In today’s tech world of digitalization, engineers are leveraging tools such as artificial intelligence for analyzing data in order to enhance their capability in evaluating product quality effectively. This research study adds value by applying algorithms and various machine learning techniques—such as support vector regression, Gaussian process regression, and artificial neural networks—on a dataset related to the grinding process of UNS S34700 steel. What sets this study apart is its consideration of factors like three types of grinding wheels, four distinct cooling solutions, and seven varied depths of cut. These parameters are assessed for their impact on surface roughness and grinding forces, resulting in the conversion of information into insights. A relational equation with 25 coefficients is developed, using optimized algorithms to predict surface roughness with an 85 percent accuracy and grinding forces with a 90 percent accuracy rate. Learning from machine models like the Gaussian process regression exhibited stability, with an R² value of 0.98 and a mean accuracy of 93 percent. Artificial neural networks achieved an R² value of 0.96, and an accuracy rate of 90 percent. These findings suggest that machine learning techniques are versatile and precise when dealing with datasets. They align well with digitalization and predictive trends. In conclusion; machine learning provides flexibility and superior accuracy for predicting data trends compared to the formulaic approach, which is contained to existing datasets only. The versatility of machine learning highlights its significance in engineering practices for making data-informed decisions.

As mechanical engineering becomes more data-driven, accurate and explainable prediction models are increasingly required. This study applied benchmarks for data-driven strategies and conducted a comprehensive analysis, using an 84-run grinding dataset on aluminum alloy 6061, with the surface roughness as the output variable. The conventional fitting method was the first applied and tuned using a closed-form formula, motivating the use of machine learning algorithms. Gaussian process regression, Artificial neural network, and Extreme gradient boosting were the algorithms that were used in predicting the relationship between input variables and the output. The best single model performance was given by GPR with an accuracy of 97.50%, a MAPE of 2.49%, and an R ² of 0.99. There were also three models of stacking ensembles that were applied. The stacking ensemble using ANN and XGB as base learners and GPR as the meta-learner offered the best trade-off between its bias and variance and achieved an overall accuracy of 94.54, a MAPE of 5.45, and an R ² of 0.98. The sensitivity analysis was employed to assess the significance of input parameters. Shapley additive explanations were also used to give attribution to each case, to attribute the impact of individual input features to each prediction. Among the variables, grinding wheel type 89A180K6V111 and the specific removal rate were the most influential. The framework can be adaptable to other datasets and grinding machining scenarios. Graphical Abstract

The digitalization of mechanical engineering requires reliable prediction tools that are interpretable and facilitate faster analysis. The study applied benchmarks for data-driven strategies and conducted a comprehensive analysis, using an 84-run grinding dataset on aluminum alloy 6061 that includes three different grinding wheel types, four different coolants, and seven specific removal rates with the surface roughness as the output variable. Particle Swarm Optimization was the first applied and tuned using a closed-form formula, but with unreliable accuracy metrics. Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGB) were the machine learning algorithms that were tested in predicting the relationship between input variables and the output. The best single model performance was given by GPR with an accuracy of 97.50%, a MAPE of 2.49% and an R ² of 0.99. There were also three models of stacking ensembles that were applied. The stacking ensemble using ANN and XGB as base learners and GPR as the meta-learner offered the best trade-off between its bias and variance and achieved an overall accuracy of 94.54, a MAPE of 5.45, and an R ² of 0.98. The sensitivity analysis was employed to assess the significance of input parameters. Shapley Additive Explanations (SHAP) was also used to give attribution to each case to attribute the impact of individual input features to each prediction. Among the variables, grinding wheel type 89A180K6V111 and the specific removal rate were the most influential. These novel stacking-ensemble and analysis methods can be broadened for future applicability.

Digitalisation of mechanical engineering necessitates the use of predictive tools that are explainable and allow for more rapid analysis. The study began with cold spraying (CS) of high entropy alloy (HEA) coatings, Al0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi coatings at nitrogen gas temperatures of 650, 750, and 850 °C. The surface roughness, Ra, was measured using profilometry and microscopy techniques. The data set comprised 20 samples with varying experimental conditions and seven measured parameters (five input variables and surface roughness (Ra) as the output. The stacking ensemble structure included three base learners, Linear Regression (LR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR) with RidgeCV as the meta-learner. LR was chosen due to its simplicity and interpretability, GPR was chosen due to its ability to capture nonlinear trends with small datasets, and RidgeCV was used to stabilize the model by shrinking coefficients and reducing variance. This ensemble was compared with XGBoost, which is a powerful single machine learning approach. Results indicated that the stacking ensemble performed better than XGBoost in all the regression metrics. XGBoost reached 96.79% accuracy, RMSE of 0.22 µm, MAPE of 3.32%, and R2 of 0.83, whereas the stacking ensemble achieved 97.26% accuracy, RMSE of 0.17 µm, MAPE of 2.73%, and R2 of 0.89. The results validate that stacking ensembles can improve predictive and robust performance even in scenarios of small data. These novel stacking-ensemble and analysis methods can be broadened for future applicability.

In Laser Powder Bed Fusion, a layer-by-layer melting of metal powder takes place and is specifically suited to high-performance applications in advanced technologies using Inconel™ 718. ANOVA and hardness measurements identified relationships that cannot be predicted using conventional statistical methods. Particle Swarm Optimization and Genetic Algorithm were proposed to define the relationships between the input and output data. Although the mean accuracy was high (97% for Particle Swarm Optimization and 91% for Genetic Algorithm), the drawbacks were calculation variation and sensitivity to parameter changes. The prediction of hardness was then done using five regression models, such as Support Vector Machine, Gaussian Process Regression (GPR), Single-Layer and Deep-Layer Artificial Neural Network (ANN), and Random Tree (RT). The low R² values were observed in the initial implementation with a mean accuracy of 90%. The linear method of optimizing output data increased R² values up to and beyond 0.9 with high average accuracy. GPR and single-layer ANN performed best in terms of training results. A rollback process was implemented on the test results. GPR and ANN displayed the best results with the highest R² (0.99) and MAPE (1.3%) values on the testing data, which proved them as the best solutions.

The combination of both Passive Thermography and machine learning in materials science and engineering allows rapid progress in advanced fatigue analysis. Focusing on mechanical aspects, the combination of these approaches is capable of interpolating the fatigue resistance in diverse conditions with minimal data, when compared to the classical solution, in which analyses are conducted using statistical processes such as the Staircase Method. Even though the thermal increment and thermal area are crucial parameters for the fatigue limit analysis, the implementation of machine-learning interpolation improves data consistency and reduces variability in the fatigue limit estimation through Type-A repeatability uncertainty reduction. This way, the two-layer artificial neural network does not have any predefined form of functions; second, it maintains the inherent non-linear features of the data. The validation of the proposed approach was conducted for a C45 steel, and two different experimental campaigns were conducted using a resonant machine. At the end, the analysis of the fatigue limit was conducted by means of an interpolation-assisted Two-Curve Method, starting from the classical thermal data evolution properly optimized with a machine-learning approach, achieving a more precise result in estimating the fatigue limit.