{"id":4913,"date":"2026-03-25T13:21:48","date_gmt":"2026-03-25T12:21:48","guid":{"rendered":"https:\/\/www.ki-fortschrittszentrum.de\/?post_type=projekt&#038;p=4913"},"modified":"2026-05-19T15:55:54","modified_gmt":"2026-05-19T13:55:54","slug":"ml-supported-prediction-of-bolt-straightening-quality","status":"publish","type":"projekt","link":"https:\/\/www.ki-fortschrittszentrum.de\/en\/projekt\/ml-gestuetzte-vorhersage-der-schraubenrichtqualitat\/","title":{"rendered":"ML-supported prediction of screw straightening quality"},"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\/2026\/03\/csm_IMG_3563_7fa1cc74a1.jpg) !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-933ef16\" id=\"gspb_container-id-gsbp-933ef16\">\n<div class=\"wp-block-stackable-image stk-block-image has-text-align-left stk-block stk-1690316\" data-block-id=\"1690316\"><style>.stk-1690316 {margin-bottom:36px !important;}.stk-1690316 .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\" src=\"https:\/\/www.ki-fortschrittszentrum.de\/wp-content\/uploads\/2026\/03\/SWG_Logo_DE-698x267.jpg\" width=\"50\" height=\"300\"\/><\/span><\/figure><\/div>\n\n\n\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-97d4068\" data-block-id=\"97d4068\"><style>.stk-97d4068 {margin-bottom:12px !important;}.stk-97d4068 .stk-block-text__text{font-size:16px !important;line-height:1.4em !important;font-weight:500 !important;font-style:italic !important;font-family:Constantia, Lucida Bright, Lucidabright, \"Lucida Serif\", Lucida, \"DejaVu Serif\", \"Bitstream Vera Serif\", \"Liberation Serif\", Georgia, serif !important;}@media screen and (max-width:999px){.stk-97d4068 .stk-block-text__text{font-size:16px !important;}}<\/style><p class=\"stk-block-text__text\">\u00bbA stream is made up of many small rivulets - it is similar in companies, where a stream of costs is formed from numerous individual amounts flowing in unnoticed. As part of the straightening process, we were able to identify a potentially unnecessary source of costs. By collaborating with Fraunhofer on the QuickCheck project, we received confirmation that there is considerable potential for cost optimization with the help of machine learning (ML).\u00ab <\/p><\/div>\n\n\n\n<div class=\"wp-block-greenshift-blocks-container gspb_container gspb_container-gsbp-eafe0ac\" id=\"gspb_container-id-gsbp-eafe0ac\">\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-25dc0f8\" data-block-id=\"25dc0f8\"><style>.stk-25dc0f8 {padding-top:0px !important;padding-bottom:0px !important;margin-top:0px !important;margin-bottom:0px !important;}.stk-25dc0f8 .stk-block-text__text{font-size:15px !important;}@media screen and (max-width:999px){.stk-25dc0f8 .stk-block-text__text{font-size:15px !important;}}<\/style><p class=\"stk-block-text__text has-text-align-left\">Thomas H. Schmid (Operational Controlling)<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-text stk-block-text stk-block stk-d634667\" data-block-id=\"d634667\"><style>.stk-d634667 {align-items:flex-start !important;padding-top:0px !important;padding-bottom:0px !important;margin-top:0px !important;margin-bottom:0px !important;display:flex !important;}.stk-d634667 .stk-block-text__text{font-size:15px !important;font-weight:200 !important;}@media screen and (max-width:999px){.stk-d634667 .stk-block-text__text{font-size:15px !important;}}<\/style><p class=\"stk-block-text__text has-text-align-left\"><\/p><\/div>\n<\/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\">Xinyang Wu<\/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:xinyang.wu@ipa.fraunhofer.de\" title=\"xinyang.wu@ipa.fraunhofer.de\"><span class=\"has-text-color stk-button__inner-text\">xinyang.wu@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\">ML-supported prediction of screw straightening quality<\/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=\"span-data-stk-dynamic-current-page-post-taxonomy-term-projektformat-contenteditable-false-class-stk-dynamic-content-post-taxonomy-placeholder-span\" data-block-id=\"cdc2d3f\"><p class=\"stk-block-heading__text\">Quick Check<\/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=\"ausgangssituation\" data-block-id=\"c21bed1\"><h2 class=\"stk-block-heading__text\">Initial situation<\/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\">In the production of screws, slight initial bending regularly occurs, which is corrected manually before further processing in a hydraulic pressing machine. The screws are supported on two prisms with a fixed distance between them, while the press acts from above and changes the deflection - particularly at the central measuring point MP0. This manual straightening process is time-consuming and labor-intensive, requires specific experience and leads to quality-dependent fluctuations. In addition, individual screws remain outside the permissible tolerances despite pressing, which causes rejects and additional process costs.<br>Around 1000 data sets with four deflection measuring points (MP1, MP2, MP0, MP3) and relevant process parameters such as press position, press force, press stroke and prism distance are available for the analysis. The aim is to develop an ML model that reliably predicts the change in deflection at MP0 based on the initial deflection and the set process parameters. The aim is to automate the straightening process, stabilize the quality of results and reduce the use of resources in the production process.<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-8c26049\" id=\"losungsidee\" data-block-id=\"8c26049\"><h2 class=\"stk-block-heading__text\">Solution idea<\/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\">Two ML methods are used for model-based prediction of the change in deflection at MP0: LightGBM and neural networks. Both methods are suitable for precisely mapping the non-linear dependencies between initial deflection and process-relevant parameters. In addition, feature engineering is used to derive technical characteristics such as local gradients, curvatures and process-related interaction variables. These features reflect central physical effects of the straightening process and improve the model quality. As a result, a robust prediction model can be developed that supports automated and quality-assured control of the straightening process.<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-76475ee\" id=\"nutzen\" data-block-id=\"76475ee\"><h2 class=\"stk-block-heading__text\">Benefit<\/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 experiments show the practical added value of the investigated models for process assessment. Despite moderate R\u00b2 values (MP0 approx. 53 percent), the regressors capture the deflection trend, thus enabling a fundamental assessment of behavior after pressing, even if MP1 shows only low explanatory power. However, the utility of the classification models is particularly high: with over 86 percent accuracy, F1 scores &gt;0.92, and a recall of 95.7 percent, they provide reliable statements about whether MP0 is within tolerance. LightGBM offers additional added value through better probability calibration (ROC AUC). Overall, it is evident that classification is significantly more stable and reliable in the present application and thus provides a robust basis for decision-making in quality assurance.<\/p><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block-heading--v2 stk-block stk-1e747a7\" id=\"umsetzung-der-ki-applikation\" data-block-id=\"1e747a7\"><h2 class=\"stk-block-heading__text\">Implementation of the AI application<\/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\">The implementation of the AI application shows that the regression task can only be solved to a limited extent due to the limited data basis of around 1000 samples. The models cannot reliably map the physical laws of the screw position change in a purely data-driven manner. Physics-informed approaches are much better suited for such data-poor scenarios, as they directly integrate physical relationships and thus enable stable and consistent predictions even with little data. The classification task, on the other hand, can be successfully implemented with both ML models, as the binary decision \u00bbwithin vs. outside the tolerance\u00ab requires significantly less data. Based on this, pressing parameters - especially the pressing position - can be specifically optimized in the future in order to automate the process and reliably ensure quality.<\/p><\/div>\n<\/div><\/div><\/div>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>","protected":false},"excerpt":{"rendered":"<p>An ML model predicts the change in deflection of bolts during the straightening process using measurement data and process parameters, enabling automation and quality assurance. Classification, in particular, is highly successful, predicting with high accuracy whether the bolts will be within tolerance after pressing.<\/p>","protected":false},"author":4,"featured_media":4919,"template":"","format":"standard","meta":{"_acf_changed":true,"_gspb_post_css":".gspb_container-id-gsbp-1fa6a74,.gspb_container-id-gsbp-933ef16,.gspb_container-id-gsbp-eafe0ac{flex-direction:column;box-sizing:border-box}#gspb_container-id-gsbp-1fa6a74.gspb_container>p:last-of-type,#gspb_container-id-gsbp-933ef16.gspb_container>p:last-of-type,#gspb_container-id-gsbp-eafe0ac.gspb_container>p:last-of-type{margin-bottom:0}#gspb_container-id-gsbp-933ef16.gspb_container{position:relative;padding:24px;box-sizing:border-box}#gspb_container-id-gsbp-eafe0ac.gspb_container{position:relative;display:block;margin-left:auto;box-sizing:border-box}#gspb_container-id-gsbp-1fa6a74.gspb_container{position:relative;display:block;margin:0;padding:24px;background-color:var(--wp--preset--color--palette-color-11, var(--theme-palette-color-11, #006e92))}@media (max-width:991.98px){#gspb_container-id-gsbp-1fa6a74.gspb_container{background-color:var(--wp--preset--color--palette-color-11, var(--theme-palette-color-11, #006e92))}}@media (max-width:767.98px){#gspb_container-id-gsbp-1fa6a74.gspb_container{background-color:var(--wp--preset--color--palette-color-11, var(--theme-palette-color-11, #006e92))}}@media (max-width:575.98px){#gspb_container-id-gsbp-1fa6a74.gspb_container{background-color:var(--wp--preset--color--palette-color-11, var(--theme-palette-color-11, #006e92))}}#gspb_container-id-gsbp-1fa6a74.gspb_container{box-sizing:border-box}"},"bereich":[24],"institut":[63,26],"projektformat":[14],"class_list":["post-4913","projekt","type-projekt","status-publish","format-standard","has-post-thumbnail","hentry","bereich-produktion-qualitaetsmanagement","institut-fraunhofer-ipa","institut-ipa","projektformat-quick-check"],"blocksy_meta":[],"acf":[],"_links":{"self":[{"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/projekt\/4913","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\/4919"}],"wp:attachment":[{"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/media?parent=4913"}],"wp:term":[{"taxonomy":"bereich","embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/bereich?post=4913"},{"taxonomy":"institut","embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/institut?post=4913"},{"taxonomy":"projektformat","embeddable":true,"href":"https:\/\/www.ki-fortschrittszentrum.de\/en\/wp-json\/wp\/v2\/projektformat?post=4913"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}