Purpose: Dry machining of Ti–6Al–4V presents significant challenges due to its low thermal conductivity, high strength, and strong adhesion at the tool–workpiece interface. These characteristics limit the accuracy of empirical surface-roughness models. This study aims to present and validate an AI-Optimized, Physics-Based Feature Engineering framework capable of predicting surface roughness with high fidelity. Methods: A Central Composite Design with 19 experiments was conducted by varying cutting speed, feed rate, and depth of cut. Surface roughness and cutting forces were measured, and physics-derived features such as specific cutting energy, strain rate, and shear stress were engineered. Multiple Machine Learning models, including boosting ensembles, neural networks, and support vector regression, were trained and evaluated through repeated cross-validation and benchmark comparisons. Results: The proposed Physics-Based framework demonstrated superior performance over classical methods. Specifically, the optimized XGBoost model achieved the highest predictive accuracy ( $$\varvec{R^2 = 0.98}$$ ), reducing the Mean Absolute Error (MAE) to 0.018 $$\mu$$ m compared to 0.23 $$\mu$$ m for Response Surface Methodology. The Neural Network also showed robust generalization ( $$\varvec{R^2 \approx 0.92}$$ ). The framework effectively captured complex thermomechanical behaviors characteristic of Ti–6Al–4V machining, delivering micrometric-precision predictions. Conclusion: The proposed framework provides a reliable tool for optimizing surface quality in dry machining of Ti–6Al–4V. By enabling precise prediction of roughness, it supports the selection of optimal cutting parameters for aerospace and biomedical components.

This study presents a systematic comparison of six machine learning algorithms for surface roughness ( $$\varvec{Ra}$$ ) prediction in dry turning of Ti-6Al-4V across two nose radii ( $$\varvec{r}_{\varvec{\varepsilon }} \varvec{= 0.4}$$ mm and $$\varvec{r}_{\varvec{\varepsilon }} \varvec{= 0.8}$$ mm) using a central composite design with 38 experiments. The algorithms evaluated are Support Vector Regression, Random Forest, XGBoost, Gaussian Process Regression, Extreme Learning Machine, and a Lichtenberg-Optimised ELM in which the Lichtenberg Algorithm replaces random weight initialisation with a structured fractal search. All models are trained on $$\varvec{\log (Ra)}$$ and validated by leave-one-out cross-validation under two feature configurations: process parameters only and process parameters augmented with cutting force measurements. GPR achieves the highest accuracy ( $$\varvec{R}^{\varvec{2}} \varvec{= 0.922}$$ ); under the present conditions, process parameters alone prove sufficient — force signals degrade four of six models due to collinearity with feed rate, confirmed by variance inflation factors exceeding 10 for $$\varvec{\bar{F}}_{\varvec{f}}$$ and $$\varvec{\bar{F}}_{\varvec{p}}$$ . A polynomial regression baseline reaches $$\varvec{R}^{\varvec{2}} \varvec{= 0.920}$$ , matching GPR; ML advantages lie in uncertainty quantification, localised nonlinearity capture, and robustness to broader conditions. LA-ELM records the largest force-induced gain ( $$\varvec{\Delta R}^{\varvec{2}} \varvec{= +0.074}$$ ), reaching $$\varvec{R}^{\varvec{2}} \varvec{= 0.895}$$ and surpassing Random Forest and XGBoost. Results confirm that the Lichtenberg Algorithm is an effective weight optimisation strategy for ELM in small-dataset manufacturing regression.

O polipirrol (PPy) é um polímero condutor que tem sido investigado para proteção de metais oxidáveis contra corrosão. Seu uso apresenta algumas vantagens como baixa toxicidade, estabilidade química e facilidade de síntese química e eletroquímica. No presente trabalho, filmes de polipirrol foram eletrodepositados por cronoamperometria, em meio aquoso contendo ácido p-tolueno sulfônico. Os resultados dos ensaios de polarização potenciodinâmica, em meio agressivo de cloreto de sódio, mostraram que o potencial de corrosão foi deslocado para valores mais positivos quando o metal foi recoberto pelo polipirrol, indicando proteção anódica. Além disso, a Microscopia de Varredura Eletrônica de varredura (MEV) indicou a formação de filmes de polipirrol compactos e homogêneos sobre a liga metálica, que não foram degradados mesmo expostos por sete dias ao meio corrosivo contendo íons de cloreto.