arXiv subjects

Tae-Hyun Kim

Publications and source records attributed to Tae-Hyun Kim.

3 recordsLinked to original sources

arXiv2023

Automation of Trimming Die Design Inspection by Zigzag Process Between AI and CAD Domains

Quality control in the manufacturing industry has improved with the use of artificial intelligence (AI). However, the manual inspection of trimming die designs, which is time-consuming and prone to errors, is still done by engineers. This study introduces an automatic design inspection system for automobile trimming dies by integrating AI modules and computer-aided design (CAD) software. The AI modules replace engineers' judgment, and the CAD software carries out operations requested by the AI modules. The inspection process involves a zigzag interaction between the AI modules and CAD software, enabling one-click operation without expert intervention. The AI modules are CAD-independent and data-efficient, making them adaptable to other CAD software. They achieve high performance even with limited training data, with an average length measurement error of only 2.4%. The inspection time is reduced to approximately one-fifth of the time required for manual inspection by experts.

math.NA↗

arXiv2021

Product Inspection Methodology via Deep Learning: An Overview

In this work, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. Also we explain entire steps for building a deep learning-based inspection system in great detail. Second, we address connection schemes that efficiently link the deep learning models to the product inspection systems. Finally, we propose an effective method that can maintain and enhance the deep learning models of the product inspection system. It has good system maintenance and stability due to the proposed methods. All the proposed methods are integrated in a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compared and analyzed the performance of methods in various test scenarios.

cs.LG↗

arXiv2004

Quantitative Approach to Intensity-Modulated Radiation Therapy Quality Assurance Based on Film Dosimetry and Optimization

To accurately verify the dose of intensity-modulated radiation therapy (IMRT), we have used a global optimization method to investigate a new dose-verification algorithm. In practical application of this quality assurance (QA) procedure, verification of the dose using calculated and measured dose distributions involves a subtle problem in the region of high dose gradient. Consideration of systematic errors shows that the large dose differences in high-dose-gradient regions are due to the unexpected shift of measuring devices. We have proposed an optimization algorithm to correct this error, and an optimization method to minimize the average dose difference has been used in this study. The relationship between the dose-verification procedure and the applied optimization algorithm is explained precisely. Optimization dramatically reduced the difference between measured and calculated dose distributions in all cases investigated. The obtained results support the relevance of our explanations for the problem in the high-dose-gradient region. We have described this dose-verification procedure for IMRT and intensity-modulated radiosurgery. Through this study we have also developed an intuitive reporting method that is statistically reasonable.

physics.med-ph↗