March 3, 2026

Comprehensive Quantitative Quality Assessment of Thermal Cut Sheet Edges using Convolutional Neural Networks

Key Points

A comprehensive and accurate assessment of edge quality is achieved using image-based evaluations, indicating significant improvements.
Primary findings indicate better predictions of edge quality metrics like roughness and burr height compared to traditional methods.
The method employs convolutional neural networks for analyzing images of thermal cut edges for quality assessment.
This approach highlights the potential for fast and effective evaluation of manufacturing processes in various applications.

Abstract

In this study, we present a novel holistic approach to assess the quality of thermal cut edges using images of the cut edges. Using deep learning techniques, we estimate quality criteria such as roughness, edge slope tolerance, groove tracking, and burr height. Our approach significantly surpasses the current state of the art in evaluating thermal cut edges using 2D images. To the best of our knowledge, this study presents the first image-based groove tracking evaluation for thermal cut edges. Our results show that a comprehensive, accurate, and fast prediction of edge quality can be effectively achieved by implementing a simple image acquisition system combined with a convolutional neural network (CNN).

Comprehensive Quantitative Quality Assessment of Thermal Cut Sheet Edges using Convolutional Neural Networks

Key Points

Abstract

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