We respond to Schaeben et al.’s1 comment on our paper,“Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.”While their observations are factually correct,they do not disprove our results.Our...We respond to Schaeben et al.’s1 comment on our paper,“Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.”While their observations are factually correct,they do not disprove our results.Our method,TACS,preserves the full distribution of crystallographic orientations and is validated with real-world data.We emphasize the importance of empirical validation over theoretical constructs in assessing machine learning methods’practical performance.展开更多
Generating reduced-order,synthetic grain structure datasets that accurately represent the measured grain structure of a material is important for reducing the cost and increasing the accuracy of computational crystal ...Generating reduced-order,synthetic grain structure datasets that accurately represent the measured grain structure of a material is important for reducing the cost and increasing the accuracy of computational crystal plasticity efforts.This study introduces a machine-learning-based approach,termed texture adaptive clustering and sampling(TACS),for generating representative Euler angle datasets that accurately mimic the crystallographic texture.The TACS approach employs K-means clustering and density-based sampling in a closed-loop iteration to create representative Euler angle datasets.Proof-of-principle experiments were performed on rolled and recrystallized low-carbon steel.Validation of the TACS approach was extended to twenty-two datasets,varying lattice structures,and complex crystallographic textures,thereby encompassing a broad range of materials and crystal structures.Kolmogorov-Smirnov(K-S)test comparisons underscore the performance of the TACS approach over traditional electron backscatter diffraction EBSD dataset reduction techniques,with average K-S test scores nearing 0.9,indicating a high-fidelity representation of the original datasets.In contrast,conventional methods display scores below 0.3,indicating less reliability of the structure representation.The independence of the TACS approach from material texture and its capability to autonomously generate datasets with predetermined data points demonstrates its unbiased potential in streamlining dataset preparation for crystallographic analysis.展开更多
基金funding for this work was provided by the Center for Extreme Events in Structurally Evolving Material under U.S.Army CCAPG-RTPDivision contract numberW011NF-23-2-0073The funder played no role in the study design,data collection,analysis,and interpretation of data,or the writing of this manuscript.
文摘We respond to Schaeben et al.’s1 comment on our paper,“Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.”While their observations are factually correct,they do not disprove our results.Our method,TACS,preserves the full distribution of crystallographic orientations and is validated with real-world data.We emphasize the importance of empirical validation over theoretical constructs in assessing machine learning methods’practical performance.
基金funding for this work was provided by the Center for Extreme Events in Structurally Evolving Material under U.S.Army CC-APG-RTP Division contract number W011NF-23-2-0073.
文摘Generating reduced-order,synthetic grain structure datasets that accurately represent the measured grain structure of a material is important for reducing the cost and increasing the accuracy of computational crystal plasticity efforts.This study introduces a machine-learning-based approach,termed texture adaptive clustering and sampling(TACS),for generating representative Euler angle datasets that accurately mimic the crystallographic texture.The TACS approach employs K-means clustering and density-based sampling in a closed-loop iteration to create representative Euler angle datasets.Proof-of-principle experiments were performed on rolled and recrystallized low-carbon steel.Validation of the TACS approach was extended to twenty-two datasets,varying lattice structures,and complex crystallographic textures,thereby encompassing a broad range of materials and crystal structures.Kolmogorov-Smirnov(K-S)test comparisons underscore the performance of the TACS approach over traditional electron backscatter diffraction EBSD dataset reduction techniques,with average K-S test scores nearing 0.9,indicating a high-fidelity representation of the original datasets.In contrast,conventional methods display scores below 0.3,indicating less reliability of the structure representation.The independence of the TACS approach from material texture and its capability to autonomously generate datasets with predetermined data points demonstrates its unbiased potential in streamlining dataset preparation for crystallographic analysis.
