{"id":14450,"date":"2026-08-01T19:46:29","date_gmt":"2026-08-01T19:46:29","guid":{"rendered":"https:\/\/www.jstconsultancy.cz\/?p=14450"},"modified":"2026-08-01T19:59:25","modified_gmt":"2026-08-01T19:59:25","slug":"how-to-do-ai-in-practice-part-vii-vacuum-plant","status":"publish","type":"post","link":"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/en\/how-to-do-ai-in-practice-part-vii-vacuum-plant\/","title":{"rendered":"How to do AI in practice, part VII, Vacuum plant"},"content":{"rendered":"<p>The second model of the quenching room is a vacuum heat treatment plant used for hardening tools. The type of product is indefinable, it is always a unique item, a part of a tool, a indivudal insert, a mold. It is similar with materials, the list is usually very long, however, the groups of steels for hot work<strong> H11, H13<\/strong>, or for cold work<strong> 1.2379, K110,<\/strong> <strong>HSS<\/strong> high-speed steels prevail.<\/p>\n<p>The heat treatment plant\u00a0 usually has more quenching furnaces with nitrogen overpressure, in our model we will consider 5 furnaces, and twice the number of tempering furnaces, preferably vacuum again. The average charge is 250 kg, the average weight of one part is up to 30 kg, the cycle time for quenching is around 8 hours, as well as the time for the tempering cycle. The quenching room again operates in 24\/7 mode, only with winter and summer shutdowns.<\/p>\n<p><a href=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/chatgpt-image-29-7-2026-23-32-54.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-14443\" src=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/chatgpt-image-29-7-2026-23-32-54-300x225.png\" alt=\"\" width=\"300\" height=\"225\" srcset=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/chatgpt-image-29-7-2026-23-32-54-300x225.png 300w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/chatgpt-image-29-7-2026-23-32-54-1024x768.png 1024w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/chatgpt-image-29-7-2026-23-32-54.png 1448w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>For some processes, the cycle time can be defined from closing the gate to opening it, but cycles with two or three tempering temperatures are also possible, without opening the gate, only between two tempering temperatures we have to cool the parts to 50 C. In this sector, we usually work with<strong> thermocouples T<sub>s<\/sub> and T<sub>c<\/sub><\/strong> for hardening, and with <strong>T<sub>c<\/sub> for tempering.<\/strong><\/p>\n<p>Since each hardening furnace can perform up to 3 hardening cycles per day, 5 furnaces can perform more than 5,000 hardenings per year, with a weight of over 1,200 tons of material. For tempering, we have 10 tempering furnaces at our disposal and they will perform 10,000 tempering cycles per year in the <strong>H+2T mode<\/strong>.<\/p>\n<p>If the 5,000 annual cycles were evenly distributed among 100 materials, only 50 hardening cycles per year would be allocated to one steel grade. But that is not the case. The predominant steel groups are hot work steels (50%), cold work steels (25%), and the rest (25%).<\/p>\n<p>If we were to request data for all combinations:<\/p>\n<ul>\n<li><span style=\"color: #800000;\"><strong>material \u00d7 product \u00d7 furnace \u00d7 fixture \u00d7 quenching pressure \u00d7 tempering mode<\/strong><\/span><\/li>\n<\/ul>\n<p>there would never be enough data. Therefore, a hierarchical structure is again recommended.<\/p>\n<p>A common vacuum quenching model will use all quality records and learn general influences. Unlike a <strong>product model,<\/strong> this type of quenching will work with a <strong>material model<\/strong>. It is not realistic to create a separate<strong> AI<\/strong> model for each product or each steel grade. The data must be grouped according to metallurgical and process families.<\/p>\n<p>For each Product ID, a route should be recorded:<\/p>\n<p><strong>Product ID<\/strong><\/p>\n<p>\u2192 RFQ<br \/>\n\u2192 Quotation Header ID<br \/>\n\u2192 Quotation Line ID<br \/>\n\u2192 Quotation Routing ID<br \/>\n\u2192 Quotation Quality Order ID<br \/>\n&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;-<br \/>\n\u2192 Sales Order Header ID<br \/>\n\u2192 Sales Order Line ID<br \/>\n\u2192 Routing ID<br \/>\n\u2192 Quality Order ID<br \/>\n&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;-<br \/>\n\u2192 Production order ID<br \/>\n\u2192 Hardening Cycle ID<br \/>\n\u2192 Hardening Process Parameters<br \/>\n\u2192 Hardening Furnace ID<br \/>\n\u2192 Tempering 1 Cycle ID<br \/>\n\u2192 Tempering 1 Process Parameters<br \/>\n\u2192 Tempering 1 Furnace ID<br \/>\n\u2192 Tempering 2 Cycle ID<br \/>\n\u2192 Tempering 2 Process Parameters<br \/>\n\u2192 Tempering 2 Furnace ID<br \/>\n&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;&#8212;<br \/>\n\u2192 Quality Order ID &#8211; Hardness<br \/>\n\u2192 Quality Release<br \/>\n\u2192 Packing slip<br \/>\n\u2192 Invoice<\/p>\n<p>Each item of this route has its own data fields. If we have them, it will be an advantage, if we don&#8217;t, we need to consider what the<strong> AI \u200b\u200blayer<\/strong> will bring us. The most important are the data related to the <strong>Production Order ID<\/strong>, and the traceability of the <strong>Production Order ID<\/strong> according to the kiln cycle via the Job ID. The <strong>JOB ID<\/strong> is the identification of the operation from the workflow to the <strong>Production Order ID<\/strong>. The image below shows the cycle planning table from AX2012. On the left is a series of cycles defined as <strong>Cycle ID,<\/strong> on the right are the parts inserted into the cycle, with the <strong>JOB ID.<\/strong><\/p>\n<p><a href=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek4.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-14445\" src=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek4-300x87.png\" alt=\"\" width=\"300\" height=\"87\" srcset=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek4-300x87.png 300w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek4-1024x297.png 1024w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek4.png 1058w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>Therefore, 5,000 hardening cycles and 10,000 tempering cycles do not represent 15,000 independent references, but represent a maximum of 5,000 <strong>complete technological routes<\/strong> per year in the form of e.g. <strong>H+2T.<\/strong><\/p>\n<p>Each route must be linked by one <strong>Product ID, multiple Cycle IDs<\/strong>, \u00a0according to the <strong>Material Family ID<\/strong>. Each group of materials will form a process family, e.g.<\/p>\n<p><a href=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek5.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-14447\" src=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek5-300x106.png\" alt=\"\" width=\"300\" height=\"106\" srcset=\"https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek5-300x106.png 300w, https:\/\/intelligent-kalam.80-240-27-133.plesk.page\/wp-content\/uploads\/2026\/08\/516-obrazek5.png 862w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n<p>Recommended number of routes for one process family:<\/p>\n<ul>\n<li>100\u2013150 routes \u2013 first orientation model,<\/li>\n<li>200\u2013500 routes \u2013 usable pilot,<\/li>\n<li>500\u20131,000 routes \u2013 good production model.<\/li>\n<\/ul>\n<p>A pilot model built in this way can help us not only in the<strong> RFQ<\/strong> process, but especially in assessing the economy, efficiency, reliability and predictability of our production.<\/p>\n<p>What to say in conclusion. From what I have said, it is clear that the transition to AI in the control of the quenching plant is an interesting task. However, in order to have enough correct data for the <strong>AI \u200b\u200blayer<\/strong>, we must have a quality <strong>ERP system<\/strong>, and above all, cycle control. This is not only for the <strong>RFQ process,<\/strong> but mainly for the future. Only the <strong>Cycle is a representative<\/strong> part of our activity, showing how efficient and profitable we really are. Only the profitability of individual sub-furnaces gives us the profitability of the heat treatment plant. Everything else, including the conversion to<strong> Product ID, kg, pcs<\/strong>, <strong>number of orders<\/strong>, is just a breakdown of how effectively we used the cycles.<\/p>\n<p>Only when we deal with cycle management can we start thinking about <strong>AI<\/strong>. <strong>Janusz Kowalewski<\/strong> wrote:<\/p>\n<p><strong>\u201cAI should not be seen as a general technological initiative, but as a targeted operational tool that translates existing data from vacuum furnaces and quality into actionable decisions.\u201d<\/strong><\/p>\n<p>This is only his technical view. Yes, the furnace, even a vacuum one, is an important subject in the <strong>AI \u200b\u200bvision<\/strong>, but it is only a part of the whole that we need to see. If we buy expensive energy, technical gases, we will have unhealthy high wages, even the best behavior and control of the furnace will not ensure the viability of our quenching plant. And we will not find this data in<strong> SCADA<\/strong>. I would rephrase it to:<\/p>\n<p><span style=\"color: #800000;\"><strong>\u201cAI should not be seen as a general technological initiative, but as a targeted operational tool that connects business, technological, quality, capacity and economic data of the heat treatment operation and translates them into specific predictions, recommendations and actionable decisions.\u201d<\/strong><\/span><\/p>\n<p>How to translate it? From the <strong>ERP<\/strong> we have perfect data, verified by accounting, about what happened.<\/p>\n<p><span style=\"color: #800000;\"><strong>But AI will tell us what will happen!!!<\/strong><\/span><\/p>\n<p>And so it is up to us whether we leave the prediction of the behavior of the heat treatment plant to us, who are fallible, to our experience and knowledge, or we hand it over to <strong>AI.<\/strong><\/p>\n<p>Once in the past, I participated in a complaint about our quenching, it was a die-casting die. The customer&#8217;s technologist absolutely convincingly argued how the melt flows in the mold, and where the critical points are. With the addition that he has been doing it for 20 years, so he simply must know. So we put the entire model into <strong>PROCAST<\/strong> and simulated the casting process. Everything was completely different. What does this mean? <strong>Even a 100-fold repeated experience may not be true if we do not have the right information.<\/strong><\/p>\n<p>So for me, yes, <strong>AI<\/strong> is a good tool, but for it to work well, <span style=\"color: #800000;\"><strong>we cannot focus only on the furnace, but must address the entire system.<\/strong><\/span><\/p>\n<p>=======================================================================================================<br \/>\n<span style=\"text-decoration: underline;\"><strong><em><span style=\"font-size: 10px;\">Abbreviations used:<\/span><\/em><\/strong><\/span><\/p>\n<p><span style=\"font-size: 10px;\"><em><strong>AI<\/strong>:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>A<\/strong>rtificial\u00a0<strong>I<\/strong>ntelligence<br \/>\n<\/em><em><strong>API:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/strong>Application Programming\u00a0<strong>I<\/strong>nterface: definovan\u00e9 rozhran\u00ed a soubor pravidel, podle kter\u00fdch si dva syst\u00e9my p\u0159ed\u00e1vaj\u00ed data a p\u0159\u00edkazy. Webov\u00e9 API m\u016f\u017ee b\u00fdt dostupn\u00e9 prost\u0159ednictv\u00edm jedn\u00e9 nebo v\u00edce URL adres, ozna\u010dovan\u00fdch jako endpoints<strong>.<br \/>\n<\/strong><\/em><em><strong>JSON: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/strong>JavaScript\u00a0<strong>O<\/strong>bject\u00a0<strong>N<\/strong>otation: textov\u00fd form\u00e1t pro strukturovan\u00fd p\u0159enos dat. Vznikl v prost\u0159ed\u00ed JavaScriptu, ale dnes je nez\u00e1visl\u00fd na programovac\u00edm jazyce.<br \/>\n<\/em><em><strong>Middleware: \u00a0\u00a0<\/strong>integra\u010dn\u00ed vrstva, kter\u00e1 propojuje syst\u00e9my, transformuje data, \u0159\u00edd\u00ed komunikaci, zabezpe\u010den\u00ed a auditn\u00ed z\u00e1znamy.<br \/>\n<\/em><em><strong>AI model: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/strong>matematick\u00fd nebo statistick\u00fd model, kter\u00fd prov\u00e1d\u00ed klasifikaci, predikci, detekci odchylek nebo doporu\u010den\u00ed.<br \/>\n<\/em><em><strong>OPC UA<\/strong>: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Open Platform Communications nebo OPC Unified Architecture<br \/>\n<\/em><em><strong>AI vrstva: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/strong>\u0159e\u0161en\u00ed sestaven\u00e9 ze standardn\u00edch platforem, konektor\u016f, pravidel a individu\u00e1ln\u011b nakonfigurovan\u00fdch nebo vyvinut\u00fdch model\u016f.<br \/>\n<\/em><em><strong>Document AI:\u00a0<\/strong>Technologie vyu\u017e\u00edvan\u00e1 k\u00a0extrakci vyu\u017e\u00edv\u00e1 k\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Information_extraction\">extrakci informac\u00ed<\/a>\u00a0z ti\u0161t\u011bn\u00fdch i\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Digital_document\">digit\u00e1ln\u00edch dokument\u016f<\/a>, jako jsou obr\u00e1zky, texty, znaky<br \/>\n<\/em><em><strong>DMS:\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 D<\/strong>ocument\u00a0<strong>M<\/strong>anagement\u00a0<strong>S<\/strong>ystem<br \/>\n<\/em><em><strong>FQ<\/strong>: \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>R<\/strong>equest\u00a0<strong>F<\/strong>or\u00a0<strong>Q<\/strong>uotation<br \/>\n<\/em><em><strong>APQP:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>A<\/strong>dvanced\u00a0<strong>P<\/strong>roduct\u00a0<strong>Q<\/strong>uality\u00a0<strong>P<\/strong>lanning<br \/>\n<\/em><em><strong>PPAP:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>P<\/strong>roduction\u00a0<strong>P<\/strong>art\u00a0<strong>A<\/strong>pproval\u00a0<strong>P<\/strong>rocess<br \/>\n<\/em><em><strong>ERP:<\/strong>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<strong>E<\/strong>nterprise\u00a0<strong>R<\/strong>esource\u00a0<strong>P<\/strong>lanning<br \/>\n<\/em><em><strong>OTIF:\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 On Time In Full (%)\u00a0 \u2013\u00a0<\/strong>percentage of orders delivered within the required deadline<br \/>\n<\/em><em><strong>TAT:\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<\/strong><\/em><em><strong>Turn Around Time (hrs) \u2013\u00a0<\/strong>\u00a0the time between order recording and packing slip printing\u00a0<\/em><\/span><\/p>\n<p>=======================================================================================================<br \/>\n<strong>Are you solving a similar problem? I will help you with the analysis\u2026<\/strong><br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a040+ years of experience in the field<br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a030+ years of experience\u2026 HT-PROGRES, Bodycote, Galvamet<br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a0cooperation\u2026 V\u0160B, Czechimplant, ECM Technologies, TAV Vacuum Furnaces, GHC Invest<br \/>\n<img decoding=\"async\" class=\"emoji\" role=\"img\" draggable=\"false\" src=\"https:\/\/s.w.org\/images\/core\/emoji\/17.0.2\/svg\/2714.svg\" alt=\"&#x2714;\" \/>\u00a012+ years of expert activity<br \/>\nWant to ask for a solution or want a non-binding consultation? Click on this link, I will usually respond within 24 hours.\u00a0<a href=\"mailto:stanislav.jirka@gmail.com\">Contact email<\/a><br \/>\n========================================================================================================<br \/>\n<strong>Ji\u0159\u00ed Stanislav, Ing. CSc.<br \/>\n<\/strong>Consultant and forensic expert<br \/>\n========================================================================================================<\/p>\n<p>1\/8\/2026<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The second model of the quenching room is a vacuum heat treatment plant used for hardening tools. The type of product is indefinable, it is always<\/p>\n","protected":false},"author":12,"featured_media":14425,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[304],"tags":[],"class_list":["post-14450","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-in-ht-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How to do AI in practice, part VII, Vacuum plant - JST Consultancy<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"cs_CZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to do AI in practice, part VII, Vacuum plant - JST Consultancy\" \/>\n<meta property=\"og:description\" content=\"The second model of the quenching room is a vacuum heat treatment plant used for hardening tools. 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