{"id":2378,"date":"2025-08-05T13:58:58","date_gmt":"2025-08-05T11:58:58","guid":{"rendered":"https:\/\/www.ki-fortschrittszentrum.de\/?post_type=projekt&#038;p=2378"},"modified":"2025-09-04T11:04:28","modified_gmt":"2025-09-04T09:04:28","slug":"sample-project-3-6-3","status":"publish","type":"projekt","link":"https:\/\/www.ki-fortschrittszentrum.de\/en\/projekt\/beispielprojekt-3-6-3\/","title":{"rendered":"Perception for automated handling of dimensionally stable components (AutoLab)"},"content":{"rendered":"<div class=\"wp-block-stackable-columns stk-block-columns stk-block stk-86663cd stk-block-background stk--has-background-overlay\" data-block-id=\"86663cd\"><style>.stk-86663cd {background-image:url(https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/Teaserbild_Fraunhofer.svg) !important;min-height:520px !important;align-items:flex-end !important;padding-bottom:0px !important;margin-bottom:0px !important;display:flex !important;}<\/style><div class=\"stk-row stk-inner-blocks stk-block-content stk-content-align stk-86663cd-column\">\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-5a28863\" data-v=\"4\" data-block-id=\"5a28863\"><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-5a28863-container stk--no-background stk--no-padding\"><div class=\"stk-block-content stk-inner-blocks stk-5a28863-inner-blocks\">\n<div class=\"wp-block-stackable-columns stk-block-columns stk-block stk-3e4895d\" data-block-id=\"3e4895d\"><div class=\"stk-row stk-inner-blocks stk-block-content stk-content-align stk-3e4895d-column\">\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-a094f1e\" data-v=\"4\" data-block-id=\"a094f1e\"><style>@media screen and (min-width:690px){.stk-a094f1e {flex:var(--stk-flex-grow, 1) 1 calc(30% - var(--stk-column-gap, 0px) * 2 \/ 3 ) !important;}}<\/style><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-a094f1e-container stk--no-background stk--no-padding\"><div class=\"stk-block-content stk-inner-blocks stk-a094f1e-inner-blocks\"><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-ba57710\" data-v=\"4\" data-block-id=\"ba57710\"><style>@media screen and (min-width:690px){.stk-ba57710 {flex:var(--stk-flex-grow, 1) 1 calc(30% - var(--stk-column-gap, 0px) * 2 \/ 3 ) !important;}}<\/style><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-ba57710-container stk--no-background stk--no-padding\"><div class=\"stk-block-content stk-inner-blocks stk-ba57710-inner-blocks\"><\/div><\/div><\/div>\n\n\n\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-4764f29 stk-block-background\" data-v=\"4\" data-block-id=\"4764f29\"><style>.stk-4764f29 {align-self:flex-end !important;background-color:var(--theme-palette-color-8, #ffffff) !important;padding-top:0px !important;padding-right:0px !important;padding-bottom:0px !important;padding-left:0px !important;}.stk-4764f29-inner-blocks{justify-content:flex-end !important;}.stk-4764f29:before{background-color:var(--theme-palette-color-8, #ffffff) !important;}@media screen and (min-width:690px){.stk-4764f29 {flex:var(--stk-flex-grow, 1) 1 calc(40% - var(--stk-column-gap, 0px) * 2 \/ 3 ) !important;}}<\/style><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-4764f29-container stk--no-background stk--no-padding\"><div class=\"stk--column-flex stk-block-content stk-inner-blocks stk-4764f29-inner-blocks\">\n<div class=\"wp-block-greenshift-blocks-container gspb_container gspb_container-gsbp-b40a6b1\" id=\"gspb_container-id-gsbp-b40a6b1\">\n<div class=\"wp-block-stackable-image stk-block-image has-text-align-right stk-block stk-a7deb7e\" data-block-id=\"a7deb7e\"><style>.stk-a7deb7e {margin-bottom:36px !important;}.stk-a7deb7e .stk-img-wrapper{width:50% !important;}<\/style><figure><span class=\"stk-img-wrapper stk-image--shape-stretch\"><img loading=\"lazy\" decoding=\"async\" class=\"stk-img wp-image-344\" src=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/04\/Fraunhofer-IPA-WEB-weiss-1.svg\" width=\"258\" height=\"72\"\/><\/span><\/figure><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-greenshift-blocks-container gspb_container gspb_container-gsbp-1fa6a74\" id=\"gspb_container-id-gsbp-1fa6a74\">\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-be1fdb3\" data-block-id=\"be1fdb3\"><style>.stk-be1fdb3 {padding-top:0px !important;padding-bottom:12px !important;margin-top:0px !important;margin-bottom:0px !important;}.stk-be1fdb3 .stk-block-text__text{font-size:15px !important;color:#ffffff80 !important;}@media screen and (max-width:999px){.stk-be1fdb3 .stk-block-text__text{font-size:15px !important;}}<\/style><p class=\"stk-block-text__text has-text-color has-text-align-left\">Contact at the AI Innovation Center<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-24ffa5e\" data-block-id=\"24ffa5e\"><style>.stk-24ffa5e {padding-top:0px !important;padding-bottom:0px !important;margin-top:0px !important;margin-bottom:0px !important;}.stk-24ffa5e .stk-block-text__text{font-size:15px !important;color:#ffffff !important;}@media screen and (max-width:999px){.stk-24ffa5e .stk-block-text__text{font-size:15px !important;}}<\/style><p class=\"stk-block-text__text has-text-color has-text-align-left\">Andreas Frommknecht<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-button-group stk-block-button-group stk-block stk-af007b4\" data-block-id=\"af007b4\"><style>.stk-af007b4 {padding-top:0px !important;padding-right:0px !important;padding-bottom:0px !important;padding-left:0px !important;margin-top:0px !important;margin-right:0px !important;margin-bottom:0px !important;margin-left:0px !important;}<\/style><div class=\"stk-row stk-inner-blocks stk-block-content stk-button-group\">\n<div class=\"wp-block-stackable-button stk-block-button is-style-plain stk-block stk-1cf8bea\" data-block-id=\"1cf8bea\"><style>.stk-1cf8bea .stk-button{padding-top:0px !important;padding-right:0px !important;padding-bottom:0px !important;padding-left:0px !important;background:transparent !important;}.stk-1cf8bea .stk-button:hover:after{background:transparent !important;opacity:1 !important;}:where(.stk-hover-parent:hover,  .stk-hover-parent.stk--is-hovered) .stk-1cf8bea .stk-button:after{background:transparent !important;opacity:1 !important;}.stk-1cf8bea .stk-button__inner-text{font-size:15px !important;color:var(--theme-palette-color-8, #ffffff) !important;font-weight:200 !important;}@media screen and (max-width:999px){.stk-1cf8bea .stk-button__inner-text{font-size:15px !important;}}<\/style><a class=\"stk-link stk-button stk--hover-effect-darken\" href=\"mailto:andreas.frommknecht@ipa.fraunhofer.de\" title=\"andreas.frommknecht@ipa.fraunhofer.de\"><span class=\"has-text-color stk-button__inner-text\">andreas.frommknecht@ipa.fraunhofer.de<\/span><\/a><\/div>\n<\/div><\/div>\n<\/div>\n<\/div><\/div><\/div>\n<\/div><\/div>\n<\/div><\/div><\/div>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-stackable-spacer stk-block-spacer stk--no-padding stk-block stk-56a43b6\" data-block-id=\"56a43b6\"><\/div>\n\n\n\n<div class=\"wp-block-stackable-columns stk-block-columns stk-block stk-fc04401\" data-block-id=\"fc04401\"><style>.stk-fc04401 {padding-right:24px !important;padding-left:24px !important;}<\/style><div class=\"stk-row stk-inner-blocks stk-block-content stk-content-align stk-fc04401-column\">\n<div class=\"wp-block-stackable-column stk-block-column stk-column stk-block stk-a194320\" data-v=\"4\" data-block-id=\"a194320\"><div class=\"stk-column-wrapper stk-block-column__content stk-container stk-a194320-container stk--no-background stk--no-padding\"><div class=\"stk-block-content stk-inner-blocks stk-a194320-inner-blocks\"><div data-block=\"hook:1248\" class=\"alignfull\"><article id=\"post-1248\" class=\"post-1248\"><div class=\"entry-content is-layout-constrained\">\n<div class=\"wp-block-stackable-button-group stk-block-button-group stk-block stk-fcd1a8a\" data-block-id=\"fcd1a8a\"><style>.stk-fcd1a8a {margin-bottom:24px !important;}<\/style><div class=\"stk-row stk-inner-blocks stk-block-content stk-button-group\">\n<div class=\"wp-block-stackable-icon-button stk-block-icon-button stk-block stk-82470ba is-style-ghost\" data-block-id=\"82470ba\"><style>.stk-82470ba .stk-button{background:transparent !important;}.stk-82470ba .stk-button:hover{background:transparent !important;opacity:1 !important;}:where(.stk-hover-parent:hover,  .stk-hover-parent.stk--is-hovered) .stk-82470ba .stk-button:after{background:transparent !important;opacity:1 !important;}.stk-82470ba .stk-button:before{border-style:solid !important;border-color:var(--theme-palette-color-9, #264e5d) !important;}.stk-82470ba .stk-button .stk--inner-svg svg:last-child, .stk-82470ba .stk-button .stk--inner-svg svg:last-child :is(g, path, rect, polygon, ellipse){fill:var(--theme-palette-color-11, #006e92) !important;}<\/style><a class=\"stk-link stk-button stk--hover-effect-darken\" href=\"javascript:window.history.back();\" title=\"Back\"><span class=\"stk--svg-wrapper\"><div class=\"stk--inner-svg\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 512 512\" aria-hidden=\"true\" width=\"32\" height=\"32\"><path d=\"M9.4 233.4c-12.5 12.5-12.5 32.8 0 45.3l128 128c12.5 12.5 32.8 12.5 45.3 0s12.5-32.8 0-45.3L109.3 288 480 288c17.7 0 32-14.3 32-32s-14.3-32-32-32l-370.7 0 73.4-73.4c12.5-12.5 12.5-32.8 0-45.3s-32.8-12.5-45.3 0l-128 128z\"><\/path><\/svg><\/div><\/span><\/a><\/div>\n<\/div><\/div>\n<\/div><\/article><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-5cbd537\" id=\"quick-check\" data-block-id=\"5cbd537\"><style>.stk-5cbd537 {margin-bottom:0px !important;}<\/style><h1 class=\"stk-block-heading__text\">Perception for automated handling of dimensionally stable components (AutoLab)<\/h1><\/div>\n\n\n\n<div class=\"wp-block-stackable-icon-label stk-block-icon-label stk-block stk-9c593c0\" id=\"quick-check\" data-block-id=\"9c593c0\"><style>.stk-9c593c0 .stk-inner-blocks{gap:8px !important;}<\/style><div class=\"stk-row stk-inner-blocks stk-block-content\">\n<div class=\"wp-block-stackable-icon stk-block-icon has-text-align-left stk-block stk-624347f\" data-block-id=\"624347f\"><style>.stk-624347f .stk--svg-wrapper .stk--inner-svg svg:last-child{height:16px !important;width:16px !important;}.stk-624347f .stk--svg-wrapper .stk--inner-svg svg:last-child, .stk-624347f .stk--svg-wrapper .stk--inner-svg svg:last-child :is(g, path, rect, polygon, ellipse){fill:var(--theme-palette-color-10, #25bae2) !important;}<\/style><span class=\"stk--svg-wrapper\"><div class=\"stk--inner-svg\"><svg style=\"height:0;width:0\"><defs><lineargradient id=\"linear-gradient-624347f\" x1=\"0\" x2=\"100%\" y1=\"0\" y2=\"0\"><stop offset=\"0%\" style=\"stop-opacity:1;stop-color:var(--linear-gradient-624347-f-color-1)\"><\/stop><stop offset=\"100%\" style=\"stop-opacity:1;stop-color:var(--linear-gradient-624347-f-color-2)\"><\/stop><\/lineargradient><\/defs><\/svg><svg data-prefix=\"fa\" data-icon=\"star\" class=\"svg-inline--fa fa-star fa-w-18\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 576 512\" aria-hidden=\"true\" width=\"32\" height=\"32\"><path fill=\"currentColor\" d=\"M259.3 17.8L194 150.2 47.9 171.5c-26.2 3.8-36.7 36.1-17.7 54.6l105.7 103-25 145.5c-4.5 26.3 23.2 46 46.4 33.7L288 439.6l130.7 68.7c23.2 12.2 50.9-7.4 46.4-33.7l-25-145.5 105.7-103c19-18.5 8.5-50.8-17.7-54.6L382 150.2 316.7 17.8c-11.7-23.6-45.6-23.9-57.4 0z\"><\/path><\/svg><\/div><\/span><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-cdc2d3f\" id=\"ai-innovation-seed\" data-block-id=\"cdc2d3f\"><p class=\"stk-block-heading__text\">AI Innovation Seed<\/p><\/div>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-c21bed1\" id=\"ubersicht\" data-block-id=\"c21bed1\"><h2 class=\"stk-block-heading__text\">Overview<\/h2><\/div>\n\n\n\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-65e3563\" data-block-id=\"65e3563\"><style>.stk-65e3563 {column-count:1 !important;}<\/style><p class=\"stk-block-text__text\">Fraunhofer IPA aims to establish a project with creative and proactive industrial partners from Baden-W\u00fcrttemberg who share common interests and wish to exchange experiences. <br><br>In the project \"Perception for the automated handling of dimensionally stable components (AutoLab)\", Fraunhofer IPA shares its expertise, particularly in the field of artificial intelligence. <br>intelligence (AI), with the partners and provides insights into their own areas of application. The industrial partners can thus transfer the benefits of using AI to their own applications and benefit from the project results. The project is scheduled to start in January 2022 and run for 12 to 18 months.<br><br>A wire harness is the most expensive part in the assembly of electromechanical systems. Almost all assembly processes with complex and varied components are carried out manually. It takes a lot of time and effort to understand customer specifications and design them according to requirements. This tedious task is a bottleneck in the production of electromechanical systems. Perception for automated handling of dimensionally stable components (AutoLab) enables the assembly process to handle even complicated cases correctly. The AutoLab project consists of three main processes: Perception, Handling and Inspection. The different components are automatically classified by the perception system using machine learning. The handling system then performs and optimizes the assembly process based on the identified requirements. Finally, the results of the assembly are monitored in the inspection phase to check that they have been carried out correctly.<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-image stk-block-image has-text-align-left stk-block stk-2fa484b\" data-block-id=\"2fa484b\"><style>.stk-2fa484b .stk-img-figcaption{font-size:12px !important;}.stk-2fa484b .stk-img-wrapper{width:698px !important;}@media screen and (max-width:999px){.stk-2fa484b .stk-img-figcaption{font-size:12px !important;}}<\/style><figure><span class=\"stk-img-wrapper stk-image--shape-stretch stk--has-lightbox\"><img loading=\"lazy\" decoding=\"async\" class=\"stk-img wp-image-2713\" src=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb1.jpg\" width=\"698\" height=\"412\" alt=\"Illustration wiring harness, source: msk.nina\/Adobe Stock\" srcset=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb1.jpg 698w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb1-400x236.jpg 400w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb1-18x12.jpg 18w\" sizes=\"auto, (max-width: 698px) 100vw, 698px\" \/><\/span><figcaption class=\"stk-img-figcaption\">Figure 1: Wiring harness, source: msk.nina\/Adobe Stock<\/figcaption><\/figure><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-8c26049\" id=\"perzeption\" data-block-id=\"8c26049\"><h2 class=\"stk-block-heading__text\">Perception<\/h2><\/div>\n\n\n\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-ab6a506\" data-block-id=\"ab6a506\"><style>.stk-ab6a506 {column-count:1 !important;}<\/style><p class=\"stk-block-text__text\">In the conception phase, customer specifications are analyzed using artificial intelligence. A 2D drawing plan designed by the customer describes their requirements, e.g. the arrangement and type of assembly parts and at which angles the objects are to be installed. The different types of information in the 2D drawing are extracted by object recognition, as shown in Figure 2, and optical character recognition (OCR). The object recognition method recognizes instances of objects of a certain class and perceives the position of the objects. In addition, the size or angle of the object is captured by the OCR technology. All captured data is calculated to map the relevant information in the right place. This calculated data is forwarded to the next step so that the robot can perceive the requirements.<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-image stk-block-image has-text-align-left stk-block stk-6491464\" data-block-id=\"6491464\"><style>.stk-6491464 .stk-img-figcaption{font-size:12px !important;}.stk-6491464 .stk-img-wrapper{width:698px !important;}@media screen and (max-width:999px){.stk-6491464 .stk-img-figcaption{font-size:12px !important;}}<\/style><figure><span class=\"stk-img-wrapper stk-image--shape-stretch stk--has-lightbox\"><img loading=\"lazy\" decoding=\"async\" class=\"stk-img wp-image-2712\" src=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-scaled.png\" width=\"2560\" height=\"1811\" alt=\"Illustration wiring harness, source: msk.nina\/Adobe Stock\" srcset=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-scaled.png 2560w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-400x283.png 400w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-698x494.png 698w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-768x543.png 768w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-1536x1086.png 1536w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-2048x1448.png 2048w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb2-18x12.png 18w\" sizes=\"auto, (max-width: 2560px) 100vw, 2560px\" \/><\/span><figcaption class=\"stk-img-figcaption\">Figure 2: Understanding 2D drawing plans - Kim, H. (2021), industry project with BAW, source: Fraunhofer IPA<\/figcaption><\/figure><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-76475ee\" id=\"handhabung\" data-block-id=\"76475ee\"><h2 class=\"stk-block-heading__text\">Handling<\/h2><\/div>\n\n\n\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-3e86ca0\" data-block-id=\"3e86ca0\"><p class=\"stk-block-text__text\">The extracted customer requirements for wire harness assembly are used as input information for the handling module. In this module, the product requirements as well as the positions and tolerances of additional components are used to model the final product. This is transferred to a physics simulation and provided with a robot cell and a robot. This simulation environment forms the basis for the machine learning algorithm. Parts handling and the subsequent assembly of the final product are controlled using a hybrid learning approach. This consists of a classic robot control algorithm and current \"stateoftheart\" machine learning algorithms. The assembly process is learned completely offline, resulting in cost-efficient training, continuous production without interruptions and early improvement loops. Several methods are used to transfer the learned control algorithm into reality, including domain adaptation and domain randomization. The combination of these methods allows high-frequency<br>Production adaptations due to the flexibility of the approach, the simultaneous learning of multiple requirements and the simplicity of adapting the automated pipeline.<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-image stk-block-image has-text-align-left stk-block stk-f8ecba8\" data-block-id=\"f8ecba8\"><style>.stk-f8ecba8 .stk-img-figcaption{font-size:12px !important;}.stk-f8ecba8 .stk-img-wrapper{width:698px !important;}@media screen and (max-width:999px){.stk-f8ecba8 .stk-img-figcaption{font-size:12px !important;}}<\/style><figure><span class=\"stk-img-wrapper stk-image--shape-stretch stk--has-lightbox\"><img loading=\"lazy\" decoding=\"async\" class=\"stk-img wp-image-2711\" src=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb3.png\" width=\"1053\" height=\"317\" alt=\"Illustration wiring harness, source: msk.nina\/Adobe Stock\" srcset=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb3.png 1053w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb3-400x120.png 400w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb3-698x210.png 698w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb3-768x231.png 768w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb3-18x5.png 18w\" sizes=\"auto, (max-width: 1053px) 100vw, 1053px\" \/><\/span><figcaption class=\"stk-img-figcaption\">Figure 3: Diagram of the learning process for an assembly task - Albus, M. (2021), Source: Fraunhofer IPA<\/figcaption><\/figure><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-1e747a7\" id=\"inspektion\" data-block-id=\"1e747a7\"><h2 class=\"stk-block-heading__text\">Inspection<\/h2><\/div>\n\n\n\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-d745445\" data-block-id=\"d745445\"><p class=\"stk-block-text__text\">During this phase, the finished product is automatically inspected using an industrial camera and suitable lighting. The results of the assemblies are recorded by the camera and analyzed using a machine learning process known as a convolutional neural network (CNN).<br><br>In a quick check project with an industrial partner, the possibilities for detecting faults based on customer requirements with high prediction accuracy were demonstrated. The characteristics such as alignment and positioning of components were defined by the industry partner. With the defined requirements, the trained model achieves 100% accuracy in the test set. The reliable results can also be used to optimize the parameters of the handling module.<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-image stk-block-image has-text-align-left stk-block stk-bec9e8a\" data-block-id=\"bec9e8a\"><style>.stk-bec9e8a .stk-img-figcaption{font-size:12px !important;}.stk-bec9e8a .stk-img-wrapper{width:698px !important;}@media screen and (max-width:999px){.stk-bec9e8a .stk-img-figcaption{font-size:12px !important;}}<\/style><figure><span class=\"stk-img-wrapper stk-image--shape-stretch stk--has-lightbox\"><img loading=\"lazy\" decoding=\"async\" class=\"stk-img wp-image-2709\" src=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb4.jpg\" width=\"912\" height=\"440\" alt=\"Illustration wiring harness, source: msk.nina\/Adobe Stock\" srcset=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb4.jpg 912w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb4-400x193.jpg 400w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb4-698x337.jpg 698w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb4-768x371.jpg 768w, https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2025\/08\/KI-Fortschrittszentrum_XXX_AIS_AutoLab_Abb4-18x9.jpg 18w\" sizes=\"auto, (max-width: 912px) 100vw, 912px\" \/><\/span><figcaption class=\"stk-img-figcaption\">Figure 4: Determining the position of fastening clips on the wire harness - Kim, H. (2021), Quick Check project with KroSchu, source: Fraunhofer IPA<\/figcaption><\/figure><\/div>\n<\/div><\/div><\/div>\n<\/div><\/div>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Automated handling of dimensionally unstable components optimizes assembly processes through AI-supported perception, handling and inspection to increase efficiency and quality in production.<\/p>","protected":false},"author":4,"featured_media":2414,"template":"","format":"standard","meta":{"_acf_changed":true,"_gspb_post_css":".gspb_container-id-gsbp-1fa6a74,.gspb_container-id-gsbp-b40a6b1{flex-direction:column;box-sizing:border-box}#gspb_container-id-gsbp-1fa6a74.gspb_container>p:last-of-type,#gspb_container-id-gsbp-b40a6b1.gspb_container>p:last-of-type{margin-bottom:0}#gspb_container-id-gsbp-1fa6a74.gspb_container,#gspb_container-id-gsbp-b40a6b1.gspb_container{position:relative;background-color:var(--wp--preset--color--palette-color-11, var(--theme-palette-color-11, #006e92))}#gspb_container-id-gsbp-b40a6b1.gspb_container{margin-bottom:0;padding:24px 24px 0}#gspb_container-id-gsbp-1fa6a74.gspb_container{display:block;margin:0;padding:24px}"},"bereich":[24],"institut":[],"projektformat":[12],"class_list":["post-2378","projekt","type-projekt","status-publish","format-standard","has-post-thumbnail","hentry","bereich-produktion-qualitaetsmanagement","projektformat-ai-innovation-seed"],"blocksy_meta":[],"acf":[],"_links":{"self":[{"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/projekt\/2378","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/projekt"}],"about":[{"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/types\/projekt"}],"author":[{"embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/users\/4"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/media\/2414"}],"wp:attachment":[{"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/media?parent=2378"}],"wp:term":[{"taxonomy":"bereich","embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/bereich?post=2378"},{"taxonomy":"institut","embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/institut?post=2378"},{"taxonomy":"projektformat","embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/projektformat?post=2378"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}