Rigid Shape Matching for 3-D Robotic Grinding Measurement With Applications to Blades

Abstract

Robotic grinding is a promising method to form the surfaces of industrial components, which improves product quality, lowers machining costs, and reduces processing time. Allowance optimization is a surface matching process, which is important to grinding measurement. The grinding efficiency highly depends on the results of the surface matching between measured points and the design model. However, existing matching methods are usually based on distance and variance minimization, ignoring the grinding requirements to different surfaces, which leads to high processing time and unstable grinding quality. In this article, we proposed a novel surface matching method for robotic grinding. The objective function is constructed via time minimization and involving weights for allowance requirements to different surfaces. This method also balances all measured points’ contributions, avoiding matching distortion due to uneven points’ distribution. Experimental results and comparisons verify the effectiveness of our methods. Comparing to existing matching methods, our method is robust to uneven point distribution and has advantages in reducing the grinding time.