Speaker
Description
The industrial cyber-physical systems (ICPS) will accelerate the transformation of offline data-driven modeling to fast computation services, such as computation pipelines for prediction, monitoring, prognosis, diagnosis, and control in factories. However, it is computationally intensive to adapt computation pipelines to heterogeneous contexts in ICPS in manufacturing.
In this paper, we propose to rank and select the best computation pipelines to match contexts and formulate the problem as a recommendation problem. The proposed method Adaptive computation Pipelines (AdaPipe) considers similarities of computation pipelines from word embedding, and features of contexts. Thus, without exploring all computation pipelines extensively in a trial-and-error manner, AdaPipe efficiently identifies top-ranked computation pipelines. We validated the proposed method with 60 bootstrapped data sets from three real manufacturing processes: thermal spray coating, printed electronics, and additive manufacturing. The results indicate that the proposed recommendation method outperforms traditional matrix completion, tensor regression methods, and a state-of-the-art personalized recommendation model.
Special/invited session
QSR/INFORMS invited session
| Keywords | Computation pipeline, computing in cyber–physical systems, recommender system, smart factories |
|---|