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The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurr...
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... acquisition was performed with a sampling rate of 2 kHz and the recorded forces were transmitted to the data receiver via Wi-Fi. The experimental setup is illustrated in Figure 2. ...
Citations
... ANNs find utility in machining and grinding due to their capacity to predict parameter relationships that influence critical outcomes like surface roughness. These processes have an impact across diverse industries [11][12][13][14], including aerospace and power generation, where the emphasis lies on optimizing and controlling parameters to achieve improved outcomes. ANNs offer a predictive tool beyond experimental and theoretical analyses, aiding in understanding and controlling complex parameter interactions for improved machining and grinding performance [15][16][17][18][19]. ...
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 a result, the Kernel Naive Bayes model achieved an accuracy of 94.4%, while the Decision Tree (Fine Tree) and k-nearest neighbors (KNN), in detail Fine KNN, models demonstrate exceptional accuracy, achieving a perfect accuracy rate of 100%. The paper [31] presents a tool wear prediction model based on cutting forces measured in-process during peripheral milling of Ti-6Al-4V. The paper explains how the residual stress state of the machined sub-surface influences the service quality indicators of a component. ...
Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a Machine Learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen Machine Learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.