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Title:

A Data Driven Approach to the Online Monitoring of the Additive Manufacturing Process.

Document type:
Konferenzbeitrag
Contribution type:
Elektronisches Dokument
Author(s):
Holtmann, J.; Kiefel, D.; Neumann, S.; Stoessel, R.; Grosse, C.U.
Pages contribution:
137-144
Abstract:
Process monitoring in additive manufacturing (AM), i.e. in laser powder bed fusion (LPBF) of metal parts, has been identified as the crucial bottleneck in accelerating the AM industrialization process. To reduce the cost and time needed to produce and qualify an AM part, an online monitoring system of the manufacturing process is desirable. While the currently available systems capture a large amount of process data, they still lack the ability to interpret the acquired data adequately. In this...     »
Keywords:
Additive Manufacturing, Automated Defect Recognition, Laser Powder Bed Fusion (LPBF), Machine Learning, Neural Network, Online Monitoring, Process Monitoring
Book / Congress title:
Advanced Materials Research, Sonderausgabe zur Konferenz Materials Science and Technology of Additive Manufacturing, Vol. 1161
Date of publication:
25.03.2021
Year:
2020
E-ISBN:
1662-8985
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.4028/www.scientific.net/AMR.1161.137
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