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.

Extensive research in smart manufacturing and industrial grinding has targeted the enhancement of surface roughness for diverse materials including Inconel alloy. Recent studies have concentrated on the development of neural networks, as a subcategory of machine learning techniques, to predict non-linear roughness behavior in relation to various parameters. Nonetheless, this study introduces a novel set of parameters that have previously been unexplored, contributing to the advancement of surface roughness prediction for the grinding of Inconel 738 superalloy considering the effects of dressing and grinding parameters. Hence, the current study encompasses the utilization of a deep artificial neural network to forecast roughness. This implementation leverages an extensive dataset generated in a recent experimental study by the authors. The dataset comprises a multitude of process parameters across diverse conditions, including dressing techniques such as four-edge and single-edge diamond dresser, alongside cooling approaches like minimum quantity lubrication and conventional wet techniques. To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. To gauge the accuracy of the methods, mean squared error and absolute accuracy metrics are applied, yielding predictions that fall within acceptable ranges for real-world industrial roughness standards. The model developed in this work has the potential to be integrated with the Industrial Internet of Things to further enhance automated machining.

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.

In the realm of digitalization, data play a crucial role across various scientific fields, from physics to engineering. Analyzing data with advanced technologies like artificial intelligence enhances en-gineers' ability to make informed judgments on product quality. This study aligns with the digital-ization trend by implementing formula generation, generalization, and optimization using genetic algorithms and three machine learning approaches: support vector regression, Gaussian process regression, and artificial neural networks, on a grinding process dataset from a previous study on UNS S34700 steel. The study introduces new approaches by considering three different grinding wheels and four cooling and lubrication solutions as influential process parameters, previously considered qualitative and fixed in earlier literature. Additionally, seven different depths of cut were implemented to measure surface roughness and grinding forces (tangential and normal). Qualitative data were encoded into quantitative data using standard techniques. By enhancing genetic algorithm optimization, a relational formula with 25 coefficients was developed to describe the relationship between input parameters and outputs. Three machine learning algorithms were evaluated for predicting the relationship between predictors and responses. The flexibility and scalability of these methods, combined with the relational formula, provide a sophisticated ap-proach for future expansions. The formula evaluation achieved around 85% accuracy for surface roughness and 90% for grinding forces. In machine learning approaches, the GPR method reached model stability with an R² of around 0.98 and a mean accuracy of 93%, while the ANN reached an R² of around 0.96 and a mean accuracy of 90%. This shows that machine learning approaches are more suitable than formula generalization. The scalability and flexibility of machine learning help generalize with more data variety, while the formula evaluation only accepted current data sets. Furthermore, ML methods achieve high accuracy and are suitable for predicting future data, aligning with digitalization and trend prediction.