The Study of Vision Inspection on Material Extrusion Process with Image Feature Analysis

• Main Authors: Bo-HanLee, 李柏翰
• Other Authors: Wei-Hsiang Lai
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/358xxb

碩士 === 國立成功大學 === 航空太空工程學系 === 107 === Material Extrusion (ME) is one of processes for additive manufacturing, and it’s the most popular one. The definition of this process is heating plastic material and then extruding from 0.4mm nozzle to constantly stack layer by layer so that the shape designed in advance can be formed. However, if there is a flaw during the process, it’ll get bigger than bigger and then end up with printing failure. Therefore, the main goal for this thesis is to solve this problem for ME machinery with automated optical inspection (AOI) system. The process is that camera captures the front field of view first. After that, the human machine interface (HMI) will show the image processed by the algorithm designed to track the nozzle’s position and define the region of interest as known as ROI, which would be extracted the image features to build the databases. When the databases are large enough, It shall use it to train SVM models as the classifier deployed on the HMI to identify if the component produced during the manufacturing time is defective or not and then transfer the corresponding G-code to make the 3D printer automated. The results of the experiment in this research show that the AOI system can completely detect the production situation (success and stack failure) for 3D printer with dark material PLA, also is capable to detect totally the non-extrusion state with a condition that the difference between the height of the nozzle and the printed component is about 7 to 10 mm.