Particle swarm algorithm-based identification method of optimal measurement area of coordinate measuring machine

Area of Science:

• Metrology and Measurement Science
• Mechanical Engineering
• Computational Intelligence

Background:

• Coordinate Measuring Machines (CMMs) are crucial for precise dimensional verification.
• Optimizing the measurement space in CMMs is essential for maximizing accuracy and efficiency.
• Existing methods may not fully address the complex volumetric error distributions in large CMMs.

Purpose of the Study:

• To develop an intelligent method for identifying the optimal measurement area for large CMMs.
• To enhance the precision of CMM measurements through intelligent optimization of measurement space.
• To validate the proposed method using experimental data and high-precision standards.

Main Methods:

• Utilized laser tracer multi-station technology to map volumetric error distribution.
• Employed the inverse distance weighting (IDW) algorithm for volumetric error interpolation.
• Applied the LASSO algorithm to solve the quasi-rigid body model and obtain geometric errors.
• Developed an error optimization model and used particle swarm optimization (PSO) for area identification.

Main Results:

• The PSO algorithm successfully identified an optimal measurement area for a (35 × 35 × 35) mm³ object within specific CMM spatial coordinates.
• The identified optimal area was further refined to a precise range (e.g., 280 mm < X < 315 mm).
• Experimental validation using a high-precision sphere confirmed the accuracy and reliability of the proposed PSO-based method.

Conclusions:

• The proposed particle swarm optimization method effectively determines the optimal measurement area for CMMs.
• This intelligent approach enhances measurement accuracy and efficiency by optimizing CMM spatial utilization.
• The findings provide a valuable tool for improving precision metrology applications.