Cloud-Enabled Distributed Process Planning

  • Lihui WangEmail author
  • Xi Vincent Wang


Today, the dynamic market requires manufacturing firms to possess a high degree of adaptability to deal with shop-floor uncertainties. Specifically targeting SMEs active in the metal cutting sector who normally deal with intensive process planning problems, researchers have tried to address the subject. Among reported solutions, Cloud-DPP elaborates a two-layer distributed adaptive process planning based on function block technology and cloud concept. One of the challenges of companies is to machine as many part features as possible in a single setup on a single machine. Nowadays, multi-tasking machines are widely used due to their various advantages, such as reduced setup times and increased machining accuracy. However, they also possess programming challenges because of their complex configuration and multiple machining functions. This chapter reports the latest state of the design and implementation of Cloud-DPP methodology to support parts with a combination of milling and turning features, and process planning for multi-tasking machining centres with special functionalities to minimise the total number of setups. This chapter covers representation of machining states and part transfer functionality, support of multi-tasking machines in adaptive setup merging, development of special function blocks to handle sub-setups and transitions, and finally generation of function block networks for the merged setups. A case study is also included to validate the reported methodology.


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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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