Effect of Three-Dimensional (3D) Scanning Factors on Minimizing the Scanning Errors Using a White LED Light 3D Scanner

Abstract

Highlights: What are the main findings? Of the main effects, MW and simplification (70%) were found to be optimal levels of watertight and simplification in almost all the scanning modes, regardless of sample geometry. Moreover, two sample geometries were scanned three dimensionally and are referred to as simple and complex samples in this research; The scanning errors were found to be lower for the complex sample, i.e., up to 0.1%, when compared to errors for the simple sample. The reduction in error up to such an extent was never reported before; What are the implications of main findings? Three-dimensional scanning research implies that the scanning of complex product components is preferred to the scanning of simple components, which may be easily modelled geometrically. Mode A of 3D scanning for complex sample was evaluated to be the optimal among all other modes of 3D scanning. The largest problem with scanning real objects involves bearing the huge costs of scanning and the low quality of point cloud data for a scanned object, thereby increasing the cost and lead time. Therefore, a need exists to improve the quality of scanning to save time, costs, and computational resources. In this research, the levels of optimal factors associated with a three-dimensional (3D) scanner were investigated, improving the quality of 3D scanning data. Optimizing the 3D scanner factors could help us acquire errorless digital scanned data that accurately resemble a 3D physical object and which may be further used in various engineering applications, e.g., additive manufacturing and non-engineering applications. For this study, four modes of 3D scanning (A, B, C, and D) were utilized with five crucial 3D scanning factors namely texture, watertightness, simplification, and alternate deployments of smoothness and sharpness. This research was divided into two stages. The former stage involved the 3D scanning of two samples with simple and complex geometrical intricacies and the later stage involved checking the scanned objects for any dimensional errors. A coordinate measuring machine (CMM) was used to measure the dimensional details of the real objects. For virtual metering, Solidworks was utilized. With reference to the limited literature in the current context, 3D scanning errors were highly reduced for the first time up to 0.1% for the complex sample when compared to the errors found for the simple sample.