scholarly journals Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network

2020 ◽  
Vol 10 (2) ◽  
pp. 545 ◽  
Author(s):  
Wenyuan Cui ◽  
Yunlu Zhang ◽  
Xinchang Zhang ◽  
Lan Li ◽  
Frank Liou
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Quality Inspection ◽  
Weight Decay ◽  
Metal Additive Manufacturing ◽  
Low Efficiency ◽  
The Impact ◽  
Metal Additive

Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in the AM industry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry.

Convolutional neural network-based inspection of metal additive manufacturing parts

2019 ◽  
Vol 25 (3) ◽  
pp. 530-540 ◽  
Author(s):  
Binbin Zhang ◽  
Prakhar Jaiswal ◽  
Rahul Rai ◽  
Paul Guerrier ◽  
George Baggs
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Online Monitoring ◽  
Quality Inspection ◽  
Content Type ◽  
Metal Additive Manufacturing ◽  
Part Quality ◽  
Metal Additive

Purpose Part quality inspection is playing a critical role in the metal additive manufacturing (AM) industry. It produces a part quality analysis report which can be adopted to further improve the overall part quality. However, the part quality inspection process puts heavy reliance on the engineer’s background and experience. This manual process suffers from both low efficiency and potential errors and, therefore, cannot meet the requirement of real-time detection. The purpose of this paper is to look into a deep neural network, Convolutional Neural Network (CNN), towards a robust method for online monitoring of AM parts. Design/methodology/approach The proposed online monitoring method relies on a deep CNN that takes a real metal AM part’s images as inputs and the part quality categories as network outputs. The authors validate the efficacy of the proposed methodology by recognizing the “beautiful-weld” category from material CoCrMo top surface images. The images of “beautiful-weld” parts that show even hatch lines and appropriate overlaps indicate a good quality of an AM part. Findings The classification accuracy of the developed method using limited information of a small local block of an image is 82 per cent. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent. Originality/value A real-world data set of high resolution images of ASTM F75 I CoCrMo-based three-dimensional printed parts (Top surface images with magnification 63×) annotated with categories labels. Development of a CNN-based classification model for the supervised learning task of recognizing a “beautiful-weld” AM parts. The classification accuracy using the full image and the ensemble of model outputs is 100 per cent.

A deep neural network for classification of melt-pool images in metal additive manufacturing

2018 ◽  
Vol 31 (2) ◽  
pp. 375-386 ◽  
Author(s):  
Ohyung Kwon ◽  
Hyung Giun Kim ◽  
Min Ji Ham ◽  
Wonrae Kim ◽  
Gun-Hee Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Additive Manufacturing ◽  
Deep Neural Network ◽  
Metal Additive Manufacturing ◽  
Melt Pool ◽  
Metal Additive

scholarly journals On Residual Stress Development, Prevention, and Compensation in Metal Additive Manufacturing

Materials ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 255 ◽  
Author(s):  
Kevin Carpenter ◽  
Ali Tabei
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Measurement Techniques ◽  
Complex Geometries ◽  
Quality Inspection ◽  
Metal Additive Manufacturing ◽  
Computational Costs ◽  
Metal Additive

One of the most appealing qualities of additive manufacturing (AM) is the ability to produce complex geometries faster than most traditional methods. The trade-off for this advantage is that AM parts are extremely vulnerable to residual stresses (RSs), which may lead to geometrical distortions and quality inspection failures. Additionally, tensile RSs negatively impact the fatigue life and other mechanical performance characteristics of the parts in service. Therefore, in order for AM to cross the borders of prototyping toward a viable manufacturing process, the major challenge of RS development must be addressed. Different AM technologies contain many unique features and parameters, which influence the temperature gradients in the part and lead to development of RSs. The stresses formed in AM parts are typically observed to be compressive in the center of the part and tensile on the top layers. To mitigate these stresses, process parameters must be optimized, which requires exhaustive and costly experimentations. Alternative to experiments, holistic computational frameworks which can capture much of the physics while balancing computational costs are introduced for rapid and inexpensive investigation into development and prevention of RSs in AM. In this review, the focus is on metal additive manufacturing, referred to simply as “AM”, and, after a brief introduction to various AM technologies and thermoelastic mechanics, prior works on sources of RSs in AM are discussed. Furthermore, the state-of-the-art knowledge on RS measurement techniques, the influence of AM process parameters, current modeling approaches, and distortion prevention approaches are reported.

Optimizing Quality Inspection and Control in Powder Bed Metal Additive Manufacturing: Challenges and Research Directions

2021 ◽  
Vol 109 (4) ◽  
pp. 326-346
Author(s):  
Santa Di Cataldo ◽  
Sara Vinco ◽  
Gianvito Urgese ◽  
Flaviana Calignano ◽  
Elisa Ficarra ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Quality Inspection ◽  
Research Directions ◽  
Powder Bed ◽  
Metal Additive Manufacturing ◽  
And Control ◽  
Metal Additive

scholarly journals Review: The Impact of Metal Additive Manufacturing on the Aerospace Industry

Metals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1286 ◽  
Author(s):  
Shahir Mohd Yusuf ◽  
Samuel Cutler ◽  
Nong Gao
Keyword(s):  
Additive Manufacturing ◽  
Outer Space ◽  
Aerospace Industry ◽  
Space Vehicles ◽  
Metal Additive Manufacturing ◽  
Wide Range ◽  
Metallic Parts ◽  
The Impact ◽  
Research Stage ◽  
Metal Additive

Metal additive manufacturing (AM) has matured from its infancy in the research stage to the fabrication of a wide range of commercial functional applications. In particular, at present, metal AM is now popular in the aerospace industry to build and repair various components for commercial and military aircraft, as well as outer space vehicles. Firstly, this review describes the categories of AM technologies that are commonly used to fabricate metallic parts. Then, the evolution of metal AM used in the aerospace industry from just prototyping to the manufacturing of propulsion systems and structural components is also highlighted. In addition, current outstanding issues that prevent metal AM from entering mass production in the aerospace industry are discussed, including the development of standards and qualifications, sustainability, and supply chain development.

scholarly journals Hybrid metal additive manufacturing: A state–of–the-art review

2021 ◽  
Vol 2 ◽  
pp. 100032
Author(s):  
J.P.M. Pragana ◽  
R.F.V. Sampaio ◽  
I.M.F. Bragança ◽  
C.M.A. Silva ◽  
P.A.F. Martins
Keyword(s):  
Additive Manufacturing ◽  
State Of The Art ◽  
Metal Additive Manufacturing ◽  
Metal Additive
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