Weld Classification With Feature Extraction by FRESH Algorithm Based on Surface Topographical Optical Coherence Tomography Data for Laser Welding of Copper

Abstract

The topographical information of a weld seam bears information about quality relevant characteristics such as humping or spatter. Optical coherence tomography (OCT) can be used for inline scanning the weld topography coaxially mounted at a laser scanning optic. Feature extraction from this topographical information is challenging due to finding mathematical representations for the identification of relevant features. Feature extraction based on scalable hypothesis tests (FRESH) allows for feature extraction by a combination of various time series characterization methods. FRESHs feature selection is supported with an automatically configured hypothesis test and hence allows for quick extraction of significant features from sensing data in laser welding processes. In this work, a proof-of-concept is demonstrated for weld result categorization from OCT data by feature extraction using the FRESH algorithm. Changes in weld topography are characterized in a vast variety of process parameters for weld categories such as spatter, deep penetration welding, humping and heat conduction welding. As a result, a quantified separation of weld categories is possible and shows the feasibility of the FRESH algorithm for future quality assessments with different sensing technologies in laser welding.

Keywords

• Classification
• copper
• FRESH
• interferometry
• laser welding
• machine learning