Abstract
As promising technology with low requirements and high depositing efficiency, Wire Arc Additive Manufacturing (WAAM) can significantly reduce the repair cost and improve the formation quality of molds. To further improve the accuracy of WAAM in repairing molds, the point cloud model that expresses the spatial distribution and surface characteristics of the mold is proposed. Since the mold has a large size, it is necessary to be scanned multiple times, resulting in multiple point cloud models. The point cloud registration, such as the Iterative Closest Point (ICP) algorithm, then plays the role of merging multiple point cloud models to reconstruct a complete data model. However, using the ICP algorithm to merge large point clouds with a low-overlap area is inefficient, time-consuming, and unsatisfactory. Therefore, this paper provides the improved Offset Iterative Closest Point (OICP) algorithm, which is an online fast registration algorithm suitable for intelligent WAAM mold repair technology. The practicality and reliability of the algorithm are illustrated by the comparison results with the standard ICP algorithm and the three-coordinate measuring instrument in the Experimental Setup Section. The results are that the OICP algorithm is feasible for registrations with low overlap rates. For an overlap rate lower than 60% in our experiments, the traditional ICP algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm.
Highlights
In modern industry, molds can be used for the mass production of high-quality medium-to-large work pieces
The results are that the Offset Iterative Closest Point (OICP) algorithm is feasible for registrations with low overlap rates
Algorithm failed, while the Root Mean Square (RMS) error reached 0.1 mm, and the rotation error was within 0.5 degrees, indicating the improvement of the proposed OICP algorithm
Summary
Molds can be used for the mass production of high-quality medium-to-large work pieces. WAAM combines the additive manufacturing and surfacing repair process using machinery to complete labor-intensive and repetitive welding work, thereby ensuring the stability of welding quality that is not restricted by time, place, and environment. It is one of the most promising directions for automatic mold repair [3]. Establishing one point cloud model of the entire work piece will result in extremely high data recording and processing difficulties These data must be registered to reorganize multiple 3D datasets and align overlapping components.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have