Results 131 to 140 of about 51,322 (331)

Fatigue response of as built DMLS processed Maraging Steel and effects of machining and heat and surface treatments [PDF]

, 2018
The main motivations for this study arise from the need for an assessment of the fatigue performance of DMLS produced Maraging Steel MS1, when it is used in the \u201cas fabricated\u201d state. The literature indicates a lack of knowledge from this point
Bogojevic, Nebojsa, Ciric-Kostic, Snezana, Croccolo, Dario, De Agostinis, Massimiliano, Fini, Stefano, Muharemovic, Nusret, Olmi, Giorgio, Robusto, Francesco, Vranic, Aleksandar   +8 more
core  

High speed laser powder bed fusion of M789 tool steel with an optimized 120 µm layer thickness approach

, 2022
Mirko Sinico, Jitka Metelková, Thibaut Dalemans, Lore Thijs, Brecht Van Hooreweder   +4 more
openalex   +1 more source

In Vitro Characterization of In Situ Alloyed Ti6Al4V(ELI)-3 at.% Cu Obtained by Laser Powder Bed Fusion [PDF]

, 2021
A.M. Vilardell, Pavel Krakhmalev, Ina Yadroitsava, Igor Yadroitsev, Natalia García‐Giralt   +4 more
openalex   +1 more source

Additive Manufacturing of Ni‐Based Thermoelectric Materials for Waste Heat Recovery

Advanced Materials Technologies, EarlyView.
Thermoelectric generators convert waste heat into electricity but are constrained by conventional fabrication. Here, laser powder bed fusion enables direct printing of customised n‐type Cu60Ni40 and p‐type Ni90Cr10 legs. The assembled module achieves 0.8% efficiency at ΔT = 419 K, demonstrating scalable additive manufacturing of eco‐friendly, design ...
Karolin Amstein, Aayush Dobhal, Heiko Reith, Ran He, Julia Kristin Hufenbach, Kornelius Nielsch, Pingjun Ying   +6 more
wiley   +1 more source

Effect of Layer Thickness and Scanning Parameters on Melt Pool Geometry and Track Continuity in Powder-Bed Arc Additive Manufacturing

Metals
Powder-bed arc additive manufacturing (PBAAM) may reduce the cost of powder-bed metal additive manufacturing and enable thicker layers than laser powder bed fusion (LPBF), but melt-track stability limits are not well established.
Arif Balci, Fatih Alibeyoglu
doaj   +1 more source

Investigation on Hydrogen Evolution Reaction Performance of Porous Electrode Prepared by Laser Powder Bed Fusion

, 2021
Haoyuan Cai, Jifei Ma, Ningxin Li, Wanqiang Li, Shunpu Li, Mingxia Qiu, Hongyu An, S. W. Zhang, Xue–Qian Li, Jiaru Chen, Sihan Lin, junbin xu, Ning Wang   +12 more
openalex   +1 more source

Shaping Carbon Nitrides for Advanced Macrostructures

Advanced Materials Technologies, EarlyView.
This review examines how carbon nitride can be shaped through a range of printing and interfacial assembly methods. By bringing together additive manufacturing and liquid–liquid structuring concepts, carbon nitride is moving beyond its traditional powder‐based photocatalyst form toward digitally designed robust macroscale architectures with high design
Simona Baluchová, Baris Kumru
wiley   +1 more source

Thermo‐Optical Analysis of Surface‐ and Volume‐Additivated Polymer Powders for Near‐Infrared Laser Powder Bed Fusion

Advanced Photonics Research
Near‐infrared laser sources have the potential to change the process dynamics in laser powder bed fusion of polymers. To take full advantage of the shorter processing laser wavelength, the addition of absorbing additives, such as carbon black ...
Alexander Sommereyns, Jochen Schmidt, Cheechau Leong, Florentin Tischer, Michael Schmidt   +4 more
doaj   +1 more source

Powder characterisation techniques and effects of powder characteristics on part properties in powder-bed fusion processes

Virtual and Physical Prototyping, 2017
Powder-bed fusion is a class of Additive Manufacturing (AM) processes that bond successive layers of powder to facilitate the creation of parts with complex geometries. As AM technology transitions from the fabrication of prototypes to end-use parts, the
Austin T. Sutton, Caitlin S. Kriewall, Ming C. Leu, Joseph W. Newkirk   +3 more
doaj   +1 more source

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source