Optimizing the uncertainty of measurements on a coordinate measuring machine when controlling complex geometric surfaces

Abstract

The object of this study is the process of optimizing measurement uncertainty on a coordinate measuring machine (CMM) when inspecting complex geometric surfaces. The problem addressed was insufficient accuracy and efficiency of measurements of complex parts on CMMs under production conditions. A method for optimizing measurement uncertainty has been devised, which includes a mathematical model of the measurement process and an adaptive algorithm for optimizing the control strategy, based on the Monte Carlo method. The model takes into account the geometry of surfaces and CMM characteristics, while the algorithm dynamically adjusts measurement parameters. The results demonstrate a reduction in measurement uncertainty by 15–20 % and a reduction in inspection time by 10–12 % compared to conventional methods. This is achieved by taking into account the specificity of complex surface geometry and an adaptive approach. The uniqueness of the developed method is the ability to automatically adapt to different types of CMMs and measured objects, optimizing the number and location of measurement points, the speed of probe movement, and its contact force with the surface. The method takes into account not only the geometric parameters of objects but also the characteristics of the CMM itself, which allows for high accuracy. The method is particularly effective for parts with complex geometry, in which conventional methods often lead to significant errors. Practical application is possible at machine-building enterprises for quality control of complex parts, especially in serial production. The implementation of the developed method allows for improving product quality and reducing production costs by 8–10 % due to optimization of the control process and reduction of defects