Aug 2026· Engineering Research Express· Vol 8· 0 citations· 49 references
Physics
TL;DR
The potential effectiveness of the proposed robustness-oriented evaluation framework for ML-assisted Ra prediction under limited-data machining conditions is supported, and ELM achieved the highest prediction accuracy.
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.
The purpose of this paper is to develop machine learning (ML) models for prediction of surface roughness and cutting forces of 42CrMo4 steel in hard turning process.
A full factorial experimental design with four input parameters: cutting speed, depth of cut, feed and insert radius was used to develop ML models for predicting the performance of turning process. The backward linear regression, random forest (RF) and XGBoost were used. Also, for the linear regression model and for the best RF and XGBoost model five-fold cross validation was done to confirm that the models provide reliable generalization estimates rather than performance dependent on a single data split.
The XGBoost model demonstrates the most compact clustering of residuals with fewer large errors, indicating better overall stability and predictive consistency compared to the linear regression and RF models.
The application of different ML methods with monitoring of standardized residuals on unseen data confirms the reliability of the developed models in real application conditions.
This study provides a structured and comparative modeling framework across multiple output variables, where backward linear regression, RF and XGBoost models were developed. Several architectural and hyperparameter variations of the RF and XGBoost models were evaluated to ensure optimal configuration for each output. Also, variable influence was examined through permutation feature importance for ensemble models and statistical significance testing for linear regression, enabling interpretation and discussion of the influence of input variables on selected outputs.
Mirza Pašić, Aleksandar Živković, K. Muhamedagic et al.· Engineering computations· 0 citations
In the current context of the manufacturing industry, optimizing the cutting parameters to achieve a controlled surface roughness involves costly and time-consuming experimental efforts. The present study addresses this challenge by developing robust machine learning-based approximate functions for predicting surface roughness (Ra) resulting from toroidal milling on a five-axis CNC. The research includes an experimental design conducted under real production conditions on C45 steel. The relatively small experimental dataset was augmented, normalized, and then scripts were written for four prediction models: two artificial neural network architectures and two models based on decision trees. Their performance was analyzed based on MSE, RMSE, R2, and MRA metrics. The results obtained reveal significant differences between the models, highlighting solutions with high accuracy, excellent robustness, and superior generalization capacity for new data. The study highlights the high potential of prediction models in optimizing machining processes, providing an effective way to reduce costly physical experiments and increase productivity in industrial environments.
M. Banica, A. Osan, Andrei Filip· Machines· 0 citations
In recent years, the landscape of predictive modeling has been significantly transformed by the adoption of sophisticated methodologies, collectively known as soft computing techniques. The surface condition of a machined part plays a crucial role in its overall performance. It can significantly affect properties such as wear behavior, resistance to corrosion, and fatigue strength. Among the various indicators used to evaluate this finish, surface roughness (Ra) remains one of the most important criteria for assessing the quality of a machined surface.
Stellite 6 is a cobalt-based alloy widely employed in applications that demand high wear resistance and withstand elevated temperatures. While its mechanical strength makes it highly reliable in harsh environments, it also makes the alloy difficult to machine, particularly when aiming to achieve a good surface finish. The problem is framed using the Design of Experiments (DOE) to measure the Ra of Stellite 6 turning: a total of 27 experiments were performed.To better anticipate surface roughness during turning, this study investigates the performance of several machine learning (ML) models developed in Python. Three algorithms: Artificial Neural Networks (ANN), Random Forests (RF), and Support Vector Regression (SVR) were trained using an experimental dataset that includes tool noise radius, cutting speed, feed rate, and depth of cut.
Model performances were assessed using coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) metrics. Among the evaluated models, the RF achieved the highest prediction accuracy, followed by ANN and then SVR. The results highlight the capability of Python-based machine learning approaches to capture the nonlinear relationships between cutting parameters and surface roughness. The Random Forest Regressor (RFR) demonstrates the best ability to predict surface roughness (Ra), closely followed by the Epsilon-SVR using the RBF kernel. RFR is less sensitive to data scaling and effectively fits the complex non-linear relationships in the synthetic dataset.
This work provides a foundation for future strategies that integrate both advanced prediction to enhance the machinability of hard-to-cut materials such as Stellite 6.
R. Saidi, H. Bouchelaghem, Tarek Mabrouki et al.· Mechanics· 0 citations
Precise prediction and control of porosity in laser powder bed fusion (L-PBF) directly enhances the performance of additively manufactured components. This study addresses the need for comprehensive machine learning (ML) analysis of pore characteristics through comparative evaluation of multiple ML models based on accuracy and reliability. Five supervised ML models-linear regression (LR), Gaussian process regression, decision tree regression, artificial neural networks (ANNs), and random forest regression (RFR)-were utilized to predict pore characteristics in 316L stainless steel components fabricated via L-PBF. Model performance was systematically assessed using three key metrics: root mean square error, mean absolute error, and coefficient of determination (
R
2
). These metrics were calculated from process-condition averages under grouped cross-validation to ensure robust evaluation. Material characterization of specimens produced using pore-optimized printing parameters further validated the predictive accuracy of the models. Our results revealed that distinct models were optimal for different pore-related targets: LR outperformed others for porosity prediction, RFR excelled in estimating average pore diameter, and ANN delivered the highest accuracy for average pore roundness. Response surfaces generated from the optimal models delineated a processing window (laser power: 150–250 W; scan speed: 800–1200 mm/s; layer thickness: 0.04 mm; and hatch spacing: 0.09 mm) associated with minimized porosity and improved pore morphology. Notably, a significant inverse correlation was observed between predicted porosity and critical mechanical properties (including yield strength, tensile strength, and elongation at break), which further corroborated the practical utility of the proposed predictive workflow.
Shi-Yu Liu, Cheng Liu, Xiao Xue et al.· Metallurgical Research &...· 0 citations
This study applies several machine learning models to predict the Marshall stability of Stone Mastic Asphalt mixtures incorporating steel slag as a partial replacement for conventional coarse aggregates, with CatBoost providing the best performance.
T. Nguyen, Hoang-Long Nguyen, N. Trần et al.· Journal of Science and Trans...· 0 citations
Accurate prediction of surface roughness and cutting forces in milling aluminum alloys remains challenging under data-scarce conditions, where limited experimental data restricts the application of conventional machine learning models. This study addresses this gap by developing a systematic machine learning framework using 108 milling experiments (repeated to 216 tests) on aluminum alloys AA2024-T351 and AA6061-T6. Five primary machining inputs—material type, spindle speed, feed rate, depth of cut, and tool coating—were used. Through feature engineering, 35 interaction features were generated to capture non-linear relationships. A two-step preprocessing strategy was applied: Winsorization at the 5th and 95th percentiles to handle outliers, followed by hybrid scaling combining RobustScaler and MinMaxScaler. Eight machine learning algorithms, including XGBoost, NGBoost, LightGBM, CatBoost, Random Forest, MLP, SVR, and Least Squares Boosting, were developed and hyperparameter-optimized using the Optuna framework with Tree-structured Parzen Estimator. Models were evaluated using R2, MAE, and RMSE on a 70/15/15 train–validation–test split. Results demonstrate that XGBoost achieved the highest predictive accuracy for surface roughness (Ra) (R2 = 0.99829) and for resultant cutting force (FN) (R2 = 0.997). Feed rate was identified as the dominant machining parameter, accounting for 87.7% of the total importance in predicting surface roughness. SHAP analysis confirmed that engineered interaction features—particularly Feed_Coating and Material_Feed—carry strong physical relevance. Additionally, NGBoost enabled probabilistic regression, providing uncertainty estimates. The proposed framework proves highly effective for multi-output prediction in machining under limited data, offering a robust, interpretable, and industry-ready solution for quality control in aluminum alloy milling operations.
Mohammad Hossein Ebrahimi, S. Niknam· Machines· 0 citations