Direct Write Additive Manufacturing and Characterization of Battery Electrodes with Engineered Architecture and Porosity

2020 ◽  
Vol MA2020-01 (1) ◽  
pp. 78-78
Author(s):  
Amjad S Almansour ◽  
Alastair Gorven ◽  
Mrityunjay Singh ◽  
Donald A. Dornbusch
Open Ceramics ◽  
2021 ◽  
pp. 100165
Author(s):  
Sergey N. Golubev ◽  
Olga Yu. Kurapova ◽  
Ivan Yu. Archakov ◽  
Vladimir G. Konakov

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

Author(s):  
Arash Alex Mazhari ◽  
Randall Ticknor ◽  
Sean Swei ◽  
Stanley Krzesniak ◽  
Mircea Teodorescu

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.


2011 ◽  
Author(s):  
Martin Freitag ◽  
Kang-Hoon Choi ◽  
Manuela Gutsch ◽  
Christoph Hohle ◽  
Reinhard Galler ◽  
...  
Keyword(s):  

Author(s):  
John C. Steuben ◽  
Athanasios P. Iliopoulos ◽  
John G. Michopoulos

A wide variety of scientific and engineering activities require the use of testing machines in order to acquire data regarding the response of materials subjected to mechanical loads. This is particularly applicable to the domain of Additive Manufacturing (AM), where mechanical qualification is essential. Such machinery should be capable of applying loads at required levels and exhibit high mechanical stiffness. Accurate force, displacement, and strain measurements are also required. As a consequence, such testing machines are typically very costly. In the present paper we introduce the Open Uniaxial Test Machine (OpenUTM) project, aimed at providing a low-cost (less than $2500.00) material testing hardware/software framework. This paper will focus on the engineering design and hardware aspects of the OpenUTM project, with particular attention paid to the use of an electrohydraulic actuator (EHA) to provide test loads. A full bill of materials and drawings package is provided, in order to enable the use of the OpenUTM framework by research groups with minimal machine tooling. We introduce several case studies demonstrating the successful use of the OpenUTM frame in AM research efforts, including the testing and characterization of AM polymers and ceramics. We conclude with discussion of the software aspects of the OpenUTM framework, which will be elaborated upon in a follow-up paper (part two). We also present a series of potential avenues towards the improvement of the OpenUTM frame in future hardware iterations.


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