Production and characterization of Ti6Al4V/CaP nanocomposite powder for powder-based additive manufacturing systems

2021 ◽  
Vol 386 ◽  
pp. 319-334
Author(s):  
Sayed Shahab Sayedain ◽  
Aliakbar Ekrami ◽  
Mohsen Badrossamay
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Nanocomposite Powder

scholarly journals Evaluating the Quality Surface Performance of Additive Manufacturing Systems: Methodology and a Material Jetting Case Study

Materials ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 995 ◽  
Author(s):  
Razvan Udroiu ◽  
Ion Braga ◽  
Anisor Nedelcu
Keyword(s):  
Surface Roughness ◽  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Statistical Design ◽  
Statistical Design Of Experiments ◽  
Systems Methodology ◽  
Study Results ◽  
Materials Used

The performance characterization of the manufacturing processes for additive manufacturing (AM) systems is a significant task for their standardization and implementation in the industry. Also, there is a large diversity of materials used in different AM processes. In the present paper, a methodology is proposed to evaluate, in different directions, the performance of an AM process and material characterization in terms of surface quality. This methodology consists of eight steps, based on a new surface inspection artifact and basic artifact orientations. The proposed artifact with several design configurations fits different AM systems sizes and meets the needs of customers. The effects of main factors on the surface roughness of up-facing platens of the artifacts are investigated using the statistical design of experiments. The proposed methodology is validated by a case study focused on PolyJet material jetting technology. Samples are manufactured of photopolymer resins and post-processed. Three factors (i.e., artifact orientation, platen orientation, and finish type) are considered for the investigation. The case study results show that the platen orientation, finish type, and their interaction have a significant influence on the surface roughness (Ra). The best Ra roughness results were obtained for the glossy finish type in the range of 0.5–4 μm.

scholarly journals MODEL-BASED DESIGN OF AM COMPONENTS TO ENABLE DECENTRALIZED DIGITAL MANUFACTURING SYSTEMS

2021 ◽  
Vol 1 ◽  
pp. 2127-2136
Author(s):  
Olivia Borgue ◽  
John Stavridis ◽  
Tomas Vannucci ◽  
Panagiotis Stavropoulos ◽  
Harry Bikas ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Data Generation ◽  
Production Networks ◽  
Model Based ◽  
Manufacturing Technologies ◽  
Am Processes ◽  
Final Consumer

AbstractAdditive manufacturing (AM) is a versatile technology that could add flexibility in manufacturing processes, whether implemented alone or along other technologies. This technology enables on-demand production and decentralized production networks, as production facilities can be located around the world to manufacture products closer to the final consumer (decentralized manufacturing). However, the wide adoption of additive manufacturing technologies is hindered by the lack of experience on its implementation, the lack of repeatability among different manufacturers and a lack of integrated production systems. The later, hinders the traceability and quality assurance of printed components and limits the understanding and data generation of the AM processes and parameters. In this article, a design strategy is proposed to integrate the different phases of the development process into a model-based design platform for decentralized manufacturing. This platform is aimed at facilitating data traceability and product repeatability among different AM machines. The strategy is illustrated with a case study where a car steering knuckle is manufactured in three different facilities in Sweden and Italy.

scholarly journals Characterization of a High Temperature Ceramics Produced via Two-Step Additive Manufacturing

Open Ceramics ◽  
2021 ◽  
pp. 100165
Author(s):  
Sergey N. Golubev ◽  
Olga Yu. Kurapova ◽  
Ivan Yu. Archakov ◽  
Vladimir G. Konakov
Keyword(s):  
High Temperature ◽  
Additive Manufacturing

scholarly journals Fatigue characterization of Titanium Ti-6Al-4V samples produced by Additive Manufacturing

2016 ◽  
Vol 2 ◽  
pp. 3168-3176 ◽  
Author(s):  
Victor Chastand ◽  
Astrid Tezenas ◽  
Yannick Cadoret ◽  
Philippe Quaegebeur ◽  
Wilson Maia ◽  
...  
Keyword(s):  
Additive Manufacturing

scholarly journals Automated Testing and Characterization of Additive Manufacturing (ATCAM)

2021 ◽  
Author(s):  
Arash Alex Mazhari ◽  
Randall Ticknor ◽  
Sean Swei ◽  
Stanley Krzesniak ◽  
Mircea Teodorescu
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Cell System ◽  
Automated Testing ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Machine Calibration

AbstractThe sensitivity of additive manufacturing (AM) to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. As a step toward integrating testing and characterization into the AM process while reducing cost, we propose the automated testing and characterization of AM (ATCAM). ATCAM is configured for fused deposition modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. We test ATCAM’s ability to distinguish the quality of three PLA feedstock at differing price points by generating and comparing 3000 dynamic coupons in 10 repetitions of 100 coupon cycles per material. ATCAM correlated the quality of each feedstock and visualized fatigue of in situ actuators over each testing cycle. Three ML algorithms were then compared, with Gradient Boost regression demonstrating a 71% correlation of dynamic coupons to their parent feedstock and provided confidence for the quality of AM data ATCAM generates.

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