{"id":84086,"date":"2025-07-09T17:15:01","date_gmt":"2025-07-09T15:15:01","guid":{"rendered":"https:\/\/www.inpro.de\/?p=84086"},"modified":"2025-08-26T17:45:17","modified_gmt":"2025-08-26T15:45:17","slug":"ai-enhanced-part-production-application","status":"publish","type":"post","link":"https:\/\/www.inpro.de\/en\/ai-enhanced-part-production-application\/","title":{"rendered":"AI-enhanced weldability testing"},"content":{"rendered":"

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Testing the weldability of multi-sheet joints, e.g. 3-sheet joints, is an important technical prerequisite for the producibility of car bodies. Testing so far has been mainly done by experiment. Read in this article how a dedicated Machine Learning model is now used to forecast the weldability of multi-sheet joints and, therefore significantly reduces the experimental testing effort.<\/p>\n<\/div>

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The challenge<\/h2><\/div>
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\n

In every new car project, the material mix is defined during the development phase. With respect to the car body, the definition of the material mix concerns different metal grades and thicknesses, as well as coatings. With an ever-increasing number of new metal grades and thinner sheet metal thicknesses, the possibilities for selecting and defining materials according to thickness for multi-sheet joints are increasing. Production planning engineering is responsible for ensuring producibility. While a considerable number of multi-sheet joints are well known with respect to their weldability, new material combinations in terms of alloys, thicknesses, and coatings frequently appear, so they must be checked for suitability for spot welding. Checking for weldability requires considerable experimental effort and typically necessitates the procurement of third-party labs.<\/p>\n<\/div><\/div><\/div>

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The solution<\/h2><\/div>
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\n

Based on an existing dataset containing information on material combinations, physicochemical and technical properties, material thickness, coating presence\/absence, and linking this with weldability information (yes\/no), a labelled dataset was created. This feature vector comprises 176 parameters and contains all the information required to train a machine learning model that can predict the weldability of new material combinations with 99.9% accuracy. The core module (the AI\/machine learning module) is part of an existing application with a web front end that allows users to import lists of welding points for analysis. This initial list is then automatically transformed into a material-thickness combination list, to which the machine learning model is applied. The result, indicating whether or not the welding points can be welded, is then provided as output. The entire workflow is controlled via the web front end, including the transformation of the various data sets and the execution of the machine learning model.<\/p>\n<\/div><\/div><\/div>

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The result<\/h2><\/div>
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The solution significantly reduces the amount of work required for experiments, since it automates the evaluation of weldability checks. This contributes to reducing indirect factory costs.<\/p>\n<\/div><\/div><\/div>

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What our customers & partners say<\/h1>\n<\/div>
“Our new AI solution to automate weldability checks for all major VW car brands has significantly reduced the cost-intensive testing efforts at our technology center and affiliated labs. “<\/q><\/blockquote>
Sebastian Kuntze<\/strong>, VW | Technology Planner at Volkswagen<\/span><\/span><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>

Discover more industrial metaverse & simulation use cases<\/h1>\n<\/div>
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Quick Win: AI chatbots in production<\/h2><\/a><\/div>
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\n

As an innovation incubator, we pick up on disruptive technologies at an early stage and match them with value-adding use cases. We have used the potential of large language models and complementary methods to develop an AI chatbot to support store floor employees in the efficient handling of technical documentation and operating manuals.<\/p>\n<\/div>

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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 are reaching their limits. An AI-supported analysis of production data helps companies to detect patterns and indications of causes.<\/p>\n<\/div>

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x-io: Scaling AI solutions<\/h2><\/a><\/div>
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Find out how you can scale your AI solutions along the value chain with standardized AI hardware and software components.<\/p>\n<\/div>

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

Testing the weldability of multi-sheet joints, e.g. 3-sheet joints, is an important technical prerequisite for the producibility of car bodies. Testing so far has been mainly done by experiment. 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