[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121244-en":3,"doc-seo-121244-105":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":20,"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},121244,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing - research paper","The research paper presents a machine learning–assisted optimization framework for tensile stress in Triply Periodic Minimal Surface (TPMS) structures using Simulated Annealing (SA). Tensile-stress datasets are generated via finite element analysis of TPMS models, then used to train Random Forest, Decision Tree, and XGBoost predictors. The optimization minimizes a negative R-squared objective on the validation set to improve predictive accuracy. The SA–XGBoost combination achieves the strongest performance with R² of 0.96, outperforming SA–Random Forest (0.89) and showing instability for the Decision Tree approach, demonstrating the value of SA for hyperparameter tuning and capturing complex relationships.","Machine Learning-Driven Optimization ofTPMS Architected Materials Using Simulated Annealing  \nAkshansh Mishra 1  \n1School of Industrial and Information Engineering, Politecnico Di Milano, Milan, Italy Mail id: [akshansh.mishra@mail.polimi.it](akshansh.mishra@mail.polimi.it)  \nAbstract: The research paper presents a novel approach to optimizing the tensile stress of Triply Periodic Minimal Surface (TPMS) structures through machine learning and Simulated Annealing (SA) . The study evaluates the performance of Random Forest, Decision Tree, and XGBoost models in predicting tensile stress, using a dataset generated from finite element analysis ofTPMS models. The objective function minimized the negative R-squared value on the validation set to enhance model accuracy. The SA-XGBoost model outperformed the others, achieving an R-squared (R²) value of 0.96. In contrast, the SA-Random Forest model achieved an R² of 0.89 while the SA-Decision Tree model exhibited greater fluctuations invalidation scores. This demonstrates that the SA-XGBoost model is most effective in capturing the complex relationships within the data. The integration of SA helps in optimizing the hyperparameters of these machine learning models, thereby enhancing their predictive capabilities.  \nKeywords: Architected Material; TPMS Structures; Machine Learning; Mechanical Properties  \n1. Introduction  \nTriply Periodic Minimal Surfaces (TPMS) became known as an important topic of research due to their distinct structure and functional features [1-4]. These surfaces are distinguished by their three-dimensional periodicity and minimal surface area for a given volume, resulting in multiple favorable features that can be used in a variety of technical applications. The relevance ofTPMS structures stems from their highly organized pore networks, which provide superior mechanical and thermal properties when compared to traditional materials [5-7] . These structures exist in both natural and synthetic forms and have been the subject of intense research due to their potential to change numerous industries, including biomedical, aeronautical, and automotive. TPMS structures, such as the gyroid, Schwarz diamond, and Neovius surfaces, have various advantageous features. They have a high surface area-to-volume ratio, which improves thermal energy transmission and makes them suitable for applications such as heat exchangers and thermal insulators. TPMS structures can sustain significant mechanical loads while keeping a lightweight profile, which is very beneficial in aerospace and automotive applications where weight reduction is required without sacrificing strength. Certain TPMS structures are also biocompatible, making them appropriate for biomedical applications such as scaffolds for tissue engineering due to their porous architecture, which promotes cell development and nutrient flow.  \nAI can aid the design of Triply Periodic Minimal Surfaces (TPMS) by allowing for more efficient, precise, and novel ways. AI-driven design uses machine learning methods and computational methodologies to optimise the complicated geometries and features of TPMS structures. These algorithms can evaluate large quantities of data and learn from patterns, allowing for the development of TPMS designs that meet certain performance requirements such as mechanical strength, thermal conductivity, and biocompatibility. By automating the design process, AI saves time and effort when compared to traditional approaches, which frequently involve trial-and-error and expensive computational resources. AI may combine many design objectives and constraints at once, resulting in more resilient and multifunctional TPMS systems. Zhang et al. [8] investigated the elastic modulus of triply periodic minimal surface (TPMS) structures for titanium, a critical biomedical material, using three machine learning (ML) methods: Random Forest, XGBoost, and Adaboost. The researchers created adataset from elastic finite element a","cbCaipj83I8MZxaL","https://ap.wps.com/l/cbCaipj83I8MZxaL","pdf",2345579,1,25,"English","en",105,"# Introduction\n## Background and significance of TPMS\n## AI-driven design for TPMS optimization\n## Related work using machine learning models","[{\"question\":\"What is the main objective of the study on TPMS materials?\",\"answer\":\"To optimize the tensile stress of Triply Periodic Minimal Surface (TPMS) structures by combining machine learning with Simulated Annealing.\"},{\"question\":\"Which machine learning models are compared for predicting tensile stress?\",\"answer\":\"Random Forest, Decision Tree, and XGBoost are evaluated for their ability to predict tensile stress from TPMS data.\"},{\"question\":\"Why does the SA-XGBoost approach perform best?\",\"answer\":\"Simulated Annealing is used to optimize hyperparameters and the SA-XGBoost model achieves the highest validation accuracy, with an R² value of 0.96, indicating strong capture of complex data relationships.\"}]","Machine Learning-Driven Optimization of TPMS Architected Materials Using Simulated Annealing - 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