Automated Quality Inspection Control Station For Ceramic Furnace Disk Using Image Detection Techniques

Abstract The primary focus of this study is to develop the inspection quality control station by applying image processing techniques. The goal is to ensure that the inspection of ceramic furnace disks is within the standards of superiority. Additionally, the aim is to minimize human error in the inspection process by using this proposed station to work parallel with the expert inspector or replace. This research utilized image processing technology, specifically employing edge detection and thresholding techniques using the Python programming language along with OpenCV, to analyses and identify defects in ceramic furnace disk products. The hardware of the proposed prototype has been developed. The proposed inspection algorithm has been integrated with the hardware and operates together. The experiments were conducted using the Ceramic furnace disks from the case study company, which has industrial experiences more than 15 years in the business of ceramic. The test was performed in 130 trials on 13 ceramic furnace disks. There were 119 correct detections and 11 errors. The results indicated that the developed system could accurately identify defects on ceramic furnace disks during inspection with an overall system accuracy of 91.54%