[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125053-en":3,"doc-seo-125053-105":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},125053,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","MACHINE LEARNING FOR STABILITY ANALYSIS OF MILLING PROCESS","Machine learning is applied to analyze the chatter-related stability of manufacturing milling machines using key machining parameters including axial depth of cut, rotary speed (rpm), and cutting force. The study employs data science and statistical tools along with machine learning models such as SHAP to generate insight into two milling stability testing methods. A partitioning approach uses known stable and unstable points under defined conditions to select the most effective point(s) for stability lobe diagram testing. The objective is to compare combination versus sequencing testing for cost-effectiveness and accuracy in identifying the stability boundary and reducing chatter.","University of Tennessee, Knoxville  \nTRACE: Tennessee Research and Creative Exchange  \n\n| Masters Theses | Graduate School |\n| --- | --- |\n| 8-2024\u003Cbr>MACHINE LEARNING FOR STABILITY ANALYSIS OF MILLING PROCESS\u003Cbr>Katy Daniels\u003Cbr>[kdanie29@vols.utk.edu](kdanie29@vols.utk.edu)\u003Cbr>Follow this and additional works at: [https://trace.tennessee.edu/utk_gradthes](https://trace.tennessee.edu/utk_gradthes)\u003Cbr> Part of the Industrial Engineering Commons, and the Manufacturing Commons |  |\n\nRecommended Citation  \nDaniels, Katy, \"MACHINE LEARNING FOR STABILITY ANALYSIS OF MILLING PROCESS. \" Master's Thesis, University of Tennessee, 2024.  \n[https://trace.tennessee.edu/utk_gradthes/1](https://trace.tennessee.edu/utk_gradthes/1)1785  \nThis Thesis is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Masters Theses by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [trace@utk.edu](trace@utk.edu).  \nTo the Graduate Council:  \nI am submitting herewith a thesis written by Katy Daniels entitled \"MACHINE LEARNING FOR STABILITY ANALYSIS OF MILLING PROCESS.\" I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science, with a major in Industrial Engineering.  \nAnahita Khojandi, Major Professor  \nWe have read this thesis and recommend its acceptance: Anahita Khojandi, Gary Null, Tony Schmitz  \nAccepted for the Council: Dixie L. Thompson  \nVice Provost and Dean of the Graduate School  \n(Original signatures are on file with official student records.)  \nMACHINE LEARNING FOR STABILITY ANALYSIS OF MILLING PROCESS  \nA Thesis Presented for the  \nMaster of Science  \nDegree  \nThe University of Tennessee, Knoxville  \nKaitlyn C. Daniels  \nAugust 2024  \nCopyright © 2024 by Kaitlyn C.“Katy” Daniels All rights reserved.  \nACKNOWLEDGEMENTS  \nThis project is dedicated to my support system Bridget and Chris Daniels, my parents, who might not have understood what I was talking about, but nonetheless let me talk through the problems I encountered within this thesis, and Andy, my dog, who constantly reminded me to take much-needed breaks like the good boy he is. I also am grateful to acknowledge Juliana Broussard for helping to tutor me in Python, so that this project could come to fruition in a timely manner. I want to thank Dr. Gary Null and Dr. Tony Schmitz for being on my committee. Lastly, I thank Dr. Anahita Khojandi for being my committee advisor and for always believing in me even when I did not believe in myself.  \nABSTRACT  \nThe properties related to chatter and the stability of manufacturing milling machines are dependent primarily on parameters such as axial depth of cut, rotary speed rpm, and cutting force. These parameters are used to calculate the milling process and avoid certain cuts that would result in unstable milling. Using applied data science tools for statistical analysis and machine learning models like SHapley Additive exPlanations (SHAP), this research will produce valuable insight into two stability testing methods used in milling machining operations. By leveraging the data, a partitioning model can be used with known stable and unstable points under certain conditions to sort and select the best point(s) to be used in testing on the stability lobe diagram. The aim is to compare the two testing methods of combination and sequencing testing for cost-effectiveness and accuracy in identifying the milling machine’s stability boundary which in turn will help with reducing the amount of chatter present in the milling. Recognizing the testing method with the better accuracy will help determine the best sequence of testing and the stopping criteria.  \nKeywords—Milling, Chatter, Stability Mapping, Partitioning Models, Sequencing, Combination  \nTABLE OF C","cbCaijo4ULdyaMAp","https://ap.wps.com/l/cbCaijo4ULdyaMAp","pdf",3630083,1,53,"English","en",105,"# Chapter 1 - Introduction and General Information\n## Background\n## Literature\n## Goals\n## Data Gaps\n# Chapter 2 - Literature Review\n## Previous Research\n# Chapter 3 - Materials and Methods\n## Data Collection\n## Stability Limit Matrix\n## Stability Map\n## Sequencing\n## Combination\n## Parameters\n## Equations","[{\"question\":\"Which machining parameters are used to model milling stability in this thesis?\",\"answer\":\"Axial depth of cut, rotary speed (rpm), and cutting force are identified as primary parameters that determine chatter and stability.\"},{\"question\":\"How does the research use machine learning models such as SHAP?\",\"answer\":\"SHAP-based machine learning models are used with applied data science and statistical analysis to produce insight into stability testing methods.\"},{\"question\":\"What is the main comparison the thesis makes between two stability testing approaches?\",\"answer\":\"It compares combination testing and sequencing testing to evaluate cost-effectiveness and accuracy in identifying the milling stability boundary and reducing chatter.\"}]","MACHINE LEARNING FOR STABILITY ANALYSIS OF MILLING PROCESS | 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machining parameters are used to model milling stability in this thesis?","Question",{"text":75,"@type":76},"Axial depth of cut, rotary speed (rpm), and cutting force are identified as primary parameters that determine chatter and stability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the research use machine learning models such as SHAP?",{"text":80,"@type":76},"SHAP-based machine learning models are used with applied data science and statistical analysis to produce insight into stability testing methods.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main comparison the thesis makes between two stability testing approaches?",{"text":84,"@type":76},"It compares combination testing and sequencing testing to evaluate cost-effectiveness and accuracy in identifying the milling stability boundary and reducing 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