Application of Automated Quality Control in Smart Factories - A Deep Learning-based Approach

• Published in: 2023 IEEE International Systems Conference (SysCon) pp. 1 - 8
• Main Authors: Mandapaka, Subbalakshmi, Diaz, Catalina, Irisson, Hasbanny, Akundi, Aditya, Lopez, Viviana, Timmer, Douglas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17-04-2023
• Subjects: Analytical models; CNN; deep learning; image classification; industry 4.0; machine vision system; Manufactured products; Object detection; object recognition; product defect detection; Prototypes; Quality control; quality control automation; smart factory; Training; Visualization
• ISSN: 2472-9647
• DOI: 10.1109/SysCon53073.2023.10131100

Industry 4.0 is the ongoing automation of conventional manufacturing and industrial applications using smart technology. Quality control (QC) is a set of procedures to ensure that a manufactured product adheres to a defined set of quality criteria or meets the requirements of the customer. Many applications within the manufacturing domain employ image-processing or machine learning systems but deep learning-based applications are rare. The goal of this project is to leverage deep learning methods for the automation of quality control. A visual QC automation application is proposed that utilizes a camera placed over a product assembly line containing 3-D printed product samples in a smart factory prototype setup for data collection. After model training, the model will perform object detection and recognition for analyzing complex free-form products and perform product dimension and surface analysis to identify the products that meet the quality control guidelines.