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Big Data Analytics for Scheduling and Machining

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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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Authors and Affiliations

  1. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Lihui Wang & Xi Vincent Wang

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  1. Lihui Wang
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  2. Xi Vincent Wang
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Correspondence to Lihui Wang .

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Wang, L., Wang, X.V. (2018). Big Data Analytics for Scheduling and Machining. In: Cloud-Based Cyber-Physical Systems in Manufacturing . Springer, Cham. https://doi.org/10.1007/978-3-319-67693-7_14

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

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