Robust machine learning prediction of surface roughness in SKD61 turning

Abstract

Accurate and reliable prediction of surface roughness (Ra) is essential for intelligent machining of hardened tool steels under limited-data conditions. This study investigates the influence of cutting and geometric parameters on Ra during the external turning of SKD61 steel using a Taguchi L27 experimental design. The investigated variables included cutting speed, feed rate (f), depth of cut, tool nose radius (r), and workpiece diameter. A comparative machine learning framework was developed to evaluate four prediction models, namely artificial neural network, extreme learning machine (ELM), support vector regression, and random forest regression, with polynomial regression serving as the benchmark. Model performance was first evaluated using repeated 5-fold cross-validation and subsequently assessed using an independent external dataset comprising previously unseen intermediate parameter combinations within the investigated parameter domain. Taguchi analysis and ANOVA identified f and r as the dominant factors affecting Ra. Among the investigated models, ELM achieved the highest prediction accuracy, yielding R2 = 0.9979 ± 0.0006 and MAE = 0.0397 ± 0.0065 µm during repeated validation, together with R2 = 0.9371 and MAE = 0.0845 µm on the independent external dataset. These results support the potential effectiveness of the proposed robustness-oriented evaluation framework for ML-assisted Ra prediction under limited-data machining conditions.