An investigation on effects of process parameters in fused-coating based metal additive manufacturing

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

scholarly journals Modelling of Microstructure Formation in Metal Additive Manufacturing: Recent Progress, Research Gaps and Perspectives

Metals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1425
Author(s):  
Dayalan R. Gunasegaram ◽  
Ingo Steinbach
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Computational Modelling ◽  
Vital Role ◽  
Formation Mechanisms ◽  
Microstructure Formation ◽  
Metal Additive Manufacturing ◽  
The Status ◽  
Am Processes ◽  
Metal Additive

Microstructures encountered in the various metal additive manufacturing (AM) processes are unique because these form under rapid solidification conditions not frequently experienced elsewhere. Some of these highly nonequilibrium microstructures are subject to self-tempering or even forced to undergo recrystallisation when extra energy is supplied in the form of heat as adjacent layers are deposited. Further complexity arises from the fact that the same microstructure may be attained via more than one route—since many permutations and combinations available in terms of AM process parameters give rise to multiple phase transformation pathways. There are additional difficulties in obtaining insights into the underlying phenomena. For instance, the unstable, rapid and dynamic nature of the powder-based AM processes and the microscopic scale of the melt pool behaviour make it difficult to gather crucial information through in-situ observations of the process. Therefore, it is unsurprising that many of the mechanisms responsible for the final microstructures—including defects—found in AM parts are yet to be fully understood. Fortunately, however, computational modelling provides a means for recreating these processes in the virtual domain for testing theories—thereby discovering and rationalising the potential influences of various process parameters on microstructure formation mechanisms. In what is expected to be fertile ground for research and development for some time to come, modelling and experimental efforts that go hand in glove are likely to provide the fastest route to uncovering the unique and complex physical phenomena that determine metal AM microstructures. In this short Editorial, we summarise the status quo and identify research opportunities for modelling microstructures in AM. The vital role that will be played by machine learning (ML) models is also discussed.

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

scholarly journals Towards the industrialization of metal additive manufacturing

2021 ◽  
Vol 18 (3) ◽  
pp. 32-37
Author(s):  
Francesca Moglia ◽  
Antonio Raspa
Keyword(s):  
Additive Manufacturing ◽  
Metal Additive Manufacturing ◽  
Metal Additive

An optimized pressure vessel made of metal additive manufacturing: Preliminary results

2021 ◽  
pp. 104434
Author(s):  
Hassan Mohamed Abdelalim Abdalla ◽  
Daniele Casagrande ◽  
Francesco De Bona ◽  
Thomas De Monte ◽  
Marco Sortino ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Pressure Vessel ◽  
Metal Additive Manufacturing ◽  
Preliminary Results ◽  
Metal Additive
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