Reverse Engineering with 3D Scanning – Automated by Scangineering

Conventional reverse-engineering approaches often stop at the generation of point clouds or polygon meshes. Creating parametric CAD models typically requires manual steps and specialized personnel. Unlike commercially available software, Scangineering relies on a high degree of automation combined with a high level of process customization.

The result is an end-to-end, digitally interconnected process chain with a higher degree of automation, improved data consistency, and reduced development times compared to standard off-the-shelf tools. Instead of a one-size-fits-all application, Scangineering offers a generalizable software solution tailored to specific use cases.

Technological innovations and improvements

Scangineering’s entire workflow has a modular structure and can be adapted to a wide variety of use cases – from individual components to entire industrial environments. The software architecture allows for the integration of customer-specific algorithms and centralized control of all parameters via a unified framework.

Furthermore, Scangineering is designed to be open-source and supports standardized data formats as well as API interfaces to common industry solutions such as Siemens NX, CATIA, SolidWorks or Revit. This open approach ensures maximum compatibility and interoperability within existing system landscapes.

Another innovation lies in the use of hybrid AI methods: Scangineering combines proven geometric methods with modern AI algorithms (e.g., convolutional neural networks and transformer models). This combination enables the precise detection of complex structures in large point clouds – faster, more robust, and more reliable than purely manual or purely geometric processes.

1. Data acquisition: A 3D scanner captures the physical object and generates a detailed point cloud. If necessary, cameras are used to capture surface information via images.
2. Preprocessing: The point cloud is cleaned up. This involves filtering out error points and noise, merging overlapping scans, and optimizing the data set.
3. Segmentation: The cleaned-up point cloud is divided into segments based on geometric features. In this process, the system identifies, for example, planar surfaces, holes, or complex structures. Both rule-based and AI-based methods are used. To additionally segment objects and artifacts from 2D image data, classical computer vision technologies are applied.
4. Classification: Each segment is analyzed and classified as a CAD feature or a complex object (e.g., spherical surface, cylindrical bore, free-form surface). The algorithms recognize the underlying design elements in the point cloud.
5. Reconstruction: The recognized features are converted into a parametric CAD model. The software generates a fully editable 3D solid while taking the original geometry into account. The captured 2D data is integrated into the 3D scene using mapping techniques.
6. Output and data integration: The final model can be exported to common CAE software. This allows, for example, for an adaptive repair process to be initiated or for the as-is data to be further processed in the digital twin. Throughout the entire process, AI-powered algorithms support automation and increase the robustness and speed of segmentation and modeling. Generated data can be integrated into existing IT infrastructures and data ecosystems using standard methods.