{"id":3754,"date":"2023-06-15T13:54:36","date_gmt":"2023-06-15T10:54:36","guid":{"rendered":"https:\/\/aire-edih.eu\/?post_type=project&p=3754"},"modified":"2024-06-12T21:20:33","modified_gmt":"2024-06-12T18:20:33","slug":"2022-proactive-feedback-system-for-manufacturing-settings-evaluation","status":"publish","type":"project","link":"https:\/\/aire-edih.eu\/en\/project\/2022-proactive-feedback-system-for-manufacturing-settings-evaluation\/","title":{"rendered":"2022: Testing innovative proactive AI based feedback system for manufacturing settings evaluation"},"content":{"rendered":"

\"\"<\/p>\n

In the course of the project “Testing innovative proactive feedback system for\u00a0manufacturing settings evaluation”, the possibilities of predicting the compliance of\u00a0products with quality requirements based on production parameters and, based on\u00a0this, providing feedback to the operator already during production regarding the\u00a0suitability of the selected production parameters, were investigated and tested. During\u00a0the study various machine learning algorithms were tested to create a relational model\u00a0to predict finished product parameters from production parameters. The production\u00a0setting parameters of already produced products, measurement data stored during\u00a0production and results obtained from quality control were used to train the feedback\u00a0system. As a result of the project, it was possible to show that there is a correlation\u00a0between the production parameters and the measurement data obtained from the\u00a0quality control of the product, and it is possible to predict this with a relational model\u00a0trained with machine learning methods. The obtained accuracy was lower than\u00a0expected, but it can be influenced by increasing the accuracy of measuring devices and\u00a0improving data processing.<\/p>\n

github<\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"

In the course of the project “Testing innovative proactive feedback system for\u00a0manufacturing settings evaluation”, the possibilities of predicting the compliance of\u00a0products with quality requirements based on production parameters and, based […]<\/p>\n","protected":false},"featured_media":5682,"template":"","year_tax":[85],"company_tax":[189],"status_tax":[92],"university_tax":[90],"sector":[164],"project_cat":[65],"class_list":["post-3754","project","type-project","status-publish","has-post-thumbnail","hentry","year_tax-2023-en","company_tax-krah-pipes-ou-en","status_tax-completed-projects","university_tax-taltech-en","sector-quality-assurance","project_cat-completed-projects"],"acf":[],"yoast_head":"\nAIRE - 2022: Testing innovative proactive AI based feedback system for manufacturing settings evaluation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aire-edih.eu\/en\/project\/2022-proactive-feedback-system-for-manufacturing-settings-evaluation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AIRE - 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