In situ quality monitoring in direct energy deposition process using co-axial process zone imaging and deep contrastive learning
Published: 21 February 2023| Version 1 | DOI: 10.17632/t295fmckbk.1
Contributors:
Vigneashwara Pandiyan, Cui Di, Sergey Shevchik, Kilian WasmerDescription
The dataset consists of six categories that covers the process map of DED process. The dataset consist of around 65,000 images that are labelled into 6 categories. The images correspond to DED process zone captured co-axially The categories are function of linear laser energy deposited. The folder is already split into Train and Test.
Files
Institutions
Empa Thun Standort
Categories
Machine Learning Algorithm, Directed Energy Deposition, Process Monitoring, Advanced Manufacturing