[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126939-en":3,"doc-seo-126939-105":30,"detail-sidebar-cat-0-en-105":95},{"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},126939,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","MACHINE LEARNING OF STRUCTURE – PROPERTY RELATIONSHIPS: AN APPLICATION TO HEAT GENERATION DURING PLASTIC DEFORMATION - research paper overview","Machine learning methods are used to study heat generation during plastic deformation of a multiphase material. The work predicts temperature increase from structure-property relationships using microstructural information and varying Taylor–Quinney coefficients, aiming for both accuracy and computational efficiency for industrial use. Automatic microstructure generation and finite element analysis provide temperature increase–strain curves for dataset creation and ML training. A 3D CNN maps microstructure configuration and TQC to the full temperature increase–strain response.","[https://doi.org/10.22190/FUME240215019N](https://doi.org/10.22190/FUME240215019N)  \nOriginal scientific paper  \nMACHINE LEARNING OF STRUCTURE – PROPERTY RELATIONSHIPS: AN APPLICATION TO HEAT GENERATION DURING PLASTIC DEFORMATION  \nFilip Nikolić1,2, Marko Čanađija2  \n1CAE Department, Elaphe Propulsion Technologies Ltd, Ljubljana, Slovenia 2University of Rijeka, Faculty of Engineering, Rijeka, Croatia  \nAbstract. In the present work, the heat generation during the plastic deformation of amultiphase material is studied using machine learning (ML) methods. The aim was to predict the temperature increase from the structure-property relationships (SPR) of amicrostructure considering various Taylor–Quinney coefficients (TQCs), with the aim of achieving precision and computational efficiency suitable for industry. Using automatic microstructure generation to create datasets and finite element analysis (FEA) to obtain temperature increase – strain curves, the dataset facilitated the training of an ML model.  \nA 3D convolutional neural network (CNN) was developed using the microstructural configuration and TQC value as input and the temperature increase – strain curve as output. The model demonstrated high prediction accuracy. The results indicated that the hard phase fraction significantly impacts the temperature increase, much more than the TQC values. This underlines the potential of the model for a better understanding of material behavior during deformation and its industrial applicability.  \nKey words: Deep learning, Taylor – Quinney coefficient, Heat generation, Structure – property relationship, Finite element analysis  \n1. INTRODUCTION  \nIt is well-known that the deformation of most materials can be divided into elastic and plastic deformation. Both can be either rate dependent or rate independent. While elastic deformation is a reversible process, plastic deformation is irreversible. During plastic deformation, most of the energy is dissipated into heat while during elastic deformation the heat generation is often neglected. For a general overview of the phenomenon, the interested reader is referred to [1] and the references therein. The issue is further complicated by the fact that the response of the material involves the temperature  \nReceived: February 15, 2024 / Accepted May 02, 2024  \nCorresponding author: Marko Čanađija  \nUniversity of Rijeka, Faculty of Engineering, Vukovarska 58, Rijeka, 51000, Croatia. [E-mail: marko.canadija@riteh.uniri.hr](E-mail: marko.canadija@riteh.uniri.hr)  \ndependence of the properties [2–4]. The fraction of plastic energy that is converted to heat is described by the Taylor–Quinney coefficient (TQC) and can depend on the type of material, strain rate, amount of plastic strain and many other factors, see [5-7] . It is often assumed to be around 90 %[5] and usually increases with deformation, see for example [2, 5, 7, 8] . Moreover, in some special cases this coefficient can be greater than one due to microstructural transformations. At this point, it should only be briefly noted that the microstructural aspects are very important, but the complexity of the phenomenon hinders the possibility of fully addressing all the details using classical techniques. Standard multiscale methods can provide some solutions to these issues, but on the other hand they are quite slow [9] .  \nIn the last few years, machine learning (ML) has become a very attractive topic in materials science and many different ML algorithms are widely used. Shallow algorithms such as the support vector machine can be found in applications such as microstructure classification [10] . Azimi et al. [11], on the other hand, used much more complex algorithms such as convolutional neural networks (CNNs) for similar tasks. DeCost et al.  \n[12] applied a CNN model based on segmentation for novel automated applications of microstructure segmentation. Applications such as defect detection and determination of distances between secondary ","cbCaiaVIHrBY7G2G","https://ap.wps.com/l/cbCaiaVIHrBY7G2G","pdf",1883934,1,21,"English","en",105,"# Abstract and Objective\n# Methodology and Data Generation\n## Microstructure generation and dataset creation\n## Finite element analysis for temperature–strain curves\n# Model Design and Inputs/Outputs\n## 3D convolutional neural network\n# Results and Key Findings\n## Role of hard phase fraction vs TQC\n# Context: Plastic vs Elastic Deformation and TQC\n# Related Work in ML for Microstructure and SPR","[{\"question\":\"What problem does the study address in plastic deformation?\",\"answer\":\"The study addresses how heat is generated during plastic deformation of a multiphase material and how to predict temperature rise from microstructural information.\"},{\"question\":\"How are training datasets created for the machine learning model?\",\"answer\":\"Datasets are built by automatically generating microstructures and using finite element analysis to compute temperature increase–strain curves.\"},{\"question\":\"What inputs and outputs does the 3D CNN use?\",\"answer\":\"The 3D CNN takes the microstructural configuration and the Taylor–Quinney coefficient value as inputs, and outputs the temperature increase–strain curve.\"},{\"question\":\"Which factor is reported to affect temperature increase more strongly than the TQC?\",\"answer\":\"The hard phase fraction significantly impacts temperature increase, reported as much more influential than the Taylor–Quinney coefficient values.\"}]","MACHINE LEARNING OF STRUCTURE – PROPERTY RELATIONSHIPS: AN APPLICATION TO HEAT GENERATION DURING PLASTIC DEFORMATION - 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