Data Preparation

2021 ◽  
pp. 173-185
Author(s):  
Vineet Raina ◽  
Srinath Krishnamurthy
Keyword(s):  
Data Preparation

scholarly journals Review on additive hybrid- and multi-material-manufacturing of metals by powder bed fusion: state of technology and development potential

2021 ◽  
Author(s):  
M. Schneck ◽  
M. Horn ◽  
M. Schmitt ◽  
C. Seidel ◽  
G. Schlick ◽  
...  
Keyword(s):  
Review Paper ◽  
Powder Bed Fusion ◽  
Process Chain ◽  
Data Preparation ◽  
Powder Bed ◽  
Theoretical Approaches ◽  
Powder Deposition ◽  
Readiness Level ◽  
Manufacturing Readiness ◽  
2D And 3D

AbstractIn this review paper, the authors investigate the state of technology for hybrid- and multi-material (MM) manufacturing of metals utilizing additive manufacturing, in particular powder bed fusion processes. The study consists of three parts, covering the material combinations, the MM deposition devices, and the implications in the process chain. The material analysis is clustered into 2D- and 3D-MM approaches. Based on the reviewed literature, the most utilized material combination is steel-copper, followed by fusing dissimilar steels. Second, the MM deposition devices are categorized into holohedral, nozzle-based as well as masked deposition concepts, and compared in terms of powder deposition rate, resolution, and manufacturing readiness level (MRL). As a third aspect, the implications in the process chain are investigated. Therefore, the design of MM parts and the data preparation for the production process are analyzed. Moreover, aspects for the reuse of powder and finalization of MM parts are discussed. Considering the design of MM parts, there are theoretical approaches, but specific parameter studies or use cases are not present in the literature. Principles for powder separation are identified for exemplary material combinations, but results for further finalization steps of MM parts have not been found. In conclusion, 3D-MM manufacturing has a MRL of 4–5, which indicates that the technology can be produced in a laboratory environment. According to this maturity, several aspects for serial MM parts need to be developed, but the potential of the technology has been demonstrated. Thus, the next important step is to identify lead applications, which benefit from MM manufacturing and hence foster the industrialization of these processes.

RSSI Data Preparation for Machine Learning

2020 ◽  
Author(s):  
Dodo Zaenal Abidin ◽  
Siti Nurmaini ◽  
Reza Firsandava Malik ◽  
Erwin ◽  
Errissya Rasywir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Preparation

Latest results and computing performance of the ePLACE data preparation tool

2009 ◽  
Author(s):  
J. Gramss ◽  
R. Galler ◽  
V. Neick ◽  
A. Stoeckel ◽  
U. Weidenmueller ◽  
...  
Keyword(s):  
Data Preparation ◽  
Computing Performance
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