13–15 Sept 2021
Online
Europe/Berlin timezone

AdaPipe: A Recommender System for Adaptive Computation Pipelines in Cyber-Manufacturing Computation Services

15 Sept 2021, 16:00
30m
Room 1

Room 1

Other/special session/invited session Advancements in Industrial Data Science

Speaker

Ran Jin

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

Primary authors

Dr Xiaoyu Chen (Virginia Tech) Ran Jin

Presentation materials

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