Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing

Abstract

Minor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni), chromium (Cr), and titanium (Ti) in stainless steel. For data pre-processing, cubic spline interpolation and wavelet threshold transform algorithms were used to perform baseline removal and denoising. The results show that this set of pre-processing methods can effectively improve the signal-to-noise ratio, remove the baseline of spectral baseline, reduce the average relative error, and reduce relative standard deviation of BP-ANN predictions. It indicates that BP-ANN combined with pre-processing methods has promising applications for the determination of Ni, Cr, and Ti in stainless steel with LIBS and improves prediction accuracy and stability.

Keywords

• laser-induced breakdown spectroscopy
• back-propagation artificial intelligence network
• stainless steel
• data pre-processing