zaključna naloga Univerzitetnega študijskega programa I. stopnje Strojništvo - Razvojno raziskovalni program
Matic Fleten (Author), Marko Šimic (Mentor), Niko Herakovič (Co-mentor)

Abstract

Spremljanje kakovosti montaže izdelkov je pomemben, a za človeka zamuden proces. V dobi industrije 4.0, kjer stremimo k čim višji stopnji avtomatizacije, je pomembno, da tudi kakovost montaže lahko spremljamo ob človeški odsotnosti. Za ta namen potrebujemo merilni sistem za preverjanje kakovosti in ustreznosti sestava izdelka Raspberry Pi, ki ga na proizvodni liniji sestavlja robot. V okviru naloge smo zato zasnovali sistem strojnega vida, ki omogoča zaznavo posameznih sestavnih delov in podsestavov izbranega izdelka Raspberry Pi. Posebej smo se osredotočili na algoritem zaznavanja sestavnih delov in podsestava, ki deluje na principu barvnih karakteristik sestavnih delov in sestavljanja slik referenčnega izdelka. Referenčno sliko podsestavov in izdelka uporabimo za primerjavo realnih slik posnetih za posamezne montažne operacije za realen podsestav in izdelek. Algoritem slike analizira, ovrednoti in poda rezultat ustreznosti sestava. Izdelal se je prototip sistema strojnega vida, podani so ključni parametri in nastavitve kamere, osvetlitve in merilnega mesta. V eksperimentalnem delu smo analizirali in preverili merilne metode in algoritem.

Keywords

diplomske naloge;strojni vid;kontrola kakovosti;binarizacija;morfologija;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FS - Faculty of Mechanical Engineering
Publisher: [M. Fleten]
UDC: 681.5:004.9(043.2)
COBISS: 105890819 Link will open in a new window
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Other data

Secondary language: English
Secondary title: Machine vision system for assembly quality control of Raspberry Pi product
Secondary abstract: Quality control is an important but time-consuming process. In the age of Industry 4.0, where we strive for the highest possible level of automation, it is important that the quality of the assembly can be monitored in the absence of humans. To this end, we need a measurement system that allows us to check the quality and suitability of the assembly of the Raspberry Pi product, which is assembled by a robot in the production line. Therefore, as part of this task, we have developed a machine vision system that allows the detection of individual components and subassemblies of the selected Raspberry Pi product. In particular, we focused on the component and subassembly recognition algorithm based on the principle of component color features and compilation of images of the reference product. Reference images of subassemblies and assemblies are used as a template for comparison with real-time images of assembly operations of an actual product and its subassemblies. The image algorithm analyzes, evaluates and provides the result of the adequacy of the assembly. A prototype of the machine vision system was made, and the main parameters and settings of the camera, lighting, and measuring point are given. In the experimental part we analyzed and tested the measurement methods and the algorithm.
Secondary keywords: thesis;machine vision;thresholding;morphology;
Type (COBISS): Final paper
Study programme: 0
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za strojništvo
Pages: XV, 42 f.
ID: 15115938
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