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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
Mtex Toolbox In Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
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MathWorks Inc matlab simulink 38
Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
Matlab Simulink 38, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc simevents toolbox
Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function <t>in</t> <t>MATLAB</t> detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.
Simevents Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 92/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function in MATLAB detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.

Journal: Journal of Materials Research and Technology

Article Title: Modeling local deformation, damage distribution, and phase transformation in zirconia particle-reinforced TRIP steel composites

doi: 10.1016/j.jmrt.2024.08.015

Figure Lengend Snippet: Fig. 11. Comparison of local damage between two cases of simulation and experiment at 6 % 11 %, and 16 % global strain (from left to right). Note that the parameters 0.75 and 0.9 indicated the critical plastic deformation damage criteria for the TRIP steel matrix within the simulation as shown on the top (blue) and bottom (green), respectively. The image processing function in MATLAB detects experimental data based on the SEM images from the in situ tensile test and five steps of the detection process: threshold, filtering, free-hand regions of interest (ROI), flood fill, and normalization.

Article Snippet: Additionally, the effect of crystallographic texture is analyzed using the MTEX toolbox in MATLAB [38] for data post-processing.

Techniques: Comparison, In Situ