Abstract:
Surface Mount Technology (SMT) is a manufacturing process in which chips are mounted on the surface of a printed circuit board (PCB). The automatic optical inspection sys...Show MoreNotes: As originally submitted and published there was an error in this document. The authors subsequently provided the following text: "This work (Grants No. P0002857) was supported by Business for Cooperative R & D between Industry, Academy, and Research Institute funded Korea Ministry of SMEs and Startups in 2018. (Project Title: Development of SMT vision inspection system with smart teaching function based on machine learning.)" The original article PDF remains unchanged.
Metadata
Abstract:
Surface Mount Technology (SMT) is a manufacturing process in which chips are mounted on the surface of a printed circuit board (PCB). The automatic optical inspection system (AOI) has mainly used the learning-based method for the defect classification of the SMT process, and recently the CNN-based classification method has appeared. However, existing techniques do not consider the area margin of the part and uneven color distribution according to the position of the chip, so the classification accuracy decreases. In this paper, we propose a system that can extract the chip region and improve the color distribution by the input image transformation. We extract the correct chip area through vertical and horizontal projection, and the color improvement enhance the brightness value distribution of the chip image through local histogram stretching. By experimental result, we prove the performance of the proposed classification method.
Notes: As originally submitted and published there was an error in this document. The authors subsequently provided the following text: "This work (Grants No. P0002857) was supported by Business for Cooperative R & D between Industry, Academy, and Research Institute funded Korea Ministry of SMEs and Startups in 2018. (Project Title: Development of SMT vision inspection system with smart teaching function based on machine learning.)" The original article PDF remains unchanged.
Published in: 2018 IEEE 3rd International Conference on Computing, Communication and Security (ICCCS)
Date of Conference: 25-27 October 2018
Date Added to IEEE Xplore: 23 December 2018
ISBN Information: