Optimization of fixture layouts of glass laser optics using multiple kernel regression

Area of Science:

• Optics and Photonics
• Materials Science
• Mechanical Engineering

Background:

• Glass laser optics require precise support structures to maintain surface integrity.
• Environmental factors like temperature can significantly impact optic performance.
• Existing fixturing models may not fully capture complex structural and thermal interactions.

Purpose of the Study:

• To develop an integrated fixturing model for glass laser optics.
• To minimize surface shape error through optimal clamp configuration.
• To enable control of surface shape error by adjusting clamping forces and temperature.

Main Methods:

• Development of a novel multiple kernel learning method: multiple kernel support vector functional regression.
• Implementation of a two-layer regression approach to group and order data sources.
• Layered analysis to evaluate the distinct influences of clamps and temperature.

Main Results:

• Computation of a near global optimal set of clamps to minimize optic surface shape error.
• Demonstration of achieving a desired surface shape error by manipulating clamping forces and environmental temperatures.
• Successful evaluation of clamp and temperature influences via the layered regression framework.

Conclusions:

• The integrated fixturing model effectively predicts and controls surface shape error in glass laser optics.
• The multiple kernel support vector functional regression offers a robust method for analyzing complex system interactions.
• This approach provides a pathway for enhanced precision and stability in laser optic systems.