Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning pp 211-214 | Cite as
Recapitulation
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Abstract
Concluding, the presented product state concept allows to identify relevant state drivers of complex manufacturing systems. The concept is able to utilize complex, diverse and high-dimensional data sets which often occur in manufacturing applications. This fits nicely with current initiatives like ‘Industrie 4.0’, ‘Cyber Physical Systems’ in Europe and the ‘Industrial Internet’ and ‘Advanced Manufacturing Partnership’ in the US as well as the growing area of Big Data research. It can be safely said that in the near future, the amount of data derived from manufacturing operations will increase due to these developments. This offers both opportunities and challenges for manufacturing companies and manufacturing research. With the developed concept, the increasing data streams can be analyzed efficiently and applicable results can be derived. The analysis results present a direct benefit in form of the most important process parameters and state characteristics, the state drivers, of the manufacturing system. These can be directly utilized in, e.g., quality monitoring and advanced process control.
Keywords
Feature Selection Manufacturing System State Driver Important Process Parameter Develop ConceptReferences
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