{"id":427,"date":"2024-02-22T15:58:19","date_gmt":"2024-02-22T14:58:19","guid":{"rendered":"http:\/\/h-nu.net\/?page_id=427"},"modified":"2026-08-20T13:14:15","modified_gmt":"2026-08-20T11:14:15","slug":"ai-and-laser-processing","status":"publish","type":"page","link":"https:\/\/h-nu.net\/index.php\/ai-and-laser-processing\/","title":{"rendered":"AI and laser processing"},"content":{"rendered":"\n
\"Eric<\/figure>
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At Amplitude, I faced a persistent challenge: the extensive time required for process development in laser technology. In spite of a global network of application labs, it was difficult to keep up with a growing demand for customer-specific solutions.<\/p>\n\n\n\n

From the very beginning, Artificial Intelligence appeared as a key for simpler, faster and more efficient process development. Still, much remains to be done in integrating AI with laser technology.<\/p>\n<\/div><\/div>\n\n\n\n

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Starting the journey<\/h2>\n\n\n\n

At h-nu, our mission is to turn the promise of AI in laser technology into practical, impactful tools to address today’s challenges. Our approach is to start with a down-to-earth, engineering driven vision, and develop smart, AI-based solutions tailored for laser material processing.<\/p>\n\n\n\n

To achieve this, we’ve outlined a straightforward, three-step strategy for typical projects:<\/p>\n\n\n\n

Step 1: Data Acquisition<\/h3>\n\n\n\n

The first challenge in applying machine learning to laser processing is data acquisition. The success of many machine learning algorithms depends on the quality and volume of data. However, gathering this data is often slow, and manual labeling is seen as a tedious task by most process engineers.<\/p>\n\n\n\n

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\"Data<\/figure>\n<\/div>\n\n\n\n
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How we can help<\/h4>\n\n\n\n