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(a) Process forces in peripheral milling. (b) Change in the cu6ing-edge radius due to tool wear. (c) Ploughing forces due to the cu6ing-edge radius. (d) Influence of the cu6ing-edge rounding on plastic deformation according to Schoop et al., Albrecht, DIN 6580 and DIN 6584 [8,18-20].
Source publication
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...
Contexts in source publication
Context 1
... the surface compressive residual stress increased, but the depth of the maximum compressive stress d RS (Figure 1d) and the range of influence of the machininginduced residual stress decreased as the edge radius changed from 0.01 mm to 0.03 mm. Tools with sharp and rounded cutting edges were used by Coelho et al. and Li et al. [12,15]. ...
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... material deformation occurs at the front of the cutting edge due to its rounded shape, particularly where the cutting edge is in contact with the workpiece. The plastic deformation of the sub-surface (Figure 1d) led to compressive stresses in both up-and down-milling. The stresses are of different magnitudes depending on the direction. ...
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... to the tool-workpiece contact during the peripheral milling process, a resulting force F occurs. It comprises the cu6ing force Fc, the feed force Ff, and the passive force Fp (Figure 1a) [19]. In general, total forces recorded in a cu6ing process are the sum of individual forces acting on the tool flank face, its cu6ing edge, and the rake face [21]. ...
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... cu6ing edge is assumed to have an ideal sharp geometry in orthogonal cu6ing theory [22]. In practice, however, the cu6ing edge is rounded (Figure 1b), which causes a severe elastic and plastic deformation of the material around the cu6ing edge to occur simultaneously with the cu6ing process. This mechanism is referred to as the ploughing effect and can be described in terms of the ploughing force FPl (Figure 1c) acting directly on the cu6ing edge [18]. ...
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... practice, however, the cu6ing edge is rounded (Figure 1b), which causes a severe elastic and plastic deformation of the material around the cu6ing edge to occur simultaneously with the cu6ing process. This mechanism is referred to as the ploughing effect and can be described in terms of the ploughing force FPl (Figure 1c) acting directly on the cu6ing edge [18]. The percentage of this force in the total cu6ing force has often been considered insignificant in the previous literature and sometimes been neglected in force modeling for simplification reasons. ...
Context 6
... to the tool-workpiece contact during the peripheral milling process, a resulting force F occurs. It comprises the cutting force F c , the feed force F f , and the passive force F p (Figure 1a) [19]. In general, total forces recorded in a cutting process are the sum of individual forces acting on the tool flank face, its cutting edge, and the rake face [21]. ...
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... cutting edge is assumed to have an ideal sharp geometry in orthogonal cutting theory [22]. In practice, however, the cutting edge is rounded (Figure 1b), which causes a severe elastic and plastic deformation of the material around the cutting edge to occur simultaneously with the cutting process. This mechanism is referred to as the ploughing effect and can be described in terms of the ploughing force F Pl (Figure 1c) acting directly on the cutting edge [18]. ...
Context 8
... practice, however, the cutting edge is rounded (Figure 1b), which causes a severe elastic and plastic deformation of the material around the cutting edge to occur simultaneously with the cutting process. This mechanism is referred to as the ploughing effect and can be described in terms of the ploughing force F Pl (Figure 1c) acting directly on the cutting edge [18]. The percentage of this force in the total cutting force has often been considered insignificant in the previous literature and sometimes been neglected in force modeling for simplification reasons. ...
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... for titanium, which is used in applications requiring high mechanical integrity, information on the dependence of the surface integrity on the cutting-edge geometry is essential. The cuttingedge radius changes during milling as a result of tool wear (Figure 1b). In order to be able to determine the resulting residual stress state, a prediction of the microgeometry of the cutting edge is required. ...
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.