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

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.

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

2020 ◽  
Vol 10 (2) ◽  
pp. 545 ◽  
Author(s):  
Wenyuan Cui ◽  
Yunlu Zhang ◽  
Xinchang Zhang ◽  
Lan Li ◽  
Frank Liou

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.


Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 959
Author(s):  
Naoko Ikeo ◽  
Hidetsugu Fukuda ◽  
Aira Matsugaki ◽  
Toru Inoue ◽  
Ai Serizawa ◽  
...  

Metal additive manufacturing is a powerful tool for providing the desired functional performance through a three-dimensional (3D) structural design. Among the material functions, anisotropic mechanical properties are indispensable for enabling the capabilities of structural materials for living tissues. For biomedical materials to replace bone function, it is necessary to provide an anisotropic mechanical property that mimics that of bones. For desired control of the mechanical performance of the materials, we propose a novel 3D puzzle structure with cube-shaped parts comprising 27 (3 × 3 × 3) unit compartments. We designed and fabricated a Co–Cr–Mo composite structure through spatial control of the positional arrangement of powder/solid parts using the laser powder bed fusion (L-PBF) method. The mechanical function of the fabricated structure can be predicted using the rule of mixtures based on the arrangement pattern of each part. The solid parts in the cubic structure were obtained by melting and solidifying the metal powder with a laser, while the powder parts were obtained through the remaining nonmelted powders inside the structure. This is the first report to achieve an innovative material design that can provide an anisotropic Young’s modulus by arranging the powder and solid parts using additive manufacturing technology.


Author(s):  
Thomas Lehmann ◽  
Dylan Rose ◽  
Ehsan Ranjbar ◽  
Morteza Ghasri-Khouzani ◽  
Mahdi Tavakoli ◽  
...  

2017 ◽  
Vol 28 ◽  
pp. 383-389 ◽  
Author(s):  
Xuewei Fang ◽  
Jun Du ◽  
Zhengying Wei ◽  
Pengfei He ◽  
Hao Bai ◽  
...  

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 867
Author(s):  
Nader Asnafi

Additive manufacturing (AM), more popularly known as 3D printing, comprises a group of technologies used to produce objects through the addition (rather than the removal) of material [...]


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