000094517 001__ 94517
000094517 005__ 20230921135434.0
000094517 0247_ $$2doi$$a10.1016/j.promfg.2019.10.042
000094517 0248_ $$2sideral$$a118481
000094517 037__ $$aART-2019-118481
000094517 041__ $$aeng
000094517 100__ $$0(orcid)0000-0001-8689-6482$$aAguado, Sergio$$uUniversidad de Zaragoza
000094517 245__ $$aAlgorithm to optimize measurement system location in a machine tool verification
000094517 260__ $$c2019
000094517 5060_ $$aAccess copy available to the general public$$fUnrestricted
000094517 5203_ $$aNowadays, machine tool accuracy is a competitive element. To improve it, machine tools are verified and compensate periodically reducing the influence of their geometric errors. As geometric errors have systematic behavior, their influence can be compensated after verification. However, verification itself is influenced by random uncertainty sources that affect verification results.
Within all influences on machine tool volumetric verification, laser tracker measurement noise is a random uncertainty source that is not usually considered. However, it should not be ignored and can be reduced through an adequate location. This paper presents an algorithm able to analyze the influence of laser tracker location, taking into consideration its specifications and machine tool characteristics. To do that, the developed algorithm provides a zone around MT to locate the measurement system using the Monte Carlo Method. Moreover, it provides the probability distribution function of laser tracker influence related to LT location zone. Therefore, if MT is used as a traceable measurement system, its uncertainty cannot be smaller than LT location uncertainty.
000094517 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T56-17R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-90106-R
000094517 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000094517 592__ $$a0.516$$b2019
000094517 593__ $$aIndustrial and Manufacturing Engineering$$c2019$$dQ2
000094517 593__ $$aArtificial Intelligence$$c2019$$dQ2
000094517 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000094517 700__ $$0(orcid)0000-0002-1093-8233$$aPérez, Pablo$$uUniversidad de Zaragoza
000094517 700__ $$0(orcid)0000-0003-4839-0610$$aAlbajez, José Antonio$$uUniversidad de Zaragoza
000094517 700__ $$0(orcid)0000-0001-7316-0003$$aSantolaria, Jorge$$uUniversidad de Zaragoza
000094517 700__ $$0(orcid)0000-0001-9617-1004$$aVelázquez, Jesús$$uUniversidad de Zaragoza
000094517 7102_ $$15002$$2515$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Ing. Procesos Fabricación
000094517 773__ $$g41 (2019), 1127-1134$$tProcedia Manufacturing$$x2351-9789
000094517 8564_ $$s1230123$$uhttps://zaguan.unizar.es/record/94517/files/texto_completo.pdf$$yVersión publicada
000094517 8564_ $$s292641$$uhttps://zaguan.unizar.es/record/94517/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000094517 909CO $$ooai:zaguan.unizar.es:94517$$particulos$$pdriver
000094517 951__ $$a2023-09-21-13:30:38
000094517 980__ $$aARTICLE