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( a ) An orthogonal cutting testbed with the mounted sensors (two vibration...
Published Online: December 4, 2023
Fig. 2 ( a ) An orthogonal cutting testbed with the mounted sensors (two vibration sensors, one dynamometer, an HSC, and one AE sensor) and the associated data acquisition system and ( b ) the force signals gathered by the sensor and the cross-section microscopic image show the NFRP microstructure... More about this image found in ( a ) An orthogonal cutting testbed with the mounted sensors (two vibration...
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The overall framework of the explainable deep learning approach for unravel...
Published Online: December 4, 2023
Fig. 3 The overall framework of the explainable deep learning approach for unraveling the effect of microstructure variations on the resultant machining behaviors for NFRP cutting. ( a ) The black-box CNN model that estimates the cutting behaviors due to the heterogeneous microstructure of the NFR... More about this image found in The overall framework of the explainable deep learning approach for unravel...
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A schematic diagram showing the CNN-based model for estimating the cutting ...
Published Online: December 4, 2023
Fig. 4 A schematic diagram showing the CNN-based model for estimating the cutting behaviors using microscopic images from NFRP cross-sections, i.e., finding the connection between the microscopic image and the behaviors of cutting forces More about this image found in A schematic diagram showing the CNN-based model for estimating the cutting ...
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Results on predicting the macro-level machinability (cutting force) using a...
Published Online: December 4, 2023
Fig. 5 Results on predicting the macro-level machinability (cutting force) using a developed deep learning approach under different cutting speeds v : ( a ) 4 m/min, ( b ) 8 m/min, and ( c ) 12 m/min. All accuracies from distinct speeds are over 90%), and the frequency analysis results from force... More about this image found in Results on predicting the macro-level machinability (cutting force) using a...
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A schematic diagram showing the framework of the stochastic gradient descen...
Published Online: December 4, 2023
Fig. 6 A schematic diagram showing the framework of the stochastic gradient descent-based model-agnostic explanations to discover connections between the microstructure segments S and the process cutting behaviors More about this image found in A schematic diagram showing the framework of the stochastic gradient descen...
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An example of the SLIC-watershed algorithm for extracting reinforcing eleme...
Published Online: December 4, 2023
Fig. 7 An example of the SLIC-watershed algorithm for extracting reinforcing element contours within the PP matrix. ( a ) Super-pixels generated by the SLIC method (blue lines denote edges of super-pixels) in an original image slice. ( b ) High gradient (underlying reinforcing element regions) det... More about this image found in An example of the SLIC-watershed algorithm for extracting reinforcing eleme...
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( a ) The model-agnostic explanations-graphical regressor approach extracts...
Published Online: December 4, 2023
Fig. 8 ( a ) The model-agnostic explanations-graphical regressor approach extracts the features from the local segments of the microstructure (mainly fibrous reinforcing elements) and their relationship to the resultant cutting force during the NFRP machining; ( b ) and ( c ) two exemplary segment... More about this image found in ( a ) The model-agnostic explanations-graphical regressor approach extracts...
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The empirical density of the FVF values for the segments with fiber compone...
Published Online: December 4, 2023
Fig. 9 The empirical density of the FVF values for the segments with fiber components detected within their microstructure under different cutting speeds: ( a ) 4 m/min, ( b ) 8 m/min, and ( c ) 12 m/min. The beta-like distribution of the FVF values within the segments suggests that the significan... More about this image found in The empirical density of the FVF values for the segments with fiber compone...
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The boxplots depict the distributions of explanation regressor coefficients...
Published Online: December 4, 2023
Fig. 10 The boxplots depict the distributions of explanation regressor coefficients of different microscopic image segments under different cutting speeds: ( a ) 4 m/min, ( b ) 8 m/min, and ( c ) 12 m/min, where the box with index equal to 1 suggest the segments with fibers inside and the group wi... More about this image found in The boxplots depict the distributions of explanation regressor coefficients...
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Exemplary segments (from LIME) of the microscopic NFRP images with ( a )–( ...
Published Online: December 4, 2023
Fig. 11 Exemplary segments (from LIME) of the microscopic NFRP images with ( a )–( e ) the most statistically significant segments and ( f )–( l ) the least statistically significant segments in determining the variations of the cutting forces. The solid lines outline the reinforcing elements with... More about this image found in Exemplary segments (from LIME) of the microscopic NFRP images with ( a )–( ...
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Exemplary segments (the range of FVF values is 0.4–0.6) of which ( a )–( i ...
Published Online: December 4, 2023
Fig. 12 Exemplary segments (the range of FVF values is 0.4–0.6) of which ( a )–( i ) are the most statistically significant segments and ( j )–( r ) are the least significant in determining the variations of the cutting force More about this image found in Exemplary segments (the range of FVF values is 0.4–0.6) of which ( a )–( i ...
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MRSTP Microscopic material removal process: ( a ) initial flow state, ( b )...
Published Online: December 4, 2023
Fig. 2 MRSTP Microscopic material removal process: ( a ) initial flow state, ( b ) formation of “Enhanced Conformal Particle Clusters”, ( c ) material removal by “Enhanced Conformal Particle Clusters”, and ( d ) return to initial flow state More about this image found in MRSTP Microscopic material removal process: ( a ) initial flow state, ( b )...
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