 1

Author(s):

Zhicheng Zhang ◽

Ismail Fidan ◽

Michael Allen

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Additive Manufacturing ◽

Problem Solving ◽

Research Team ◽

Material Consumption ◽

Growth Opportunities ◽

Process Failures ◽

Am Processes

Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.

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