A Generalized Method for In-Process Defect Detection in Friction Stir Welding

• Published in: Journal of Manufacturing and Materials Processing Vol. 6; no. 4; p. 80
• Main Authors: Hunt, Johnathon B., Mazzeo, Brian A., Sorensen, Carl D., Hovanski, Yuri
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Aluminum; Artificial intelligence; Automobile industry; Compliance; Cost control; Defects; force-based; Fourier transforms; Friction stir welding; Methods; Neural networks; non-destructive evaluation; non-destructive testing; Nondestructive testing; Process parameters; Scrap; Speed limits; Welding parameters
• ISSN: 2504-4494; 2504-4494
• DOI: 10.3390/jmmp6040080

Friction stir welding (FSW) is an advantageous solid-state joining process that is suitable for many materials in multiple industries. In an industrial setting, manufacturers are actively seeking faster welding speeds to increase throughput. Increasing welding speed limits the size of defect-free parameter windows, which may increase the frequency of defects. The push for faster welding speeds emphasizes the need for economical non-destructive evaluation (NDE) for FSW, like any other type of welding. This work introduces a generalized defect detection method that recognizes the stochastic nature of the FSW process, and that can be generally applied to FSW of a material across a dynamic range of process parameters and welding conditions. When applied to aluminum friction stir-welded blanks at speeds ranging from 1500 to 3000 mm/min with varying ranges of tool tilts, the methodology proved 100% effective at positive detection when defects were present with zero scrap rate. Furthermore, additional development demonstrated the proposed stochastic approach can be used to detect the spatial location of a defect within a weld with 94% detection accuracy and a 4.2% scrap rate.