{"id":177038,"date":"2020-11-23T14:02:19","date_gmt":"2020-11-23T14:02:19","guid":{"rendered":"http:\/\/facfox.com\/news\/?p=177038"},"modified":"2020-11-23T14:02:19","modified_gmt":"2020-11-23T14:02:19","slug":"senvol-machine-learning-to-be-used-for-missile-application-with-the-u-s-army-am-software","status":"publish","type":"post","link":"https:\/\/facfox.com\/news\/senvol-machine-learning-to-be-used-for-missile-application-with-the-u-s-army-am-software\/","title":{"rendered":"Senvol machine learning to be used for missile application with the U.S. Army AM Software"},"content":{"rendered":"

Senvol was awarded a United States Army Research Laboratory (ARL) contract to apply its machine learning software, Senvol ML, to rapidly design additive manufactured part<\/a>s. The software allows the Army to qualify part<\/a>s across AM processes and platforms, thus reducing the Army\u2019s supply chain lead time. By leveraging ML algorithms, the qualification plan will also be notably more efficient than more traditional qualification plans (i.e. require fewer builds and less time).<\/p>\n

Senvol\u2019s part<\/a>ners on the program include Lockheed Mart<\/a>in Missiles and Fire Control, EWI, and Pilgrim Consulting. The contract is administered by the National Center for Manufacturing Sciences (NCMS) through the Advanced Manufacturing, Materials, and Processes Program (AMMP) program.<\/p>\n

Ms. Stephanie Koch, ARL\u2019s Manager, said that \u201cAdditive manufacturing is a promising technology that could be used to enable multiple Army Modernization Priorities application<\/a>s. Despite the potential that additive manufacturing offers, the rate of adoption is very slow due to the high cost and time associated with the design, qualification, and certification of additively manufactured part<\/a>s. We are very encouraged with Senvol\u2019s approach, and look forward to seeing how we can leverage machine learning to improve processes.\u201d<\/p>\n

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Senvol President Annie Wang also commented that \u201cSenvol will implement data-driven machine learning technology for the U.S. Army that will substantially reduce the cost of material and part<\/a> qualification. The significant reduction in cost and the increase in speed will allow the Army to support warfighter readiness by unlocking the full transformative potential that additive manufacturing offers.\u201d<\/p>\n

Dr. William E. Frazier, retired Chief Scientist for Material Engineering at NAVAIR and currently President of Pilgrim Consulting, adds, \u201cI\u2019m very pleased to be supporting Senvol on this program. In part<\/a>icular, I\u2019m looking forward to the demonstration. The plan is to fabricate a missile part<\/a> and evaluate how close the actual performance requirements are compared to those predicted by the Senvol ML software, and to ultimately determine whether or not the part<\/a> should be qualified.\u201d<\/p>\n

Users of Senvol ML include organizations in aerospace<\/a>, defense, oil & gas, consumer<\/a> products, medical<\/a>, and automotive<\/a> industries, as well as AM machine manufacturers and AM material suppliers.<\/p>\n

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Authentise integrates nebumind digital twin tool into MES: <\/a>nebumind produces \u2018digital twin\u2019 visualizations, which fuse machine parameters and sensor data with the original part geometry. The integration of these visualizations with AMES will help users identify problem zones of each part more easily and lead to less time intensive and more accurate inspections. In addition, real-time alerts generated by the nebumind system inside AMES will help the user address any deviations during the process, reducing waste.<\/p><\/blockquote>\n

* This article is reprinted from 3D Printing Media Network<\/a>. If you are involved in infringement, please contact us to delete it.<\/p>\n

<\/i> Author: Adam Str\u00f6mbergsson<\/p>\n","protected":false},"excerpt":{"rendered":"

Senvol was awarded a United States Army Research Laboratory (ARL) contract to apply its machine learning software, Senvol ML, to rapidly design additive manufactured parts. The software allows the Army to qualify parts across AM processes and platforms, thus reducing the Army\u2019s supply chain lead time. By leveraging ML algorithms, the qualification plan will also be notably more efficient than more traditional qualification plans (i.e. require fewer builds and less time).<\/p>\n","protected":false},"author":3,"featured_media":177039,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"fifu_image_url":"","fifu_image_alt":"","footnotes":""},"categories":[37],"tags":[3039],"class_list":["post-177038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-design","tag-acquisitions-partnershipsaiam-software"],"yoast_head":"\nSenvol machine learning to be used for missile application with the U.S. Army AM Software - FacFox News<\/title>\n<meta name=\"description\" content=\"Senvol was awarded a United States Army Research Laboratory (ARL) contract to apply its machine learning software, Senvol ML, to rapidly design additive manufactured parts. 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