A Stitching Algorithm for Automated Surface Inspection of Rotationally Symmetric Components

TL;DR

This paper introduces a feature-based stitching algorithm for creating accurate, distortion-free images of rotationally symmetric components, enabling efficient surface inspection and condition monitoring in industrial applications.

Contribution

It presents a novel stitching process that mimics line scan camera systems using feature-based methods, applicable to various rotationally symmetric parts.

Findings

Successfully validated on a worn ball screw drive spindle.

Developed new metrics for evaluating stitching quality.

Demonstrated broad applicability to industrial surface inspection.

Abstract

This paper provides a novel approach to stitching surface images of rotationally symmetric parts. It presents a process pipeline that uses a feature-based stitching approach to create a distortion-free and true-to-life image from a video file. The developed process thus enables, for example, condition monitoring without having to view many individual images. For validation purposes, this will be demonstrated in the paper using the concrete example of a worn ball screw drive spindle. The developed algorithm aims at reproducing the functional principle of a line scan camera system, whereby the physical measuring systems are replaced by a feature-based approach. For evaluation of the stitching algorithms, metrics are used, some of which have only been developed in this work or have been supplemented by test procedures already in use. The applicability of the developed algorithm is not only limited to machine tool spindles. Instead, the developed method allows a general approach to the surface inspection of various rotationally symmetric components and can therefore be used in a variety of industrial applications. Deep-learning-based detection Algorithms can easily be implemented to generate a complete pipeline for failure detection and condition monitoring on rotationally symmetric parts.