Work

Metrological validation of a deep learning pipeline for in-line detection and dimensional quantification of three-dimensional surface defects

Year: 2025

Type: article

Abstract: Defect detection is critical in manufacturing processes such as casting, machining, and additive manufacturing, where imperfections can impair part functionality and reliability. This work proposes a deep learning-based methodology for the detection and dimensional inspection of three-dimensional surface defects. The approach integrates deep learning trained on pre-labelled data and applied to two-dimensional deviation maps derived from laser-based measurements, and a skeletonization algorithm to estimate defect dimensions. Accuracy is ensured by metrological validation using reference measurements from a multisensor coordinate measuring machine. Applied to die-cast components, the framework demonstrates robust performance, offering a reliable tool for integration into real-world quality control workflows.

Source: CIRP journal of manufacturing science and technology

DOI: 10.1016/j.cirpj.2025.12.002

Authors: Sofia Catalucci, Enrico Savio

Institution: University of Padua

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,950

Funder: European Partnership on Metrology