In the world of Automobile manufacturing, achieving flawless and defect free results is the primary necessity which cannot be compromised. Laser Welding process has many applications in automobile manufacturing, various parts of automobiles are joined using Laser welding. But this process has some requirements. The weld needs to be precise, strong enough and defect free. However, if the weld strength is not enough, the component may get rejected. There can be many factors which may affect weld strength.

Factors like choosing wrong parameters for process, too high heat input, presence of contaminants, air gaps, etc., and many other process factors and parameters can cause defects in Laser welding. That is why process monitoring is a very crucial factor in the automobile manufacturing process.
Limitations of manual inspection:

• Traditionally, this inspection process was done manually, that is, the components were visually inspected by a human supervisor, or a camera system was fitted and the process was observed. But this had some limitations.
• Human inspectors may leave out some minute defects, which may later cause major defects in performance.
• Visual inspection can be subjective which can cause inconsistencies and quality control issues.
• It depends a lot on experience and knowledge of the human inspector.
• Basic camera systems cannot capture complex welding processes and point out the defects.
• Even after defects are detected, analyzing and finding out the root cause for the defect is a long, time-consuming process.

AI Comes into picture:

Taking into consideration growing demands for production efficiency, industries are now looking to automate this process of process monitoring system.

In the latest process monitoring systems, the camera system and illumination module is coaxially mounted with the laser welding optics, and this system is integrated with AI.

How does it work??

• All the visuals of process are recorded, process parameters used for welding each and every part are recorded.
• These parameters include, but are not limited to Laser Power, Robot Speed, swivel axis, Z position, Wire feed rate, pressure, etc.
• It also detects pores, oil residues and other defects.
  All this data collected by the system is analyzed by deep learning.
• The user defines the errors according to specific needs, let’s say porosity, then the system is accordingly trained to detect the specific error.
• Once the system is trained and installed at the manufacturing line, it automatically detects the defects.
• As the system is trained with more and more data, the system gets more effective and can analyze the cause for defects and store it.
  This is used for further reference, and prevents those defects in further processes.

Conclusion:
Artificial intelligence, though currently in its developing stage, is expected to grow rapidly with more raw data, and rapid technological developments. It will carry out more complex operations and increase the efficiency of production like never before.