Friction Stir Welding Tool Condition Prediction Using Vibrational Analysis Through Machine Learning – A Review

Abstract

Abstract Friction stir welding (FSW) is a relatively new solid-state joining process. This joining technique is energy efficient, environment friendly, and versatile. In particular, it can be used to join high-strength aerospace aluminum alloys and other metallic alloys that are hard to weld by conventional fusion welding. FSW is considered to be the most significant development in metal joining in a decade. Recently, friction stir processing (FSP) was developed for micro structural modification of metallic materials. In this review article, the current state of understanding and development of the FSW and FSP tool process parameters are addressed. To identify the tool parameters, it is necessary to monitor the tool condition. Diagnosis the recognition of the nature and cause of a certain phenomenon. It is generally used to determine cause and effect of a problem. Machine fault diagnosis, a field of finding faults arising in machines. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, Thermal imaging, Oil particle analysis etc. Then these data’s are processed using methods like spectral analysis, wavelet analysis, wavelet transform, Short term fourier transform, high resolution spectral analysis, waveform analysis etc. The results of this analysis are used in a root cause failure analysis in order to determine the original cause of the fault. This paper presents a brief review about one such application known as machine learning for the friction stir welding tool monitoring.