In-context learning versus trained models: generative AI and machine learning for surface roughness prediction in dry Ti-6Al-4V turning

Abstract

Surface roughness Ra is a critical quality indicator in dry turning of Ti-6Al-4V titanium alloy, a material widely used in aerospace and biomedical applications. Empirical powerlaw models remain the industrial standard but cannot represent interaction effects or non-linear responses. Machine learning offers greater flexibility, yet its deployment is constrained by small experimental datasets and the need for rigorous validation protocols. Large language models represent an alternative predictive paradigm, potentially encoding implicit knowledge of machining physics that may support quantitative prediction through in-context learning alone. This work compares five predictive models, comprising two conventional mathematical approaches (Power Law and RSM) and three machine learning models (SVR, Gaussian Process Regression, and XGBoost), against three LLMs (Claude Sonnet 4.5, GPT-4o-mini, and Gemini 3 Flash Preview) for Ra prediction in Ti-6Al- 4V turning. A Central Composite Design with k = 3 factors and two carbide inserts of distinct nose radii yielded N = 38 observations. The conventional and ML models were evaluated under nested LOOCV with 5-fold grid search where applicable. LLMs were accessed via API at temperature zero under two prompting strategies, few-shot and chain-of-thought, across three independent runs, with results reported as mean ± standard deviation. Claude Sonnet 4.5 in few-shot mode achieved the best overall performance (RMSE = 0.288 ± 0.016 μm, R2 = 0.985), outperforming SVR, the best trained model (RMSE = 0.341 μm), by 16% without task-specific training or fine-tuning. The effect of prompting strategy was model dependent: few-shot prompting outperformed chain-ofthought for Claude and GPT, whereas Gemini CoT achieved a substantially lower mean RMSE than Gemini FewShot but exhibited markedly greater run-to-run variability. This variability was especially pronounced for Gemini CoT, whose RMSE ranged from 0.31 to 0.99 μm across identical executions. The results show that a capable LLM supplied with well-structured experimental demonstrations can surpass optimised trained models for Ra prediction without task-specific training, while also demonstrating that predictive performance and stability depend strongly on both model architecture and prompting strategy.