3-D Shape Matching of a Blade Surface in Robotic Grinding Applications

Abstract

Robotic grinding is a promising technique to generate the final shape of blades. It can relieve human from participating in dirty and noisy environments, improve product quality, and lower production costs. One important task in robotic grinding is 3-D shape matching. However, existing matching methods do not consider the requirements associated with different grinding allowances, which can potentially lead to an unstable grinding force. This paper proposes a novel shape matching method for robotic grinding. The goal is to define a new objective function considering different allowance weights for stable grinding, and address incorrect shape matching from the missing points or uneven density points. The main contribution of this paper is the application of variance minimization to construct an objective function, from which the required shape matching parameters are iteratively calculated. This method balances the contributions of all the measured points, weighs the allowances for the pressure and suction surfaces of a blade, and avoids incorrect matching tendencies for high-density points. It is advantageous to maintaining a relatively stable grinding force. The effectiveness of this method is verified through simulations and scanning/grinding experiments of different blades.