{"id":3272,"date":"2026-11-03T16:08:58","date_gmt":"2026-11-03T15:08:58","guid":{"rendered":"https:\/\/www.ai-predict.de\/hesse-lignal\/"},"modified":"2026-11-04T15:29:52","modified_gmt":"2026-11-04T14:29:52","slug":"hesse-lignal","status":"publish","type":"page","link":"https:\/\/www.ai-predict.de\/en\/hesse-lignal\/","title":{"rendered":"Hesse Lignal"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-1a45zty-0979c9e5e54b139d8490bf9c839c6981\">\n.avia-section.av-1a45zty-0979c9e5e54b139d8490bf9c839c6981{\nbackground-repeat:no-repeat;\nbackground-image:url(https:\/\/www.ai-predict.de\/wp-content\/uploads\/2024\/08\/Header-nur-Pinsel.png);\nbackground-position:50% 50%;\nbackground-attachment:scroll;\n}\n<\/style>\n<div id='av_section_1'  class='avia-section av-1a45zty-0979c9e5e54b139d8490bf9c839c6981 main_color avia-section-default avia-no-border-styling  avia-builder-el-0  el_before_av_section  avia-builder-el-first  avia-full-stretch avia-bg-style-scroll av-minimum-height av-minimum-height-15 av-height-percent  container_wrap sidebar_right'  data-section-bg-repeat='stretch' data-av_minimum_height_pc='15' data-av_min_height_opt='percent'><div class='container av-section-cont-open' ><main  role=\"main\" itemprop=\"mainContentOfPage\"  class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-3272'><div class='entry-content-wrapper clearfix'>\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div><div id='av_section_2'  class='avia-section av-17g3pdi-1cb622d81defed139c0aff646af86804 main_color avia-section-huge avia-no-border-styling  avia-builder-el-1  el_after_av_section  el_before_av_section  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-3272'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-160mcp2-19468133a5d14268652dfdf111e97fe5 av_one_half  avia-builder-el-2  el_before_av_one_half  avia-builder-el-first  first flex_column_div  '     ><section  class='av_textblock_section av-149d1o6-784b10af3ee8de853f14897bb3856dd9 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><h2>Hesse Lignal: 6x faster to UV color release<\/h2>\n<p> <strong>From laboratory knowledge to an intelligent AI twin.<\/strong><\/p>\n<p> How can color shades be developed faster for UV-curing lacquers without losing sight of important properties such as reactivity and curing behavior?<\/p>\n<p> Together with Hesse Lignal, <span style=\"color: #e5a100;\">ai<\/span> <span style=\"color: #003b75;\">-predict<\/span> has shown how the intelligent combination of measurement data, physicochemical knowledge and artificial intelligence can significantly improve formulation development.<\/p>\n<p> With our <strong>Deep Knowledge Integration (DKI) approach,<\/strong> development times were reduced by a factor of six and the necessary laboratory tests were significantly decreased in the investigated use case.<\/p>\n<p> <strong>The result:<\/strong> Faster color development, transparent decisions and a return on investment within two years.<\/p>\n<\/div><\/section><\/div><div  class='flex_column av-13awwly-35b8accc01ad405d4700421ed894c62b av_one_half  avia-builder-el-4  el_after_av_one_half  avia-builder-el-last  flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-113wgx2-947d4a54aa4ab2e9f38eacd8ea5d4829\">\n.avia-image-container.av-113wgx2-947d4a54aa4ab2e9f38eacd8ea5d4829 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-113wgx2-947d4a54aa4ab2e9f38eacd8ea5d4829 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-113wgx2-947d4a54aa4ab2e9f38eacd8ea5d4829 av-styling- avia-align-center  avia-builder-el-5  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-3166 avia-img-lazy-loading-not-3166 avia_image ' src=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-773x1030.png\" alt='Laobor UV Hesse' title='IMG_3981_publish'  height=\"1030\" width=\"773\"  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-773x1030.png 773w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-225x300.png 225w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-768x1024.png 768w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-1152x1536.png 1152w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-1536x2048.png 1536w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-1125x1500.png 1125w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-529x705.png 529w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/IMG_3981_publish-scaled.png 1920w\" sizes=\"(max-width: 773px) 100vw, 773px\" \/><\/div><\/div><\/div><\/div>\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-rpvehy-2d4923950c7edbbf4c89cd77acb21184\">\n.avia-section.av-rpvehy-2d4923950c7edbbf4c89cd77acb21184{\nbackground-color:#f2f2f2;\nbackground-image:unset;\n}\n<\/style>\n<div id='av_section_3'  class='avia-section av-rpvehy-2d4923950c7edbbf4c89cd77acb21184 main_color avia-section-default avia-no-border-styling  avia-builder-el-6  el_after_av_section  el_before_av_section  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-3272'><div class='entry-content-wrapper clearfix'>\n<div class='av-column-wrapper-individual av-mobile-columns-flex av-columns-reverse'><div  class='flex_column av-2at0km-0a35ac9f7f0e9ee96332596508cb9452 av_one_half  avia-builder-el-7  el_before_av_one_half  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-k1sx06-64f1b7146eb108961e6f38cbdb64f5ad\">\n.avia-image-container.av-k1sx06-64f1b7146eb108961e6f38cbdb64f5ad img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-k1sx06-64f1b7146eb108961e6f38cbdb64f5ad .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-k1sx06-64f1b7146eb108961e6f38cbdb64f5ad av-styling- avia-align-center  avia-builder-el-8  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" class='wp-image-3093 avia-img-lazy-loading-not-3093 avia_image ' src=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-287x300.jpg\" alt='Hesse Lignal' title='Representation of a component&#039;s expected behavior (red) \/ actual behavior (blue) \/ reliability (black).'  height=\"300\" width=\"287\"  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-287x300.jpg 287w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-984x1030.jpg 984w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-768x804.jpg 768w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-1467x1536.jpg 1467w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-1956x2048.jpg 1956w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-1433x1500.jpg 1433w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6-673x705.jpg 673w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/10\/Baum_gradient6.jpg 2000w\" sizes=\"(max-width: 287px) 100vw, 287px\" \/><\/div><\/div><\/div><\/div><div  class='flex_column av-q1mrie-203d1c40080fb4e68939d1d00df72045 av_one_half  avia-builder-el-9  el_after_av_one_half  avia-builder-el-last  flex_column_div  '     ><section  class='av_textblock_section av-nov2ee-537d813c1aebb77a9a86703a1aff23b1 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><h3 data-pm-slice=\"1 1 &#091;&#093;\">The Challenge: When Color and Curing Must Match<\/h3>\n<p>Developing UV-curable coatings is a complex process. In addition to the desired color, reactivity, curing behavior, and various process conditions must also be taken into account.<\/p>\n<p>The transition to modern LED-UV technology, in particular, places high demands on formulation: LED light sources have narrower emission spectra, which means that existing formulations cannot always be used as-is without adjustments.<\/p>\n<p>In pigmented coating systems, there are additional interactions between color and curing. Changes to individual raw materials can affect several properties at the same time.<\/p>\n<p><strong>The current approach:<\/strong> Experienced formulators develop and test different formulation variants through extensive laboratory testing.<\/p>\n<p><strong>The key question:<\/strong> How can these development steps be systematized to arrive at suitable formulations more quickly while also meeting the technical requirements?<\/p>\n<\/div><\/section><\/div><\/div><!--close flex column wrapper. Autoclose: 1 -->\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='av_section_4'  class='avia-section av-5ydm8a-b291ba802353bbf4cf0112fde2b7bdee main_color avia-section-huge avia-no-border-styling  avia-builder-el-11  el_after_av_section  el_before_av_section  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-3272'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-48zebu-2d3ef78e1c4a93ead12b6719a13e3087 av_one_full  avia-builder-el-12  el_before_av_one_full  avia-builder-el-first  first flex_column_div  '     ><section  class='av_textblock_section av-mvtojutx-af527fb2bfa113751bce2599f924068f '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><h3 data-pm-slice=\"1 1 &#091;&#093;\">The solution: Intelligently combine knowledge, data, and AI<\/h3>\n<p> Instead of advancing color development solely through repeated laboratory experiments, <span style=\"color: #e5a100;\">ai<\/span> <span style=\"color: #003b75;\">-predict<\/span> relied on a systematic approach: <strong>Deep Knowledge Integration (DKI)<\/strong> .<\/p>\n<p>To achieve this, existing formulation and measurement data were combined with physicochemical models and the expertise of Hesse&#8217;s formulators.<\/p>\n<p>It was particularly important to take into account the relationships between <strong>color, photoinitiators, reactivity, and curing behavior<\/strong>.<\/p>\n<p>Based on this, a digital decision-making environment was created in which various formulation alternatives could be calculated and evaluated.<\/p>\n<p><strong>The key difference:<\/strong> Instead of developing individual formulations through trial and error, it was possible to narrow down suitable solution spaces in a targeted manner and evaluate conflicting objectives in a transparent way.<\/p>\n<p>This laid the foundation for faster, more systematic, and data-driven color development.<\/p>\n<\/div><\/section><\/div><div  class='flex_column av-30je0q-ff500364d78a6dd5bd84c0fe4c31cf42 av_one_full  avia-builder-el-14  el_after_av_one_full  avia-builder-el-last  first flex_column_div  column-top-margin'     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-mvtou0hh-2edc9a1363a5d8cab88c30ba1290dac0\">\n.avia-image-container.av-mvtou0hh-2edc9a1363a5d8cab88c30ba1290dac0 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-mvtou0hh-2edc9a1363a5d8cab88c30ba1290dac0 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-mvtou0hh-2edc9a1363a5d8cab88c30ba1290dac0 av-styling- avia-align-center  avia-builder-el-15  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" class='wp-image-3169 avia-img-lazy-loading-not-3169 avia_image ' src=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision.png\" alt='trial and error become decisions' title='trial and error become decisions'  height=\"948\" width=\"1659\"  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision.png 1659w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision-300x171.png 300w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision-1030x589.png 1030w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision-768x439.png 768w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision-1536x878.png 1536w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision-1500x857.png 1500w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/trial-to-decision-705x403.png 705w\" sizes=\"(max-width: 1659px) 100vw, 1659px\" \/><\/div><\/div><\/div><\/div>\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-85ize2-8ed798f29b514662687b33e04e7dd9b9\">\n.avia-section.av-85ize2-8ed798f29b514662687b33e04e7dd9b9{\nbackground-color:#f2f2f2;\nbackground-image:unset;\n}\n<\/style>\n<div id='av_section_5'  class='avia-section av-85ize2-8ed798f29b514662687b33e04e7dd9b9 main_color avia-section-default avia-no-border-styling  avia-builder-el-16  el_after_av_section  el_before_av_section  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-3272'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-5w63kq-c6930ed1cc9be85d0c2eaab5998875c1 av_one_half  avia-builder-el-17  el_before_av_one_half  avia-builder-el-first  first flex_column_div  '     ><section  class='av_textblock_section av-mvtp5ymn-ee44091763fc9c223deb984afaffa5ca '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><h2 data-pm-slice=\"1 1 &#091;&#093;\">The results: Faster development. More informed decisions.<\/h2>\n<p>The use of Deep Knowledge Integration significantly accelerated color development at Hesse Lignal in the case study examined.<\/p>\n<p><strong>Up to 6\u00d7 faster color development<\/strong><\/p>\n<p>The development time required was reduced to about one-sixth of the original effort.<\/p>\n<p><strong>Return on Investment within 2 years<\/strong><\/p>\n<p>Fewer laboratory tests, shorter development cycles, and more efficient decision-making processes made it possible to recoup the investment within two years.<\/p>\n<p>In addition, conflicts between color, reactivity, and curing behavior could be better identified, and various formulation alternatives could be systematically evaluated.<\/p>\n<p><strong>The result: fewer rounds of testing, greater transparency, and a significantly more efficient formulation development process.<\/strong><\/p>\n<\/div><\/section><\/div><div  class='flex_column av-3spene-633ab72e1a6d4f6745aa00e06eb177c6 av_one_half  avia-builder-el-19  el_after_av_one_half  avia-builder-el-last  flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-bbqd6-71a186bd347f1b073afb8a2795da90fc\">\n.avia-image-container.av-bbqd6-71a186bd347f1b073afb8a2795da90fc img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-bbqd6-71a186bd347f1b073afb8a2795da90fc .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-bbqd6-71a186bd347f1b073afb8a2795da90fc av-styling- avia-align-center  avia-builder-el-20  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" class='wp-image-3175 avia-img-lazy-loading-not-3175 avia_image ' src=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison-1030x590.png\" alt='development time Hesse' title='Representation of a component&#039;s expected behavior (red) \/ actual behavior (blue) \/ reliability (black).'  height=\"590\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison-1030x590.png 1030w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison-300x172.png 300w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison-768x440.png 768w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison-1536x879.png 1536w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison-1500x859.png 1500w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison-705x404.png 705w, https:\/\/www.ai-predict.de\/wp-content\/uploads\/2026\/11\/process-comparison.png 1658w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/div>\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-10vf92-861ee0ab18f17be98ef0f3e086f16427\">\n.avia-section.av-10vf92-861ee0ab18f17be98ef0f3e086f16427{\nbackground-color:#003366;\nbackground-image:unset;\n}\n<\/style>\n<div id='av_section_6'  class='avia-section av-10vf92-861ee0ab18f17be98ef0f3e086f16427 main_color avia-section-large avia-no-border-styling  avia-builder-el-21  el_after_av_section  avia-builder-el-last  fallstudie-form avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-3272'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-9fprti-834ca85c328b7a62ee7a51d81b3b42b0 av_two_third  avia-builder-el-22  el_before_av_one_third  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-84s67a-cd403b9c6e3cccbd9e038a46080cdbb7\">\n#top .av_textblock_section.av-84s67a-cd403b9c6e3cccbd9e038a46080cdbb7 .avia_textblock{\ncolor:#ffffff;\n}\n<\/style>\n<section  class='av_textblock_section av-84s67a-cd403b9c6e3cccbd9e038a46080cdbb7 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock av_inherit_color'  itemprop=\"text\" ><h3 data-pm-slice=\"1 1 &#091;&#093;\">Request the full Hesse Lignal case study<\/h3>\n<p><strong>6\u00d7 faster color development\u2014how was this achieved?<\/strong><\/p>\n<p>Learn in our eight-page case study how Hesse Lignal and <span style=\"color: #e5a100;\">ai-predict<\/span> successfully combined existing measurement data, physicochemical knowledge, and AI.<\/p>\n<p>Discover the Deep Knowledge Integration (DKI) approach, the technical implementation, and the results achieved.<\/p>\n<p><strong>Request the full case study now\u2014free of charge and with no 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