{"id":9748,"date":"2024-10-10T13:12:13","date_gmt":"2024-10-10T10:12:13","guid":{"rendered":"https:\/\/aire-edih.eu\/?post_type=project&p=9748"},"modified":"2025-11-03T13:42:35","modified_gmt":"2025-11-03T11:42:35","slug":"2024-experimenting-of-ai-based-process-control-for-plastic-bag-production","status":"publish","type":"project","link":"https:\/\/aire-edih.eu\/en\/project\/2024-experimenting-of-ai-based-process-control-for-plastic-bag-production\/","title":{"rendered":"2024: Experimenting of AI based process control for plastic bag production"},"content":{"rendered":"

This<\/span> project<\/span> focuses<\/span> on <\/span>the<\/span> deployment<\/span> and <\/span>validation<\/span> of <\/span>an<\/span> AI-<\/span>driven<\/span> defect<\/span> detection<\/span> and <\/span>process<\/span> optimization<\/span> system<\/span> for<\/span> industrial<\/span> manufacturing<\/span> environments<\/span>, <\/span>specifically<\/span> targeting<\/span> the<\/span> production<\/span> of <\/span>plastic<\/span> films<\/span> and <\/span>bags<\/span>. The <\/span>primary<\/span> objective<\/span> is<\/span> to<\/span> enhance<\/span> product<\/span> quality<\/span>, <\/span>reduce<\/span> production<\/span> waste<\/span>, and <\/span>minimize<\/span> energy<\/span> consumption<\/span> through<\/span> intelligent <\/span>automation<\/span>. The <\/span>solution<\/span> integrates<\/span> multiple<\/span> deep<\/span> learning<\/span> models<\/span>\u2014<\/span>Convolutional<\/span> Neural<\/span> Networks (<\/span>CNNs<\/span>) <\/span>for<\/span> visual<\/span> defect<\/span> recognition<\/span>, <\/span>Recurrent<\/span> Neural<\/span> Networks (<\/span>RNNs<\/span>) and <\/span>Long<\/span> Short<\/span>-Term <\/span>Memory<\/span> (LSTM) <\/span>networks<\/span> for<\/span> sequential<\/span> sensor <\/span>data<\/span> analysis<\/span>, and <\/span>Multilayer<\/span> Perceptrons<\/span> (<\/span>MLPs<\/span>) <\/span>for<\/span> structured<\/span> data<\/span> classification<\/span>. <\/span>These<\/span> models<\/span> work<\/span> collaboratively<\/span> to<\/span> detect<\/span> surface<\/span> and <\/span>structural<\/span> defects<\/span> such<\/span> as <\/span>folds<\/span>, <\/span>abrasions<\/span>, and <\/span>punctures<\/span>. At <\/span>the<\/span> core<\/span> of <\/span>the<\/span> system<\/span> lies<\/span> a <\/span>fuzzy<\/span> inference<\/span> engine<\/span> that<\/span> interprets<\/span> the<\/span> AI <\/span>outputs<\/span> and <\/span>autonomously<\/span> adjusts<\/span> key<\/span> manufacturing<\/span> parameters<\/span> (<\/span>e.g<\/span>., <\/span>temperature<\/span>, <\/span>composition<\/span>). <\/span>This<\/span> allows<\/span> the<\/span> system<\/span> to<\/span> respond<\/span> adaptively<\/span> to<\/span> varying<\/span> conditions<\/span> on <\/span>the<\/span> production<\/span> line<\/span> and <\/span>eliminate<\/span> the<\/span> need <\/span>for<\/span> manual<\/span> configuration<\/span> by <\/span>operators<\/span>. The <\/span>solution<\/span> is<\/span> seamlessly<\/span> integrated<\/span> into<\/span> the<\/span> existing<\/span> production<\/span> infrastructure<\/span> via<\/span> interfaces<\/span> and<\/span> edge<\/span> computing<\/span> platforms<\/span>. M<\/span>onitoring <\/span>dashboards<\/span> provide<\/span> operators<\/span> with<\/span> actionable<\/span> insights<\/span>, <\/span>while<\/span> automated <\/span>feedback<\/span> loops<\/span> ensure<\/span> continuous<\/span> optimization<\/span> of <\/span>process<\/span> parameters<\/span>. As a <\/span>result<\/span>, <\/span>the<\/span> project<\/span> has<\/span> successfully<\/span> demonstrated<\/span> significant<\/span> improvements<\/span> in <\/span>defect<\/span> detection<\/span> accuracy<\/span>, a <\/span>reduction<\/span> in <\/span>production<\/span> downtime<\/span>, and <\/span>measurable<\/span> gains<\/span> in <\/span>energy<\/span> efficiency<\/span>. <\/span>This<\/span> AI-<\/span>powered<\/span> system<\/span> paves<\/span> the<\/span> way<\/span> for<\/span> smarter<\/span>, <\/span>more<\/span> resilient<\/span>, and <\/span>data-driven<\/span> manufacturing<\/span> operations<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n

 <\/p>\n

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

This project focuses on the deployment and validation of an AI-driven defect detection and process optimization system for industrial manufacturing environments, specifically targeting the production of plastic films and bags. 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