Normally, the phrase "making the invisible visible" is used when sensors/cameras are used for monitoring purposes that operate in a different spectral range than the human eye.
In this contribution to ICALEO 2023 we will interpret this phrase somewhat differently.
There are several systems available in the market which capture or measure and analyze physical effects of the process zone and their properties during the laser processing, but none of these effects and properties stand for “seam quality” for themselves, which is typically defined by mechanical, geometrical and metallurgical properties of the solidified seam. The process properties like emitted visible and thermal radiation or geometrical values of the melt pool or the penetration depth of the laser forming the keyhole (penetration depth) only provide indications of how the desired quality might be achieved. It is also well known that the introduction of OCT in laser materials processing has significantly increased safety in both defect detection and process control.
However, the focus of this presentation is on the use of artificial intelligence algorithms to "see new things." We will discuss how classified, physical properties can be derived from already reliable process information - "making the invisible visible," so to speak. Instead of defining complex rules for algorithms, the use of Data Science and Machine Learning methods uncovers hidden structures in noisy, unstructured data and makes it possible to find the relationships of the data to physical measurements.