Electrochemical Endpoint Determination and Machine-Learning Prediction of Pickling Time for Hot-Rolled Automotive High-Strength Steel.

Accurate determination and prediction of pickling time are essential for preventing under-pickling and over-pickling and for improving the surface quality of hot-rolled high-strength steel. In this study, an electrochemical endpoint detection method combined with hybrid machine-learning models was developed to predict the pickling time of hot-rolled automotive high-strength steel. The variation in open-circuit potential during hydrochloric-acid pickling was monitored using an electrochemical workstation, and a potential-derivative near-zero method was proposed to determine the completion of oxide-scale removal. Based on repeated experiments under ten representative process conditions, a potential-derivative threshold of -5 × 10-4 V/s was adopted as the operational criterion for identifying the pickling endpoint. The effects of oxide-scale thickness, HCl concentration, pickling temperature, and accelerator concentration on pickling time were subsequently investigated. To describe the nonlinear relationship between these variables and pickling time, BP, GA-BP, ELM, and PSO-ELM regression models were established. The 48-observation dataset was evaluated using leakage-free grouped nested six-fold cross-validation repeated ten times, with all observations from the same strip group kept within the same fold. Among the investigated models, PSO-ELM exhibited the best prediction performance, achieving R2 = 0.85 ± 0.05, MAE = 7.27 ± 1.01 s, MAPE = 0.14 ± 0.02, and RMSE = 9.65 ± 1.48 s. These quantities are regression-performance statistics and are not interpreted as the percentage prediction accuracy. The proposed endpoint criterion and regression framework provide a laboratory-scale basis for data-driven pickling-time estimation within the investigated material and process ranges, and broader industrial application requires validation using larger multi-grade production datasets.