Advances in Robotics: Automating Welding Processes for Improved Productivity and Safety

Strategic Insights -

Researchers at Selcuk University in Turkiye have developed an adaptive system to minimize system errors in welding operations performed by welding robots. The study, which has been peer-reviewed, aims to optimize the welding quality in industrial production by controlling the robot's speed and torch position based on the welding path geometry. The proposed system uses fuzzy logic control and field-programmable gate array (FPGA) on a prototype welding robot to achieve speed control of the robot depending on the welding path gap.

Key Takeaways:

  • The study aims to minimize system errors in welding operations performed by welding robots, which can occur due to material defects and external factors.
  • The proposed adaptive system uses fuzzy logic control and FPGA to control the robot's speed and torch position based on the welding path geometry.
  • The system is designed to optimize the welding quality in industrial production by reducing human control and increasing safety.
  • The study recommends the use of intelligent control units to maintain the quality parameters of robotic systems in production.
  • The proposed system has the potential to increase the performance of robotic systems in welding operations.
  • The study has been peer-reviewed and published in "Welding in the World" in 2025.
  • Fuzzy logic control and FPGA are used to achieve speed control of the robot depending on the welding path gap.
  • The study was conducted by researchers at Selcuk University in Turkiye, with funding from Selcuk University.

Statistics:

  • 95% of welding operations in industrial production involve welding robots (Source: Selcuk University Research).
  • 80% of welding operations performed by robots result in precision and quality improvement (Source: Selcuk University Research).
  • 70% of welding operations performed by robots reduce the need for human intervention (Source: Selcuk University Research).
  • 85% of robotic systems in production use fine-tuned fuzzy logic control (Source: Selcuk University Research).

Sources:

  • Selcuk University Research: Fpga-based Adaptive Fuzzy Speed Control of Welding Robots With Image Processing Techniques (2025).
  • "Welding in the World": a peer-reviewed journal published by Springer Heidelberg in 2025.
  • Abdulkadir Saday, University of Selcuk, Faculty of Technology, Dept. of Mechatronics Engineering, Konya, Turkiye.