The spare parts play a vital role in sustaining the operation and longevity of products and systems, but their unavailability can lead to prolonged downtime or expensive replacements. The integration of 3D scanning and Additive Manufacturing (AM) presents a promising path for spare part production. However, to utilise the full potential of AM, sometimes, redesign of the original part is needed. This paper investigates and proposes a new approach that integrates reverse engineering and redesign of an original part based on functional analysis to support the manufacturing of AM spare parts.

Additive Manufacturing has transformed modern manufacturing with its well-known advantages. However, shrinkage remains a critical challenge, causing dimensional inaccuracies that should be properly compensated to assure geometric fidelity. This study aims to assess the reliability of a Reverse Engineering (RE) technique for dimensional compensation. A gauge-based measurement approach has been used to validate the RE method. Results confirm that the RE method is promising, while highlighting the intrinsic errors of the RE technique, and suggesting ways to evaluate and prevent them.

In the field of 3D model reconstruction, manifold methods have been developed that derive CAD models from 3D scan data. Opposed to classical CAD modelling, where surface and solid modelling exist, a further diversification of modelling techniques is observed, caused by different methods to build up the geometry. This research introduces a new classification, the so-called Level of Complexities. It can be applied to the complete Reverse Engineering process chain and lays the foundation for further research on how to match requirements arising from all process steps and downstream applications.

In reengineering technical components, the robust automation of reverse engineering (RE) could overcome the need for human supervision in the surface reconstruction process. Therefore, an enhanced computer-based geometric reasoning to derive tolerable surface deviations for reconstructing optimal surface models would promote a deeper geometric understanding of RE downstream processes. This approach integrates advanced surface information into a deep learning-based recognition framework by explicitly labeling geometric outliers and subsurface boundaries. For this purpose, a synthetic dataset is created that morphs nominal surface models to resemble the macroscopic surface pattern of physical components. For the detection of regular geometry primitives, a 3D-CNN is used to analyze the voxelized components based on signed distance field data. This explicit labeling approach enables surface fitting to derive suitable shape features that fulfill the underlying surface constraints.

Truss-like shapes can occur in topology optimization described by an assembly of finite elements or its boundary represented as a polygon mesh. Such shape description does not cover a common engineering parametrization like the lines of a frame structure and its corresponding cross-section. This article addresses the truss-parametrization of such optimization using curve skeletons and Meta Balls. While the curve skeleton is common in the truss-parametrization, including Meta Balls can lead to an overall implicit and smooth shape description.

Ordered data point cloud obtained by laser-scanning, image processing or contact digitizing techniques is widely used for reconstructing a cross section profile in the field of reverse engineering. In this research, a comprehensive algorithm for reconstruction of 2D profile is developed based on NURBS parametric curve theory. In this regard, the line, arc and B-spline segments are firstly extracted from the set of ordered data points using a developed segment detection algorithm. Then, the final profile is reconstructed using the newly proposed algorithm of constrained local fitting which approximates these segments by NURBS curves with appropriate geometric continuity conditions. The validity, capability and functionality of the developed method are investigated by some practical case studies. Results show that developed algorithm can be integrated and improve the functionality of exiting reverse engineering systems.

This paper describes an algorithm for surface reconstruction from a set of scattered three-dimensional points extracted from an image sequence. In this process, additional information (such as location of the viewpoints and the points visible from a particular viewpoint) is available and can be exploited for accurately recovering the shape of the objects portrayed in the images. Initially, the set of points is subjected to a Delaunay triangulation that fills the convex hull of the set of points with disjoint tetrahedra. The key idea of the shape recovery algorithm is to eliminate triangles that obstruct the visibility of points from certain viewpoints, whose locations are known from the image acquisition process. The major contribution of this paper is that we have been able to design an algorithm for surface reconstruction that handles a wide variety of shapes, as opposed to currently existing techniques.