The growing environmental, economic, and social challenges have spurred a demand for cleaner mobility solutions. In response to the transformative changes in the automotive sector, manufacturers must prioritize digital validation of products, manufacturing processes, and tools prior to mass production. This ensures efficiency, accuracy, and cost-effectiveness.
By utilizing 3D modeling of factory layouts, factory planners can digitally validate production line changes, substantially reducing costs when introducing new products. One key innovation involves creating 3D models using point cloud data from factory scans. Traditional factory scanning processes face limitations like blind spots and periodic scanning intervals. This research proposes using drones equipped with LiDAR (Light Detection and Ranging) technology for 3D scanning, enabling real-time mapping, autonomous operation, and efficient data collection. Drones can navigate complex areas, access small spaces, and optimize factory planning with precise point cloud data. This enables planners to maintain updated layouts and implement necessary modifications for future projects. However, the current manual process of converting point cloud data into 3D models is time-intensive, causing delays in meeting market demand.
To address this, point cloud data is segmented into two categories: (a) standard 3D components from libraries and (b) non-standard components like machines and air ducts. Automating the point cloud-to-3D modeling process yields significant improvements in conversion results. While automation enhances the placement of standard objects with geometric precision, smoother surface finishing is still required for non-standard components.
Keywords – Point cloud, LiDAR Scanning, 3D modelling, factory layout design, automation, python