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Document type:
Zeitschriftenaufsatz 
Author(s):
Weiß, Iris; Vogel-Heuser, Birgit; Trunzer, Emanuel; Kruppa, Simon 
Title:
Unsupervised Product Quality Monitoring in Hydraulic Metal Powder Presses using a Minimal Sample of Sensor and Actuator Data 
Abstract:
Machine learning and artificial intelligence provide methods and algorithms to take advantage of sensor and actuator data in automated production systems. Product quality monitoring is one promising application for data-driven modeling, in particular in cases where the quality parameters cannot be measured with reasonable effort. This is the case, e.g., for defects such as cracks of workpieces in hydraulic metal powder presses. However, challenges such as an enormous variety of products with di...    »
 
Journal title:
ACM Transactions on Internet Technology 
Year:
2020