Optimized quality monitoring for your laser process with Artificial Intelligence

Production processes are subject to constant change, which requires new and innovative solutions. By using artificial intelligence, manufacturers now have the opportunity to optimize their production processes even further.

The Laser Welding Monitor LWM with its latest AI function represents a breakthrough in the analysis of laser welding emissions. With this technology, physical properties such as the maximum force that the weld seam can withstand can be easily predicted. This enables quantification and increases objectivity when analyzing the weld.

The Laser Welding Monitor, LWM detects process irregularities in a fully automated production environment and is the tool of choice to fulfil your quality requirements. Every single laser weld is monitored, analyzed and documented to ensure 100% traceability and consistency in series production.

 

Prediction and streamlined processes

LWM AI foresees key properties for precise acceptable weld limits

Streamlines processes with a single value defining acceptable boundaries

Assessment and quantification

LWM AI offers quantifiable results, enhancing objectivity

Adaptability and analysis

Get valid results without retraining, while changing the process parameters in a certain range

LWM AI identifies partial root causes, aiding defect classification and rework strategies

Battery contacting and hairpin welding with Artificial Intelligence

With the LWM Artificial Intelligence, emissions are being used to extract data for the physical properties of laser weld.

The feature has successfully been tested for the applications of battery contacting and hairpin welding. For battery contacting, the maximum force of the weld can successfully be predicted.

For hairpin welding, the effective cross section can be predicted. To some extent, even the root cause of a low-quality weld such as lack of laser power, wrong focal position can be foreseen (classification). Knowing the root-cause, an advanced and adapted rework strategy can be applied by the end-user. Due to the advanced training of the model, the user can vary pretrained process parameters within a certain range, such as the laser power, while the model is still valid.

 

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