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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 \u22125 \u00d7 10\u22124 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 \u00b1 0.05, MAE = 7.27 \u00b1 1.01 s, MAPE = 0.14 \u00b1 0.02, and RMSE = 9.65 \u00b1 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.<\/jats:p>","DOI":"10.3390\/ma19183974","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:23:17Z","timestamp":1790011397000},"page":"3974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Electrochemical Endpoint Determination and Machine-Learning Prediction of Pickling Time for Hot-Rolled Automotive High-Strength Steel"],"prefix":"10.3390","volume":"19","author":[{"given":"Zhou","family":"Xu","sequence":"first","affiliation":[{"name":"School of Electrical and Automation, Wuhu Vocational Technical University, Wuhu 241006, China"},{"name":"Opening Project of Anhui Key Laboratory of Electric 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