Big Data Analytics for Scheduling and Machining

  • Lihui WangEmail author
  • Xi Vincent Wang


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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Production EngineeringKTH Royal Institute of TechnologyStockholmSweden

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