Increase of automation and autonomy of production is the latest trend incorporated into Industry 4.0 objectives. Production autonomy is very desirable in the field of damaged parts replacement. To fulfill this goal numerous reverse engineering systems have been developed that support geometry recognition from the 3D scan data. This study is focused on converting non-parametric geometry representation of shaft-type elements into a CAD model with a rebuilt feature tree. Algorithms are based on the analysis of parallel cross-sections. The proposed system is also capable of identification of additional geometric features typical for 2.5 axes milling such as pockets, islands and outer walls. The proposed algorithms are optimized to increase efficiency of the process. Initial identification parameters are selected with respect to defined criteria, e.g., identification accuracy, computing power and scanning accuracy. Described algorithms can be implemented in reverse engineering systems.
The article presents the results of measurements of the geometric structure of the surface after micro end-milling. In the experiments, coated monolithic super micrograin cemented carbide micro end mills with 2 flutes were employed. The machined parts were additively made of CoCr alloy using Selective Laser Melting technology (SLM). Analyzed variables were the volumetric density of energy supplied by the laser during the SLM process and the feed rate during micro-milling. The results showed a strong influence of SLM process parameters on the surface roughness, which, according to the authors, results from the significant variability of the mechanical properties of the material as a function of the volumetric density of energy supplied during melting.
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