000171664 001__ 171664
000171664 005__ 20181203022537.0
000171664 0247_ $$2doi$$a10.1080/00207540903382873
000171664 022__ $$a0020-7543
000171664 02470 $$2ISI$$a000288119200018
000171664 037__ $$aARTICLE
000171664 245__ $$aOptimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm
000171664 260__ $$bTaylor & Francis$$c2011
000171664 269__ $$a2011
000171664 336__ $$aJournal Articles
000171664 520__ $$aIn the milling of large monolithic structural components for aircraft, 70-80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a(p)), radial immersion (a(e)), feed rate (f(t)) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine-tool-part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.
000171664 6531_ $$afinite element analysis
000171664 6531_ $$afeature-based design
000171664 6531_ $$aexpert systems
000171664 6531_ $$aevolutionary computation
000171664 6531_ $$aEnd-Mills
000171664 6531_ $$aOptimization
000171664 6531_ $$aForces
000171664 6531_ $$aDeflection
000171664 6531_ $$aDesign
000171664 700__ $$aRai, Jitender K.
000171664 700__ $$aBrand, Daniel
000171664 700__ $$aSlama, Mohammed
000171664 700__ $$g107556$$aXirouchakis, Paul$$0243953
000171664 773__ $$j49$$tInternational Journal Of Production Research$$q3045-3068
000171664 909C0 $$0252295$$pLICP$$xU10361
000171664 909CO $$particle$$ooai:infoscience.tind.io:171664
000171664 917Z8 $$x182496
000171664 937__ $$aEPFL-ARTICLE-171664
000171664 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000171664 980__ $$aARTICLE