Machine learning-based prediction of pore characteristics in laser powder bed fusion-fabricated 316L stainless steel

Robust machine learning prediction of surface roughness in SKD61 turning

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.

Development of machine learning models to estimate strength characteristics of combined cellulose xanthate and fibre reinforced soft clayey soil used as subgrade

Comparative study of predictive models for the ultimate tensile strength of FDM-printed PLA parts based on literature-integrated data

Fused deposition modeling is an important additive manufacturing technology, and accurate prediction of the mechanical properties of three-dimensional-printed parts is essential for engineering applications. However, existing studies are often limited by small sample sizes, insufficient comparison of predictive methods and a lack of external data validation. This study therefore aims to construct a cross-literature data set and compare different predictive approaches for the ultimate tensile strength (UTS) of polylactic acid printed parts. A total of 235 experimental data points from ten published studies were integrated to establish a data set for UTS prediction. Five input variables were considered: infill density, nozzle temperature, nozzle diameter, layer height and printing speed. Response surface methodology, multiple machine learning regression algorithms and an artificial neural network (ANN) were systematically compared under unified preprocessing conditions. In addition, ensemble models were constructed using histogram-based gradient boosting regressor (HGBR), gradient boosting machine (GBM), random forest and kernel ridge regression (KRR). External validation was performed using 36 independent data points. Among the single models, HGBR achieved the best performance with a test-set R² of 0.8952. The GBM-KRR-HGBR ensemble further improved accuracy, reaching a test-set R² of 0.9290. For this ensemble model, external validation showed that 61.11% of samples had prediction errors below 10%. Permutation importance analysis indicated that infill density was the most influential variable. The originality of this study lies in a reliability-oriented literature-data curation strategy, a unified comparison of statistical, machine learning, ANN and ensemble models under the same data framework, and a source-wise external error analysis for evaluating model applicability under heterogeneous literature-derived data conditions.

Prediction of cutting forces and surface roughness in hard turning process of 42CrMo4 steel using machine learning

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.

Prediction of compressive and flexural strength of modified concrete using machine learning

Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.

Optimization and predictive measurement of compressive strength of iron ore slag modified concrete using data-driven supervised machine learning algorithms.

An integrated machine learning-experimental framework to predict the compressive strength (CS) of concrete incorporating ternary industrial wastes glass powder, marble powder, and iron ore slag is developed and the hybrid XGB-GBR model demonstrates the highest balanced performance.