Laser welding is becoming more and more important in the automotive industry and quality of the weld is critical for a successful application. In many cases, the increase in welding speed provided by laser welding has caused the welding system operator to be unable to keep up with the production rate while fully inspecting each part. Therefore, either additional inspectors are required or some form of real-time on-line inspection of the weld must be provided. This is especially necessary where the laser weld properties are critical to the final performance.

This thesis describes a system for the prediction of various parameters of the fusion zone of a weld from the emitted radiation during laser welding. A neural network system is used to associate data from three photodiode sensors to geometrical properties of the fusion zone measured in cross-section.

A machine welding automotive transmission gears with a CO₂ laser was used to test the system. The neural network system was able to predict, with acceptable accuracy, two of the most important parameters describing the geometry of the fusion zone:​ the total area and the lateral position of the fusion zone relative to the weld seam. The system shows promise in being able to predict unacceptable welds if incorporated as part of an on-line quality monitoring process.