[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126285-en":3,"doc-seo-126285-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126285,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","IDENTIFICATION OF OPTIMUM MILLING PARAMETERS THROUGH MACHINE LEARNING - Master Thesis","Milling operations are widely used across industries, where automotive production prioritizes high-volume throughput while aerospace and electronics require precise manufacturing within strict tolerance bands. The work formulates optimization goals such as maximizing material removal rate within machine limits and minimizing tool deflection and chatter risk to ensure conforming parts, both of which strongly influence unit cost through machining time and tool expenses. Supervised machine learning is adopted to identify the optimum solution, using simulated Ti-6-4 milling with carbide tools to evaluate forces, chatter, form errors, machining time, tool life, and tool breakage. A Gaussian Process Regression model is trained and optimized via Bayesian optimization to determine best parameters.","IDENTIFICATION OF OPTIMUM MILLING PARAMETERS THROUGH MACHINE LEARNING  \nby  \nGAMZE BALÇIK  \nSubmitted to the Graduate School of Engineering and Natural Sciences in partial fulfillment of the requirements for the degree of Master of Science  \nSabancı University  \nFall 2024  \n© Gamze Balçık 2024  \nAll Rights Reserved  \nii  \nABSTRACT  \nMilling operations are commonly utilized in many industries. Productivity rate becomes prominent in the industries such as automotive due to the need for high-volume manufacturing or the requirement to produce large die casts, whereas the aerospace and electronics industry must focus on precise manufacturing that does not exceed tolerance bands. This condition results in different types of optimization equations such as maximizing material removal rate with respect to machining center limits or minimizing the tool deflection and chatter risk to achieve conforming parts. Both optimizations will indicate a major effect on the unit cost of the product, hence they should describe the trade-off between the machining time and tool cost and help the selection of the optimum cutting parameters and tool dimensions.  \nThe increase in AI implementations and their promising accuracy levels were the main reasons to choose the supervised machine learning (ML) to investigate the optimum solution. In this thesis, Titanium alloy (Ti-6-4) workpiece material cutting process with carbide tool has been simulated for many different cutting tool and process parameter scenarios to calculate cutting forces, chatter status, surface form errors, machining time, tool life and tool breakage. Following the data preparation step, Gaussian Process Regression model has been computed for the optimization step with Bayesian approach.  \nKeywords: Milling, Bayesian Optimization, Machine Learning  \nÖZET  \nFrezeleme işlemleri birçok endüstride yaygın olarak kullanılmaktadır. Otomotiv gibi endüstrilerde, yüksek hacimli imalat ihtiyacı veya büyük kalıpların üretilmesi gerekliliğinedeniyle üretim hızı öne çıkarken, havacılık ve elektronik endüstrileri tolerans bantlarını aşmayan hassas imalata odaklanmak zorundadır. Bu durum, işleme merkezi sınırları dahilinde malzeme kaldırma oranını maksimize etmek veya kabul edilebilir parçalar eldeetmek için takım sapması ve titreşim riskini minimize etmek gibi farklı optimizasyon denklemleri gerektirir. Her iki optimizasyon da ürünün birim maliyeti üzerinde büyük bir etki yaratacağından, işleme süresi ile takım maliyeti arasındaki dengeyi tanımlamalı ve optimum kesme parametreleri ile takım boyutlarının seçimine yardımcı olunmalıdır.  \nBu tezde, yapay zeka uygulamalarındaki artış ve tahminlerdeki doğruluk seviyelerinedeniyle, optimum çözüm, denetimli makine öğrenimini (ML) ile araştırıldı . Titanyumalaşımı (Ti-6-4) iş parçası malzemesinin karbür takım ile kesme işlemi, farklı kesici takım ve parametre senaryolarıyla simüle edilerek kesme kuvvetleri, titreşim durumu, yüzey hataları, işleme süresi, takım ömrü ve takım bükülme stresi hesaplanmıştır. Veri hazırlama adımının ardından, verileri test etmek ve eğitmek için Gauss Süreci regresyonu kullanılmış, Bayes Optimizasyonu ile en iyi sonuçlar belirlenmiştir.  \nAnahtar Kelimeler: Frezeleme, Bayesçi Eniyileme, Makine Öğrenmesi  \nTo my mom,  \nwho put me into pressurized die cast process to get a very strong woman at any cost I was like air, she made me solid.  \nIn the end I could brew them both Now I am in my liquid phase Flowing with life  \nTo my love,  \nwho thought me to bend not to break  \nTo my kids,  \nSo ordinary and so unique At the same time How come!  \nv  \nACKNOWLEDGEMENTS  \nI am deeply thankful to my thesis advisor, Prof. Dr. Erhan Budak, with whom we crossed twice. His very warm welcome after a sudden quit and 14-year break gave me the encourage to restart with amnesty law. I feel blessed to have an access to his expertise for both my professional and personal life. His supports and patience is of great contribution to my thesis completion.  \nI w","cbCaihz93qhjhOZd","https://ap.wps.com/l/cbCaihz93qhjhOZd","pdf",5363569,5,1,94,"English","en",105,"# INTRODUCTION\n## Literature Survey\n## Problem Definition\n## Methodology\n# DATA PREPARATION FOR MACHINE LEARNING\n## Cutting Forces\n## Form Error\n## Tool Life","[{\"question\":\"What optimization objectives are targeted in the milling process study?\",\"answer\":\"The study targets maximizing material removal rate within machining-center limits and minimizing tool deflection and chatter risk to achieve conforming parts, while accounting for unit cost trade-offs between machining time and tool cost.\"},{\"question\":\"Which materials and tooling setup were simulated for model training?\",\"answer\":\"Titanium alloy Ti-6-4 workpieces were milled using a carbide tool across many cutting tool and process-parameter scenarios.\"},{\"question\":\"How is machine learning used to find optimum milling parameters?\",\"answer\":\"Cutting forces, chatter status, surface form errors, machining time, tool life, and tool breakage are computed from simulations, then Gaussian Process Regression is used for the optimization step with Bayesian approach.\"}]","IDENTIFICATION OF OPTIMUM MILLING PARAMETERS THROUGH MACHINE LEARNING - 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