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2018 | OriginalPaper | Buchkapitel

14. Big Data Analytics for Scheduling and Machining

verfasst von : Lihui Wang, Xi Vincent Wang

Erschienen in: Cloud-Based Cyber-Physical Systems in Manufacturing

Verlag: Springer International Publishing

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Abstract

In modern manufacturing industries, defect prediction and prevention are the key features. In this context, this chapter introduces a big data analytics based approach to scheduling and machining. In order to minimise machining errors in advance, a big data analytics based fault prediction approach is presented first for shop-floor scheduling, where machining tasks, machining resources, and machining processes are represented by data attributes. Based on the available data on the shop floor, the potential fault/error patterns, referring to machining errors, machine faults, maintenance states etc., are mined to discover unsuitable scheduling arrangements before machining as well as upcoming errors during machining. Targeting a global machining optimisation, this chapter then presents a big data analytics based optimisation method for machining process planning. Within the context, the machining factors are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine condition, machining process, machining result, etc. The problem of machining optimisation is treated as a statistical problem.

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Vorheriges Kapitel Product Tracking and WEEE Management
Nächstes Kapitel Outlook of Cloud, CPS and IoT in Manufacturing
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Metadaten
Titel
Big Data Analytics for Scheduling and Machining
verfasst von
Lihui Wang
Xi Vincent Wang
Copyright-Jahr
2018
Buch
Cloud-Based Cyber-Physical Systems in Manufacturing

Print ISBN: 978-3-319-67692-0

Electronic ISBN: 978-3-319-67693-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-67693-7_14

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