A novel grinding path generation method for removing the parting line of large casting

To tackle the issues of low efficiency and inaccurate results in the automated grinding of large castings, this paper presents a pioneering technique for generating grinding paths. The method combines the capabilities of the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms. The RANSAC is used for discovering local feature point clouds, while the ICP estimates geometric deviations due to shrinkage and expansion. The expansion–contraction similarity (ECS) is pivotal to this approach, facilitating direct generation of global grinding path points from scant local ones. After comparing outcomes to two other methodologies and conducting a robot casting component experiment, the effectiveness and superiority of this technique were proved.