{"id":45823,"date":"2020-11-10T12:19:36","date_gmt":"2020-11-10T20:19:36","guid":{"rendered":"https:\/\/www.cohu.com\/?p=45823"},"modified":"2025-03-28T12:45:10","modified_gmt":"2025-03-28T19:45:10","slug":"ai-for-manufacturing-use-cases","status":"publish","type":"post","link":"https:\/\/www.cohu.com\/tignis\/ai-for-industrial-and-manufacturing-use-cases","title":{"rendered":"AI for Industrial and Manufacturing Use Cases"},"content":{"rendered":"\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141\">\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-parallax .av-parallax-inner{\nbackground-repeat:no-repeat;\nbackground-image:url(\/wp-content\/uploads\/2025\/03\/resources-banner-1500x333.jpg);\nbackground-position:50% 50%;\nbackground-attachment:scroll;\n}\n.avia-section.av-m8g28v5p-746d8baae8d89148fb421ccddfa3a141 .av-section-color-overlay{\nopacity:0.3;\nbackground-color:#000000;\n}\n<\/style>\n<div 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.av-special-heading-tag{\nfont-size:33px;\n}\n.av-special-heading.av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a .av-subheading{\nfont-size:15px;\n}\n}\n<\/style>\n<div  class='av-special-heading av-m8g29ww6-eae5135b0895c3f76a92dcd7e248c63a av-special-heading-h1 custom-color-heading blockquote modern-quote  avia-builder-el-2  avia-builder-el-no-sibling  av-inherit-size av-linked-heading'><h1 class='av-special-heading-tag '  itemprop=\"headline\"  >PHYSICS, MACHINES AND DATA<\/h1><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-2pyrzd-6af470dabd56f3b979785905ac98eceb\">\n.flex_column.av-2pyrzd-6af470dabd56f3b979785905ac98eceb{\nborder-radius:0px 0px 0px 0px;\npadding:0px 0px 0px 0px;\n}\n<\/style>\n<div  class='flex_column av-2pyrzd-6af470dabd56f3b979785905ac98eceb av_one_third  avia-builder-el-3  el_after_av_two_third  avia-builder-el-last  flex_column_div av-zero-column-padding  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a\">\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-m8sqnek7-5a54248de211167837b6e7f1c5ebaa5a av-styling- avia-align-center  avia-builder-el-4  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-45060 avia-img-lazy-loading-not-45060 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png\" alt='Tignis, A Cohu Analytics Solution logo in white' title='Tignis-Logo-cohu-tagline-all white-transparent'  height=\"89\" width=\"300\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-300x89.png 300w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1030x305.png 1030w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-768x228.png 768w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1536x455.png 1536w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-2048x607.png 2048w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-1500x444.png 1500w, \/wp-content\/uploads\/2025\/03\/Tignis-Logo-cohu-tagline-all-white-transparent-705x209.png 705w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/div><\/div><\/div><\/div>\n<\/div><\/div><\/main><!-- close content main element --><\/div><\/div><\/div><div id='after_section_1'  class='main_color av_default_container_wrap container_wrap fullsize'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-full alpha units'><div class='post-entry post-entry-type-page post-entry-45823'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-2y29t5-8eac002ebd25dfff14a3d2eed1a49041 av_one_full  avia-builder-el-5  el_after_av_section  el_before_av_post_metadata  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266\">\n#top .av_textblock_section.av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 .avia_textblock{\nfont-size:40px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8gaohgy-e779b05cc50cec788c64ad3beca2e266 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p style=\"text-align: center;\"><strong>AI for Industrial and Manufacturing Use Cases: Remember that Machine Learning is Never Done<\/strong><\/p>\n<\/div><\/section><\/div>\n<div  class='av-post-metadata-container av-av_post_metadata-f2d93833baf55c47e6bd6c86e40f308e av-metadata-container-align-center  avia-builder-el-7  el_after_av_one_full  el_before_av_hr  av-metadata-container-1'><div class='av-post-metadata-container-inner'><span class=\"av-post-metadata-content av-post-metadata-meta-content\"><span class=\"av-post-metadata-content av-post-metadata-published\"><span class=\"av-post-metadata-published-date\" >10. November 2020<\/span><\/span><span class=\"av-post-metadata-content av-post-metadata-separator\">|<\/span><span class=\"av-post-metadata-content av-post-metadata-category\"><span class=\"av-metadata-before av-metadata-before-categories\">in<\/span><span class=\"av-post-metadata-category-link\" ><a href=\"https:\/\/www.cohu.com\/category\/thought-leadership\/\" >Thought Leadership<\/a><\/span><\/span><\/span><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8t25391-39260fa6e7d6022a5f36742c2f35864e\">\n#top .hr.hr-invisible.av-m8t25391-39260fa6e7d6022a5f36742c2f35864e{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8t25391-39260fa6e7d6022a5f36742c2f35864e hr-invisible  avia-builder-el-8  el_after_av_post_metadata  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-1ltc3hq-d678e11ab7342dc0fff705952edff81f av_one_full  avia-builder-el-9  el_after_av_hr  el_before_av_hr  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-14x5t1q-4915023948c9654ef82725de43f05046\">\n.avia-image-container.av-14x5t1q-4915023948c9654ef82725de43f05046 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-14x5t1q-4915023948c9654ef82725de43f05046 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-14x5t1q-4915023948c9654ef82725de43f05046 av-styling- avia-align-center  avia-builder-el-10  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-45824 avia-img-lazy-loading-not-45824 avia_image ' src=\"\/wp-content\/uploads\/2025\/03\/AI-for-Industrial-and-Manufacturing-Use-Cases-banner-image-1030x361.png\" alt='' title='AI-for-Industrial-and-Manufacturing-Use-Cases-banner-image'  height=\"361\" width=\"1030\"  itemprop=\"thumbnailUrl\" srcset=\"\/wp-content\/uploads\/2025\/03\/AI-for-Industrial-and-Manufacturing-Use-Cases-banner-image-1030x361.png 1030w, \/wp-content\/uploads\/2025\/03\/AI-for-Industrial-and-Manufacturing-Use-Cases-banner-image-300x105.png 300w, \/wp-content\/uploads\/2025\/03\/AI-for-Industrial-and-Manufacturing-Use-Cases-banner-image-768x269.png 768w, \/wp-content\/uploads\/2025\/03\/AI-for-Industrial-and-Manufacturing-Use-Cases-banner-image-705x247.png 705w, \/wp-content\/uploads\/2025\/03\/AI-for-Industrial-and-Manufacturing-Use-Cases-banner-image.png 1200w\" sizes=\"(max-width: 1030px) 100vw, 1030px\" \/><\/div><\/div><\/div><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48\">\n#top .hr.hr-invisible.av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48{\nheight:50px;\n}\n<\/style>\n<div  class='hr av-m8g8t5wg-76ecfb6e051b02404cc71f111581bb48 hr-invisible  avia-builder-el-11  el_after_av_one_full  el_before_av_one_full '><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n<div  class='flex_column av-tc5sy6-c610ce4ce37ddc8755f7c103673cd0c6 av_one_full  avia-builder-el-12  el_after_av_hr  avia-builder-el-last  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035\">\n#top .av_textblock_section.av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 .avia_textblock{\nfont-size:18px;\n}\n<\/style>\n<section  class='av_textblock_section av-m8g8tpp9-ff1fa86bc0dd446fd91b41108625f035 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p>Predictive analytics use cases are validating its value proposition and driving increased investments in artificial intelligence (AI) and machine learning (ML). Real-world examples in manufacturing and other asset-intensive industries demonstrate the technology\u2019s ability to study data patterns in systems, sensors, and processes; automatically detect anomalies; and proactively predict failures and inefficiencies in time to make corrections.<\/p>\n<p>What is not well understood is that ML models require ongoing care and feeding. There is generally an unfounded expectation that ML initiatives are implemented in project form, on a finite timescale. In reality, model implementation is never \u201cdone\u201d because operational objectives, connected systems, process inputs, and personnel tend to change over time. Without continual tuning and retraining, algorithm learnings will eventually become distorted, leading to faulty conclusions and higher risks and costs from missed opportunities to improve reliability, efficiency, and performance.<\/p>\n<p>The return on investment will deteriorate as well when there is a lack of awareness or resources to proactively manage ML models. Selecting a predictive analytics solution that includes oversight by data scientists and subject matter experts \u2014 at no extra cost \u2014 provides protection from excessive lifecycle costs of the advanced technology.<\/p>\n<p><strong>Change is inevitable<\/strong><\/p>\n<p>To optimize predictive analytics and avoid undesirable surprises, attention must be paid to how conditions and variables evolve after ML models are trained and implemented. Examples of what may be encountered include:<\/p>\n<p><u>Incomplete training<\/u>: Systems may have a set of significantly different modes of operation. For example, expected system performance is radically different in startup, shutdown, and steady state modes. Models will decline in prediction quality when they enter a mode for which they have not been trained. Proactive retraining is needed to ensure the models become relevant and accurate.<\/p>\n<p><u>Unexpected inputs<\/u>: An ML model that is not trained on all possible inputs will not predict as well when something unexpected is encountered. For example, if an oil and gas plant\u2019s ML model is trained on a certain composition of crude, or a coal-fired power plant\u2019s model is trained on a certain composition of coal, and somewhere upstream another form of the product enters the process, the model may need to be retrained to accommodate the new input.<\/p>\n<p><u>Evolving priorities<\/u>: Predictive models trained with a high sensitivity to precision will make fewer but more accurate predictions. On the other hand, those trained with a high sensitivity to recall will predict a wider range of problems, though the risk of false positives and irrelevant results is greater. When sensitivity targets shift over time, the model needs to be retrained to the new objectives, even if it is technically predicting the same physical variables.<\/p>\n<p><u>Altered systems<\/u>: Adding new equipment to a system, such as an additional chiller, or upgrading an asset to one with a different power curve, are examples of system alterations that require algorithm retraining. Likewise, if a sensor breaks or is removed, or a new type of sensor is added, retraining becomes necessary.<\/p>\n<p><u>New personnel<\/u>: A new plant manager or engineer may bring a different operating philosophy to the plant. If they are used to operating with setpoints that are different than how a model was originally trained, it must be retrained to reflect the changes.<\/p>\n<p><strong>Managing change is optimal<\/strong><\/p>\n<p>Having ongoing ML oversight services included with your AI solution will help to prevent unexpected risks and decreasing ROI. Tignis\u2019 physics-driven analytics solution includes subject matter expertise for the life of the subscription, at no extra cost. Not only do Tignis ML models automatically retrain on new data as it arrives, but our data science experts will ensure your objectives are always being met by continuously monitoring the performance of your connected systems, and meeting regularly with your teams to discuss model training and optimization opportunities.<\/p>\n<p>Though the enduring need for model tuning is not always well communicated, you need not be caught unaware. Remember that machine learning is never done, and you will be on the right track for analytics optimization.<\/p>\n<\/div><\/section><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":35,"featured_media":45824,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[413],"tags":[],"class_list":["post-45823","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-thought-leadership"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI for Industrial and Manufacturing Use Cases - Tignis | Cohu<\/title>\n<meta name=\"description\" content=\"AI and ML ability to study data patterns in processes automatically detect anomalies; and proactively predict failures and inefficiencies in time to make corrections.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.cohu.com\/tignis\/ai-for-industrial-and-manufacturing-use-cases\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI for Industrial and Manufacturing Use Cases - Tignis | Cohu\" \/>\n<meta property=\"og:description\" content=\"AI and ML ability to study data patterns in processes automatically detect anomalies; 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