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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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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

Quantification and qualification analysis results of the included studies.

Journal: Frontiers in Human Neuroscience

Article Title: Time-delay estimation in biomechanical stability: a scoping review

doi: 10.3389/fnhum.2024.1329269

Figure Lengend Snippet: Quantification and qualification analysis results of the included studies.

Article Snippet: , Stance balance , Yes , Yes , No , No , Yes , Yes , 38–85 , Yes , Constant , MATLAB , Direct analysis of clinical data (MAOD ) , EMG , Healthy and CLBP , External , ML , Unexpected , No , No , No.

Techniques: