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Combining Dynamic Machining Feature With Function Blocks for Adaptive Machining
KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
2016 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 13, no 2, 828-841 p.Article in journal (Refereed) PublishedText
Abstract [en]

Feature-based technologies are widely researched for manufacturing automation. However, in current feature models, features once defined remain constant throughout the whole manufacturing lifecycle. This static feature model is inflexible to support adaptive machining when facing frequent changes to manufacturing resources. This paper presents a new machining feature concept that facilitates responsive changes to the dynamics of machining features in 2.5/3D machining. Basic geometry information for feature construction of complex parts with various intersecting features is represented as a set of meta machining features (MMF). Optimum feature definition is generated adaptively by choosing optimum merging strategies of MMFs according to the capabilities of the selected machine tool, cutter, and cutting parameters. A composite function block for dynamic machining feature modelling is designed with Basic Machining Feature Function Block, Meta Machining Feature Extraction Function Block and Feature Interpreter Function Block. Once changes of the selected machining resources occur, they are informed as input events and machining features are then updated automatically and adaptively based on the event-driven model of function blocks. An example is provided to demonstrate the feasibility and benefits of the developed methodology.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. Vol. 13, no 2, 828-841 p.
Keyword [en]
Adaptive machining, dynamic feature, function block
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-188094DOI: 10.1109/TASE.2015.2409294ISI: 000374442300037ScopusID: 2-s2.0-84926336554OAI: oai:DiVA.org:kth-188094DiVA: diva2:937415
Note

QC 20160615

Available from: 2016-06-15 Created: 2016-06-03 Last updated: 2016-06-15Bibliographically approved

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Wang, Lihui
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