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Cloud-DPP for distributed process planning of mill-turn machining operations
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0002-3517-3636
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.ORCID iD: 0000-0001-8679-8049
2017 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 47, p. 76-84Article in journal (Refereed) Published
Abstract [en]

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 proposed 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 reducing setup times and increasing part accuracy. However, they also possess programming challenges because of their complex configuration and multiple machining functions. This paper reports the latest state of 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 centers with special functionalities to minimize the number of setups. The contributions of this work are: 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 network for the merged setups. The developed prototype is validated through a case study.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 47, p. 76-84
Keywords [en]
Multi-tasking machine, Mill-turn part, Function block-based adaptive distributed process planning, Machine mode
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-243497DOI: 10.1016/j.rcim.2016.11.007ISI: 000403512400011Scopus ID: 2-s2.0-85007553327OAI: oai:DiVA.org:kth-243497DiVA, id: diva2:1287023
Conference
25th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), 2015, Univ Wolverhampton, Fac Sci & Engn, Wolverhampton, ENGLAND
Note

QC 20190208

Available from: 2019-02-08 Created: 2019-02-08 Last updated: 2019-02-08Bibliographically approved

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Givehchi, MohammadHaghighi, AzadehWang, Lihui

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