Akademik Veri Yönetim Sistemi
ASES International Kayseri Scientific Research Conference, Kayseri, Türkiye, 1 - 03 Eylül 2023, cilt.1, ss.254-258
Laser scanning is utilized in a variety of mechanical engineering applications, including
industrial, reverse engineering, and quality control. Mechanical dimensional examination is just
one of the several uses. In comparison to other standard metrology technologies and
methodologies, laser scanning technology enables for higher resolution and significantly faster
3D digitization. Laser scanning has become one of the most essential means of gathering
information on structures of importance to engineering surveys. Its key benefit over other
methods is the ability to generate a whole model of the structure rather than simply its selected
geometrical parameters. The output of scanning is a point cloud, which serves as the foundation for creating a 3D model, followed by its characteristic elements such as axes, edges, cross
sections, surface areas, and so on. When modeling of the complete structure is not required, or
is too difficult, it can be simplified to approximation of the desired aspects of the structure
alone, or retrieve selected information straight from the cloud. Regardless of the end purpose
or kind, a continuous model of the structure, rather than a discrete, dense point cloud, allows
for a significant reduction in the amount of data that must be saved and allows for the
comparison of periodic measurements. Artificial intelligence (AI) is becoming more common
as digital and mobile technology advances. The impact of machine learning has increased in
these industries thanks to new hardware and cloud-based solutions. The gathered 3D laser point
cloud data may be utilized for mapping, measurement, analysis, modeling, monitoring, virtual
reality, and other activities. The acquired 3D point cloud data and 3D modeling results may be
transformed into standard format and exported into file formats that can be recognized and
processed by other engineering applications. Machine learning has had a greater impact on these
areas since the launch of new hardware and cloud-based solutions.