[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119308-en":3,"doc-seo-119308-105":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},119308,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","COLD SPRAY OPTIMIZATION VIA MACHINE LEARNING - Master’s thesis","Additive manufacturing can reduce the financial and temporal cost of logistics for the Department of Defense, and cold spray is capable of producing high-quality, cost-effective parts within limited time. However, cold spray remains a developing technology that has not been fully optimized. This thesis builds a machine learning model to optimize cold spray parameters by training on prints generated with varied settings. The dataset includes print parameters (temperature and pressure) and powder properties (yield strength and particle size), using composites of alumina with AA7075 as well as pure AA6061 and AA7075. Deposition efficiency results guide training, and a multilayer perceptron predicts adjustable parameters likely to maximize relative deposition efficiency for a given material. The model achieves 99.43% proper correlation for tested materials and can be extended to new materials or machines.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| 2024-12\u003Cbr>COLD SPRAY OPTIMIZATION VIA MACHINE LEARNING\u003Cbr>Hunter, Joel R.\u003Cbr>Monterey, CA; Naval Postgraduate School |\n| --- |\n| [https://hdl.handle.net/10945/73467](https://hdl.handle.net/10945/73467) |\n\nNPS Scholarship Theses  \nThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nCOLD SPRAY OPTIMIZATION VIA MACHINE LEARNING  \nby  \nJoel R. Hunter  \nDecember 2024  \nThesis Advisor: Troy Ansell  \nCo-Advisor: Wei Kang  \nDistribution Statement A. Approved for public release: Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  |  | Form Approved OMB No. 0704-0188 |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704- 0188) Washington, DC, 20503. |  |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>December 2024 |  | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master’s thesis |  |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>COLD SPRAY OPTIMIZATION VIA MACHINE LEARNING |  |  |  |  |  | 5. FUNDING NUMBERS\u003Cbr>N0001422WX00041 |  |\n| 6. AUTHOR(S) Joel R. Hunter |  |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>Office of Naval Research – Arlington, VA 22203-1995 |  |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Distribution Statement A. Approved for public release: Distribution is unlimited. |  |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>Additive manufacturing can be leveraged by the Department of Defense to reduce the financial and temporal cost of logistics. Cold spray has been shown to produce high-quality, cost-effective parts in a limited time frame. Cold spray is a developing technology and has not been fully optimized. Machine learning can be used to optimize cold spray parameters.\u003Cbr>To create a machine learning model, many cold spray prints were performed using varied parameters. These parameters included print parameters like temperature and pressure as well as properties of the powder like yield strength and particle size. Some of these prints were performed using composites ofalumina powder and AA7075 . Other prints were pure AA6061 or AA7075 . Highest deposition efficiencies for the materials varied from 3.63% using AA7075 (80%) & Al_2 O_3 (20%) to 43.62% using pure AA6061 . Pure AA7075 reached 33.72% deposition efficiency. The selected parameters for every material and print were fed into a multilayer perceptron. Once trained, a model was created to predict which adjustable parameters were most likely to produce the highest relative deposition efficiency for a given material. This model is","cbCairBh1tbUzkKK","https://ap.wps.com/l/cbCairBh1tbUzkKK","pdf",9305052,1,97,"English","en",105,"# Abstract\n## Additive manufacturing and cold spray background\n## Machine learning model for parameter optimization\n## Dataset design and materials\n## Model performance and predicted outputs\n## Future expansion","[{\"question\":\"Why is cold spray optimization important for additive manufacturing logistics?\",\"answer\":\"Additive manufacturing can lower logistics cost and time, and cold spray can produce high-quality parts. The technology still lacks full optimization, motivating data-driven parameter selection.\"},{\"question\":\"What inputs does the machine learning model use to optimize cold spray?\",\"answer\":\"The model uses print parameters such as temperature and pressure, and powder properties including yield strength and particle size.\"},{\"question\":\"How does the thesis evaluate model accuracy and usefulness?\",\"answer\":\"A multilayer perceptron is trained to predict parameters that maximize relative deposition efficiency. For the tested materials, the model achieves 99.43% proper correlation, supporting confident use on similar aluminum powders.\"}]","COLD SPRAY OPTIMIZATION VIA MACHINE LEARNING - Master’s thesis | PDF",1785723639,244,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cold-spray-optimization-via-machine-learning-masters-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cold-spray-optimization-via-machine-learning-masters-thesis/119308/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is cold spray optimization important for additive manufacturing logistics?","Question",{"text":75,"@type":76},"Additive manufacturing can lower logistics cost and time, and cold spray can produce high-quality parts. 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