On-substrate fabrication of a self-activated nanostructured ZnO gas sensor.

Gaining rational control over bottom-up device fabrication processes is necessary to achieve high-performance devices and overcome technical obstacles. Among these is the need for activation of metal oxide gas sensors (GSs) by an external heating source, which limits their miniaturization and integration. A well-controlled, seedless, and position-selective hydrothermal method to fabricate high-performance self-activated zinc oxide (ZnO) nano-needle (ZNN) GSs directly on a substrate was developed. The morphology and position of the grown ZnO nanostructures were controlled by tuning the substrate coating and growth reaction parameters such as the growth solution concentration and the growth time, as well as introducing capping agents to the growth solution during the growth process. Furthermore, the efficiency of the fabricated device structure was improved and subsequently enhanced its performance substantially. Compared to other fabricated nanostructured ZnO GSs, the on-substrate fabricated bridging ZNN (BZNN) GS demonstrated superior sensitivity and self-activation, which were attributed to the reduction in the sensing material dimensions and ultrahigh surface-to-volume ratio, as well as the unique device structure with direct contact between ZnO and Au electrodes. This work paves the way for low cost, large scale, low temperature, seedless and position-selective fabrication of high-performance self-activated nanostructured ZnO GSs on flexible and transparent substrates.

Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization.

Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.

Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA.

Tool wear negatively impacts the quality of workpieces produced by the drilling process. Accurate prediction of tool wear enables the operator to maintain the machine at the required level of performance. This research presents a novel hybrid machine learning approach for predicting the tool wear in a drilling process. The proposed approach is based on optimizing the extreme gradient boosting algorithm's hyperparameters by a spiral dynamic optimization algorithm (XGBoost-SDA). Simulations were carried out on copper and cast-iron datasets with a high degree of accuracy. Further comparative analyses were performed with support vector machines (SVM) and multilayer perceptron artificial neural networks (MLP-ANN), where XGBoost-SDA showed superior performance with regard to the method. Simulations revealed that XGBoost-SDA results in the accurate prediction of flank wear in the drilling process with mean absolute error (MAE) = 4.67%, MAE = 5.32%, and coefficient of determination R2 = 0.9973 for the copper workpiece. Similarly, for the cast iron workpiece, XGBoost-SDA resulted in surface roughness predictions with MAE = 5.25%, root mean square error (RMSE) = 6.49%, and R2 = 0.975, which closely agree with the measured values. Performance comparisons between SVM, MLP-ANN, and XGBoost-SDA show that XGBoost-SDA is an effective method that can ensure high predictive accuracy about flank wear values in a drilling process.

Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method.

This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.