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dc.contributor.authorCoppel, Ricardo
dc.contributor.authorAbellán Nebot, José Vicente
dc.contributor.authorSiller, Héctor R.
dc.contributor.authorRodríguez, Ciro A.
dc.contributor.authorGuedea, Federico
dc.date.accessioned2016-11-25T09:50:40Z
dc.date.available2016-11-25T09:50:40Z
dc.date.issued2016-06
dc.identifier.citationCOPPEL, Ricardo, et al. Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches. The International Journal of Advanced Manufacturing Technology, 2015, p. 1-20.ca_CA
dc.identifier.urihttp://hdl.handle.net/10234/164704
dc.description.abstractNowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high-quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micro-manufacturing. In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: (1) a monitoring sensor system composed of a dynamometer, (2) an estimation module with Artificial Neural Networks models, (3) an optimization module with evolutionary optimization algorithms, and (4) a CNC interface module. In order to operate in a nearly real-time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as particle swarm optimization (PSO), genetic algorithms (GA), and simulated annealing (SA) in terms of accuracy, precision, and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3 and 29 % in comparison with conservative and high-production strategies, respectively.ca_CA
dc.format.extent19 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.relation.isPartOfThe International Journal of Advanced Manufacturing Technology June 2016, Volume 84, Issue 9ca_CA
dc.rights© Springer-Verlag London 2015ca_CA
dc.subjectMicro-millingca_CA
dc.subjectHardened steelsca_CA
dc.subjectAdaptive controlca_CA
dc.subjectIntelligent machining systemsca_CA
dc.titleAdaptive control optimization in micro-milling of hardened steels-evaluation of optimization approachesca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1007/s00170-015-7807-6
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://link.springer.com/article/10.1007/s00170-015-7807-6ca_CA


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