[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118580-en":3,"doc-seo-118580-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118580,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","High-velocity impact study of an advanced ceramic using finite element model coupling with a machine learning approach","A numerical workflow combining finite element modeling with machine learning is developed to predict the performance of an alumina ceramic tile under high-velocity impact. The ceramic is modeled by embedding a user-defined Johnson-Holmquist-Beissel (JHB) material law into an SPH formulation in LS-DYNA, and the implementation is validated via equivalent stress–pressure comparisons in a single-element test. The framework is then verified against literature plate-impact and ballistic experiments, after which simulation outputs are used as training data for an ANN to forecast residual velocity and projectile erosion, followed by sensitivity studies over key material properties and impact geometry.","High-velocity impact study of an advanced ceramic using finite element model coupling with a machine learning approach  \nAlex Yanga,∗, Dan Romanyka , James D. Hogana  \na Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada  \nAbstract  \nA numerical approach combining finite element modeling and machine learning is used to inform the material performance of an alumina ceramic tile undergoing high-velocity impact. In this study, the alumina ceramic tile is simulated by incorporating a user-defined Johnson-Holmquist-Beissel (JHB) material model within the framework of smoothed particle hydrodynamics (SPH) in LS-DYNA finite element software. The implementation of the JHB model is verified by comparing equivalent stress-pressure responses through a single element simulation test. After implementation, the computational framework is simulated across our chosen range of conditions by matching the results from both plate impact experiments and ballistic testing from the literature. The computational model is then used to generate training data sets for an artificial neural network (ANN) to predict the residual velocity and projectile erosion for an alumina ceramic tile undergoing highvelocity impact in the SPH framework. The ANN is then used to perform a sensitivity analysis involving exploring the effect of mechanical properties (e.g., strength and shear modulus) and impact simulation geometries (e.g., thickness of ceramic tile) on material performance (i.e., residual projectile velocity and erosion) . Overall, this study shows the capability of the FEM-ANN approach in studying the high-velocity impact on ceramic tiles and is applicable to guide the structural-scale design of ceramic-based protection systems.  \nKeywords: High-velocity impact, Ceramic armor, Johnson-Holmquist-Beissel (JHB), Smoothed particle hydrodynamics, Artificial neural network  \n∗ Corresponding author  \nEmail address: [sy11@ualberta.ca](sy11@ualberta.ca) (Alex Yang)  \nPreprint submitted to Ceramic International 08/08/2022  \n1. Introduction  \nAdvanced ceramics, such as alumina, have been incorporated into the design of various armor systems as frontal layers, mainly owing to their relatively high strength, hardness, and low cost-to-performance ratio [1–3] . To make efforts towards designing and improv-  \n5 ing armour systems, many experimental and numerical studies have sought to understand the role of mechanical properties and geometries on the dynamic ballistic performance of ceramics [4–7] . Comparing with experimental approaches, numerical approaches enable a wider range of material constants and design parameters to be explored, with improved temporal and spatial resolutions, especially under extreme loading conditions where ex-  \n10 perimentation and field testing are difficult and costly (e.g., ballistic impact [8], laser shock [9]) . For example, ballistic testing in the literature are often conducted within a rather narrow impact velocity range [10], which limits the systematic study of both ballistic (e.g., dwell and penetration [11, 12]) and material responses (e.g., change of mechanisms) . Hence, future design strategies and materials development will be largely  \n15 guided by advancements in numerical approaches after careful verification and validations [13–16], and these will be pursued in this study.  \nNumerical simulations informed and validated by experiments is a powerful engineering tool for the optimization and design of structures subjected to complex loading conditions (e.g., impact loads [17]) . The choices of the material model and the numerical  \n20 framework plays a key role in the accuracy of predictive results [16] . In the literature, phenomenological models have been extensively implemented to study the behavior of ceramics under the high-velocity impact, such as the Johnson-Holmquist models which considers the strain rate, pressure, bulking, and phase change effects (JH1, JH2, and JHB)[18–20] . A ","cbCaibXeVK0rqWoF","https://ap.wps.com/l/cbCaibXeVK0rqWoF","pdf",15745917,1,47,"English","en",105,"# Introduction\n## Advanced ceramics in armor systems\n## Numerical modeling and material model selection\n## Johnson-Holmquist-Beissel (JHB) rationale\n# Numerical framework and implementation\n## SPH for large deformation\n## Coupling FEM/ANN for predictive performance\n# Model validation and training data generation\n## Comparing with plate impact and ballistic tests\n## Building ANN training sets\n# Prediction and sensitivity analysis\n## Residual velocity and erosion prediction\n## Effects of mechanical properties and geometry","[{\"question\":\"How is the alumina ceramic tile modeled for high-velocity impact in this study?\",\"answer\":\"The tile is simulated by implementing a user-defined Johnson-Holmquist-Beissel (JHB) material model within an SPH framework in LS-DYNA.\"},{\"question\":\"How is the JHB implementation verified before broader simulations?\",\"answer\":\"Verification is performed by comparing equivalent stress–pressure responses from a single element simulation test.\"},{\"question\":\"What does the artificial neural network (ANN) predict, and how is it used?\",\"answer\":\"The ANN is trained using simulation-generated datasets to predict residual projectile velocity and projectile erosion, and it is then applied for sensitivity analysis over material properties and impact geometries.\"}]","High-velocity impact study of an advanced ceramic using finite element model coupling with a machine learning approach | 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is the alumina ceramic tile modeled for high-velocity impact in this study?","Question",{"text":75,"@type":76},"The tile is simulated by implementing a user-defined Johnson-Holmquist-Beissel (JHB) material model within an SPH framework in LS-DYNA.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the JHB implementation verified before broader simulations?",{"text":80,"@type":76},"Verification is performed by comparing equivalent stress–pressure responses from a single element simulation test.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the artificial neural network (ANN) predict, and how is it used?",{"text":84,"@type":76},"The ANN is trained using simulation-generated datasets to predict residual projectile velocity and projectile erosion, and it is then applied for sensitivity analysis over material properties and impact 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