{"id":98140,"date":"2026-08-26T17:39:04","date_gmt":"2026-08-26T15:39:04","guid":{"rendered":"https:\/\/www.inpro.de\/?p=98140"},"modified":"2026-09-03T12:58:47","modified_gmt":"2026-09-03T10:58:47","slug":"geometric-deep-learning","status":"publish","type":"post","link":"https:\/\/www.inpro.de\/en\/geometric-deep-learning\/","title":{"rendered":"Geometric Deep Learning: taking the measurement-point work off engineers\u2019 hands"},"content":{"rendered":"

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A new body part comes out of design, and before it can be manufactured, the painstaking work begins: Where do the clamping points for the fixture go? Where are the weld points? Which locations belong in the measurement plan? Up to 23% of an engineering team\u2019s time<\/a> goes into this kind of preliminary work \u2013 points that are effectively re-set by hand for every new part variant, even though the databases often already hold a very similar part from an earlier project for which they were long ago defined. This is exactly where we at inpro come in, with our expertise in Geometric Deep Learning.<\/p>\n<\/div>

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Untapped engineering knowledge in the CAD archives<\/h2><\/div>
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Legacy CAD data is an untapped treasure. A part is redesigned several times over the years \u2013 and each time, defining reference and inspection points starts over from scratch, even though a geometrically almost identical predecessor part already exists. Conventional search systems find only exact duplicates or parts with matching metadata, but no geometric similarity across different naming conventions and storage locations.<\/p>\n<\/div><\/div><\/div>

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Our workflow: find a similar part, transfer the points, validate the result<\/h2><\/div>
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Based on AI and digital twin technologies\u00a0we developed a three-stage workflow together with Volkswagen and Siemens:<\/p>\n

    \n
  1. Search<\/strong> \u2013 For a new part, we automatically find the most geometrically similar reference part in the existing data \u2013 regardless of file name or storage structure.<\/li>\n
  2. Transfer<\/strong> \u2013 We detect which regions of the reference part and the new part correspond to each other and automatically transfer the points already defined there \u2013 clamping, weld, measurement, and inspection points. The engineer only needs to review and fine-tune our proposal.<\/li>\n
  3. Validation<\/strong> \u2013 We validate the transferred concept numerically, for example through structural simulation, before it is released.<\/li>\n<\/ol>\n<\/div><\/div><\/div>
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    Application in manufacturing planning and quality assurance<\/h2><\/div>
    <\/div>
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    We are currently developing this approach for several use cases along the process chain, from manufacturing planning through to quality assurance. Initial pilots on a range of body and part geometries already show that points from a reference part can be reliably transferred to a similar but not identical new part. Further use cases are currently being trialed.<\/p>\n<\/div><\/div><\/div>

    <\/i><\/div>

    The goal: less manual work on safety-critical routine tasks<\/h2><\/div>
    <\/div>
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    Our aim is to relieve engineers of this repetitive but safety-critical task. The necessary hands-on knowledge almost always already exists \u2013 it simply sits in a different CAD model. For the first time, we make it systematically findable and reusable, instead of having to reinvent it for every new project. And our approach is not an isolated solution: it can be integrated as an additional capability into existing engineering and PLM environments, where CAD data, release processes, and quality checks already converge anyway \u2013 for example in the data management, simulation, and quality systems that engineers already work with today.<\/p>\n<\/div><\/div><\/div>

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    More Industrial Engineering innovations<\/h2>\n<\/div>
    \"\"<\/div>

    AI-enhanced weldability testing<\/h2><\/a><\/div>
    <\/div>
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    Testing the weldability of multi-sheet joints is an important technical prerequisite for production. We show how AI shortens this validation step and makes it reproducible.<\/p>\n<\/div>

    <\/div>+<\/a>
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    AI-supported analysis of production data<\/h2><\/a><\/div>
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    In times of growing variant diversity, increasing quality requirements and limited expert resources, traditional analysis methods reach their limits. AI-supported analysis of production data helps companies spot patterns and clues to root causes.<\/p>\n<\/div>

    <\/div>+<\/a>
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    Game changer: computer vision in shopfloor automation<\/h2><\/a><\/div>
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    Find out how to scale your AI solutions along the value chain with standardised AI hardware and software components.<\/p>\n<\/div>

    <\/div>+<\/a>
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    <\/div><\/div><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"

    In this blog post, discover how Geometric Deep Learning turns untapped engineering knowledge locked in CAD archives into a systematically searchable, reusable asset. We’re currently developing the approach across the entire process chain \u2014 from manufacturing planning to quality assurance.<\/p>\n","protected":false},"author":7,"featured_media":98429,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[349,378],"tags":[],"class_list":["post-98140","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogbeitrag-oeffentlich-en","category-blogbeitrag-oeffentlich-en-2"],"yoast_head":"\nGeometric Deep Learning: taking the measurement-point work off engineers\u2019 hands - inpro<\/title>\n<meta name=\"description\" content=\"Every new body panel triggers the same painstaking prep before it can be manufactured \u2014 up to 23% of an engineering team's time spent placing reference points by hand for each new variant, even though a nearly identical, already-defined part often sits in the database from an earlier project. That's exactly where inpro's Geometric Deep Learning comes in.\" \/>\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.inpro.de\/en\/geometric-deep-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quick Win: KI-Chatbots in der Produktion\" \/>\n<meta property=\"og:description\" content=\"Als Innovationsinkubator greifen wir disruptive Technologien fr\u00fchzeitig auf und matchen sie mit wertsch\u00f6pfenden Anwendungsf\u00e4llen. 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