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Merilni sistem za kontrolo dimenzijskih odstopanj ohišja žarometa
ID HABJAN, JURE (Avtor), ID Špiclin, Žiga (Mentor) Več o mentorju... Povezava se odpre v novem oknu

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Izvleček
Magistrska naloga obsega zasnovo, teoretično ozadje in realizacijo merilnega sistema za kontrolo dimenzijskih odstopanj ohišja avtomobilskega žarometa v proizvodnji liniji. Ohišje je poleg leče najbolj pomemben element v tolerančni verigi (sestav neodvisnih dimenzij in geometrijskih lastnosti odstopov od imenskih mer, torej toleranc, ki so medsebojno povezane v sklenjeno verigo, zaporedje) žarometa. Ker se dimenzije ohišja tekom vgradnje notranjih komponent in samega vpenjanja na montažno stojalo lahko spreminjajo, smo zasnovali merilni sistem, ki omogoča zajem meritev direktno v procesu spajanja leče. Sistem je brezkontakten in implementiran na že obstoječe montažno stojalo žarometa in je sestavljen iz laserskih senzorjev razdalje, potrebne periferije in programske kode za zajem in pošiljanje podatkov. Podatke pridobljene z merilnim sistemom smo nadalje shranili v izdelano bazo podatkov za analizo. Za potrebe analize sta bila ustvarjena dva različna algoritma napovedovanja merskih točk leče žarometa. Prvi algoritem temelji na specifični izpeljavi togih preslikav, drugi pa na uporabi umetnih nevronskih mrež. Algoritma sta pomembna zato, ker lahko preko merskih točk na leči kontroliramo geometrijsko točnost žarometa. Uvodni del zajema teorijo toleranc in kontrole procesa. Omenjeno ozadje je potrebno za razumevanje in kasnejše načrtovanje sistema za opisano kontrolo. Prav tako opišemo podane koordinatne sisteme in korelacije med njimi za potrebe razumevanja in nadaljnje obdelave podatkov. Nenazadnje se poglobimo tudi v tolerančno verigo našega primera kontrole dimenzij žarometa, kjer opišemo posamezne, nam relevantne korake v izdelavi žarometa. Osrednji, empirični del, je fokusiran na izbiro konkretnega projekta in žarometa. Opisano je vrednotenje, prav tako izbira in implementacija senzorjev za naš merilni sistem. Za tem so pojasnjeni še zajem in obdelava podatkov zajetih z izbrano senzoriko, v kateri so zavzete vse metode računanja in vključena programska oprema. Predstavimo tudi Excel bazo podatkov, ustvarjeno s Python jezikom, ki je namenjena smiselni hrambi in prikazu rezultatov. Opisani sta še teoriji za oba algoritma napovedovanja merskih točk leče, en z uporabo togih geometrijskih preslikav in drug z uporabo umetnih nevronskih mrež. Na koncu so opisno in grafično predstavljeni podatki pridobljeni z razvitim sistemom. Analiziran je hitro-vzorčni zajem procesa lepljenja leče na žaromet in enkratne meritve vsakega ohišja, zajete skozi daljše obdobje nekaj mesecev. Na realnem primeru tako prikažemo pomembnost hrambe in analize podatkov za detekcijo napak v proizvodnem procesu. Opisana sta tudi testiranje in analiza obeh algoritmov za napovedovanje merskih točk leče, med seboj primerjana in validirana z že obstoječo referenčno merilno metodo v podjetju. Navsezadnje, je bila za analizo pridobljenih podatkov ustvarjenega sistema in izbranega algoritma, ustvarjena izboljšava na montažnem stojalu ohišja žarometa in s slednjim izboljšana dimenzijska stabilnost žarometa. Predstavljene so tudi možne izboljšave in korist uporabe prikazanega merskega sistema za podjetje.

Jezik:Slovenski jezik
Ključne besede:žaromet, merske točke, referenčni sistem, dimenzijska kontrola, toge geometrijske preslikave, umetna nevronska mreža, Python
Vrsta gradiva:Magistrsko delo/naloga (mb22)
Organizacija:FE - Fakulteta za elektrotehniko
Leto izida:2020
Datum objave v RUL:12.06.2020
Število ogledov:565
Število prenosov:203
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Sekundarni jezik

Jezik:Angleški jezik
Naslov:Measurement system for dimensional quality control of car headlamps
Izvleček:
This master's thesis covers the design, theoretical background and realization of a measuring system for the control of dimensional deviations of a car headlamp housing in a production line. Other than the car lens, the housing is the most important part in the tolerance chain (composition of independent dimensions and geometrical properties of deviations from nominal dimensions, i.e. tolerances, that are interconnected in a closed chain, sequence) of the cars headlamp. Since the dimensions of the housing can change during the installation of the internal components and during the clamping on the mounting stand, we designed a measuring system, that allows you to capture measurements directly in the process of joining the lens. The system uses a non-contact measuring method and is implemented on an existing headlamp mounting stand and consists of laser distance sensors, necessary peripherals and program code for capturing and sending data. The data obtained with the measuring system is stored in a database for further analysis. For the purpose of the analysis, two different algorithms for predicting the measuring points of the headlamp lens were created. The first algorithm is based on the specific derivation of rigid geometric mappings, and the second on the use of artificial neural networks. The algorithms are important because with the help of measuring points on the lens we can control the geometric accuracy of the headlamp. In the introductory part, we describe the theory of tolerances and process control. This background is necessary for understanding and planning the system for such control. We also describe the given coordinate systems and correlations between them for the needs of understanding and further data processing. We also delve into our examples tolerance chain and dimension control of the headlamp, where we describe the individual relevant steps in the headlamp manufacturing process. The central or empirical part is focused on a specific project and headlamp. We describe the evaluation, selection and implementation of sensors for our measuring system. After this part, the capturing and processing of data obtained by sensors is explained including all calculation methods, software and hardware. We also present an Excel based database created with the Python language, which is intended for storing and display of results. Theories for both methods of predicting lens measurement points, one using rigid geometric mappings and the other with neural networks, are also described. In the conclusion we descriptively and graphically present data obtained with the developed system. We analyze a fast capturing of the process of gluing the lens to the headlamp and single captured "online" data over a longer period (several months). In this paragraph, the importance of data storage and analysis for fault detection in the production process are shown on a real-world example. We also describe the testing and analysis of both methods for predicting the measurement points of the lens, compare them and validate them with the already created reference measurement method in the company. Finally, by analyzing the obtained data of the created system and the chosen method, we create an improvement on the headlamp housing mounting stand and improve the dimensional stability of the headlamp. We also describe possible improvements and the benefit of using the presented measurement system for the company.

Ključne besede:headlamp, measuring points, reference system, dimensional control, rigid geometric mappings, artificial neural network, Python

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