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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). A gene selection method for cancer classification using support vector machines. Machine Learning, 46, 389–422. doi:10.1155/2012/586246.
Wuest, T., Hribernik, K., & Thoben, K. (2012). Can a product have a facebook? A new perspective on product avatars in product lifecycle management. In L. Rivest, A. Bouraz, & B. Louhichi (Eds.), Product Lifecycle Management: Towards Knowledge-Rich Enterprises. Proceedings of the 9th International Conference on Product Lifecycle Management. Montréal, Canada.
Wuest, T., Hribernik, K., & Thoben, K. (2013). Digital representations of intelligent products: Product avatar 2.0. In M. Abramovici & R. Stark, M. Abramovici & R. Stark (Eds.), Smart Product Engineering, LNPE (pp. 675–684). Berlin, Heidelberg: Springer. doi:10.1007/978-3-642-30817-8.
Wuest, T., Hribernik, K. & Thoben, K.-D. (2014). Accessing servitization potential of PLM data by applying the product avatar concept. Production Planning and Control (accepted).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Wuest, T. (2015). Recapitulation. In: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-17611-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-17611-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-17610-9
Online ISBN: 978-3-319-17611-6
eBook Packages: EngineeringEngineering (R0)