Abstract
Rapid advances in manufacturing technologies have spurred a tremendous focus on automation and flexibility in smart manufacturing ecosystems. The needs of customers require these ecosystems to be capable of handling product variability in a prompt, reliable and cost-effective way that expose a high degree of flexibility. A critical bottleneck in addressing product variability in a factory is the manual design of manufacturing processes for new products that heavily relies on the domain experts. This is not only a tedious and time-consuming task but also error-prone. Our method supports the domain experts by generating manufacturing processes for the new products by learning the manufacturing knowledge from the existent processes that are designed for similar products to the new products. We have successfully applied our approach in the gas turbine manufacturing domain, which resulted in a significant decrease of time and effort and an increase of quality in the final process design.
Funded by the Austrian Science Fund (FWF) Elise Richter programme under agreement V 569-N31 (PRAIS).
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Notes
- 1.
A path graph is a graph in which the nodes can be listed in an order \(t_1, t_2, \ldots , t_n\) such that the edges are \((t_i, t_{i+1})\) where \(i = 1, 2, \ldots , n - 1\).
- 2.
A path in a graph is a sequence of edges which connect a sequence of vertices that are all distinct from one another.
- 3.
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Havur, G., Haselböck, A., Cabanillas, C. (2019). Automated Multi-perspective Process Generation in the Manufacturing Domain. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_8
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