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Experimental Characterisation of Tool Hardness Evolution Under Consideration of Process Relevant Cyclic Thermal and Mechanical Loading During Industrial Forging Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Combining Process Mining and Machine Learning for Lead Time Prediction in High Variance Processes

Authors : M. Welsing, J. Maetschke, K. Thomas, A. Gützlaff, G. Schuh, S. Meusert

Published in: Production at the leading edge of technology

Publisher: Springer Berlin Heidelberg

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Abstract

Machine learning offers a high potential for the prediction of manufacturing lead times. In practical operations the lack of defined processes and high-quality input data are a major obstacle for the use of machine learning. The method of process mining creates a better transparency of such workflows and enriches related data. This paper develops a method, which combines the benefits of machine learning and process mining with the goal of high accuracy lead time prediction. The method is focused on high variance processes and verified with a case study containing real industrial data from heavy engine assembly processes.

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previous chapter Autoconfiguration of a Vibration-Based Anomaly Detection System with Sparse a-priori Knowledge Using Autoencoder Networks
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Metadata
Title
Combining Process Mining and Machine Learning for Lead Time Prediction in High Variance Processes
Authors
M. Welsing
J. Maetschke
K. Thomas
A. Gützlaff
G. Schuh
S. Meusert
Copyright Year
2021
Publisher
Springer Berlin Heidelberg
Book
Production at the leading edge of technology

Print ISBN: 978-3-662-62137-0

Electronic ISBN: 978-3-662-62138-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-662-62138-7_53

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