OICP: An Online Fast Registration Algorithm Based on Rigid Translation Applied to Wire Arc Additive Manufacturing of Mold Repair

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.

An Assisted Teaching Method of College English Translation Using Generative Adversarial Network

“…Just because the system adopts the shallow text characteristic analysis technology, its main feature is that it pays more attention to the language surface structure of the composition but ignores the content level of the composition and does not involve the content, organizational structure, and genre of the composition [15]. Wu et al combined dictionary, semantic analysis, and other analysis tools and various resources in the system, which improved the performance of the system to a certain extent, avoided the problem of overreliance on the surface features of the composition, and greatly improved the scoring effect [16]. Cao et al adopted information retrieval technology, screened out the indicators that have an influence on the writing level, and calculated the proportion of influence of each indicator [17].…”

“…Aiming at the similarity of concept words between sentences, on the basis of related research [16], the similarity calculation method of concept degree is rede ned, that is, the semantic similarity value between two concept words is determined to be in the interval [0, 1], and the mutual relationship is as shown in the following formula:…”