Online Parameter Optimization in Robotic Assembly Processes

Description:

This technology introduces a method for optimizing performance in manufacturing processes, specifically robotic assembly, through successive iterations of system modeling and process parameter optimization. Utilizing Gaussian regression, the technology iteratively identifies and optimizes a model of the manufacturing process, allowing for real-time adjustments to enhance efficiency and output quality.

Key Advantages:

• Enables real-time optimization of manufacturing processes.
• Improves efficiency and output quality through precise parameter adjustments.
• Reduces cycle times by balancing exploration and exploitation processes.
• Adaptable to various manufacturing environments due to its iterative and model-based approach.
• Utilizes Gaussian Process Regression for accurate modeling and optimization.

Problems Solved:

• Inefficiencies in robotic assembly processes.
• Lack of adaptability in process optimization methods.
• High cycle times in manufacturing processes.
• Difficulty in achieving optimal process parameters in real-time.

Market Applications:

• Robotic assembly lines in automotive, electronics, and consumer goods manufacturing.
• Process optimization software for industrial automation systems.
• Manufacturing process control in industries seeking efficiency and quality improvements.
• Development of intelligent manufacturing systems and solutions.