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Title:Matematični model ocene negotovosti pri merjenju ravnosti
Authors:ID Gusel, Andrej (Author)
ID Mudronja, Vedran (Mentor) More about this mentor... New window
Files:.pdf DR_Gusel_Andrej_2009.pdf (4,80 MB)
MD5: E928E650FBCA8B44076C7BE902625BC1
PID: 20.500.12556/dkum/b51ba824-122d-4826-8451-6d79a7e3c6e3
 
Language:Slovenian
Work type:Dissertation
Organization:FS - Faculty of Mechanical Engineering
Abstract:Meritve ravnosti predstavljajo sredstvo za vrednotenje ravnosti merilnih plošč in ostalih površin, ki služijo kot osnova pri meritvah lege in oblike. Kljub poznavanju postopkov, metod in merilne opreme za merjenje ravnosti ter kljub vedno novim metodam, ki poskušajo poenostaviti postopek, se vedno pojavljajo isti problemi. En glavnih problemov so vplivni faktorji, ki učinkujejo na meritev. Te vplivne faktorje, še bolj pa njihove učinke na meritev, moramo poznati in biti sposobni ovrednotiti, saj predstavljajo osnovo za določanje negotovosti meritve ravnosti. Vplivni faktorji so na splošno sicer znani, manj znani pa so njihovi vplivi na meritev, kar vpliva na odločitev, katere upoštevati in kako, katere pa lahko zanemarimo oziroma preprečimo. Prej ali slej se soočimo tudi s pomanjkljivostmi obstoječih metod. Večino meritev ravnosti izvajamo po metodi Union Jack, ki poleg vrste prednosti prinaša tudi nekaj pomanjkljivosti. Glavna pomanjkljivost je, da mreža že v osnovi bolj slabo pokrije obravnavano površino. Bolj groba mreža res pohitri postopek merjenja, vendar pa se zato pokritost površine še dodatno zmanjša, s tem pa se nam lahko iz obravnave izmuzne katero od odstopanj površine, to pa seveda vpliva na rezultat in negotovost. Zdi se, da bi za bolj drobne nepravilnosti potrebovali bolj gosto mrežo, za večja odstopanja pa bi zadoščala bolj groba mreža. Če torej glede na obliko površine določamo gostoto merilne mreže, gostota merilne mreže pa spet pogojuje stopnjo pokritosti površine, moramo najti odgovor na vprašanje, ki sledi: kako sta povezani oblika površine in negotovost meritve? Ali bi bilo res možno (in smiselno), da bi za različne oblike površin vnaprej definirali različne merilne mreže? Pri odgovoru na te izzive si pomagamo z metodo Monte Carlo. Model meritve služi za osnovo algoritma, s katerim je mogoče preko serije simulacij določiti negotovost meritve. Rezultati za različne merilne mreže, prilagojene obliki površine, kažejo presenetljive izsledke.
Keywords:model negotovosti ravnosti, simulacija ravnosti Monte Carlo, simulacija merilne plošče
Place of publishing:Maribor
Publisher:A. Gusel]
Year of publishing:2009
PID:20.500.12556/DKUM-10245 New window
UDC:531.717.8
COBISS.SI-ID:245713920 New window
NUK URN:URN:SI:UM:DK:DWH0BBDQ
Publication date in DKUM:19.05.2009
Views:4287
Downloads:310
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FS
:
GUSEL, Andrej, 2009, Matematični model ocene negotovosti pri merjenju ravnosti [online]. Doctoral dissertation. Maribor : A. Gusel. [Accessed 1 April 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=10245
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Secondary language

Language:English
Title:Mathematical model of uncertainty evaluation in flatness measurement
Abstract:Flatness measurement is a mean for flatness evaluations of measurement plates and other surfaces, which represent a basis for measurements of position and form. Procedures, measurement equipment and methods are well known, and despite all the efforts to simplify the procedures, we face the very same problems as before. Large part of these problems are the impact factors, which influence the measurement. In order to be able to determine the measurement uncertainty, we must be able to evaluate these factors and their effects on the measurement itself. All impact factors are well known, less known is their impact on the measurement, and a resulting decision, which factors are taken into consideration and how, and which can be ignored or even prevented. Sooner or later we face the deficiencies of existing methods. Usually, flatness measurements are performed using a Union Jack method, which brings a lot of advantages, together with some disadvantages. The main disadvantage of this method is only average coverage of the surface we measure. A coarser grid does improve the duration of the measurement, but as the grid gets coarser, the coverage factor gets smaller. With that, it is possible to neglect some of possible surface defects, which affect both the result and uncertainty. It seems as a denser grid would be needed for finer defects on the surface, and a coarser grid would be sufficient for larger irregularities. If the coverage of the surface is based on the density of the grid, and the grid density could be determined based on the shape of the surface, we must ask ourselves the following question – what is the relation between the shape of the surface and the measurement uncertainty? Would it be possible and would it make any sense to define suitable measurement grids according to the type of the surface? To answer all these challenges, a Monte Carlo method comes of use. A measurement model serves as a basis for the algorithm, which is used to determine the measurement uncertainty over a series of simulations. Results for different types of measurement grid, adapted to the shape of the surface, are astonishing.
Keywords:flatness uncertainty model, Monte Carlo flatness simulation, surface plate simulation


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