The objective of this work is to propose an advanced automated damage detection and damage reconstruction algorithm for damaged gear tooth repair. It can automate tool path design and provide precise repair volume detection for complex repair volume. First, models of the damaged and nominal parts were obtained by reverse engineering. Next, the damaged model was aligned with the nominal model. After that, both models were sliced into layers, and a set of parallel and equidistant casting rays was used to intersect with these layers to extract the repair volume. Then the repair tool path was generated and used to guide the laser additive manufacturing process for repair. The corresponding repair experiment and validated numerical model based on repairing a complex gear fracture was conducted to evaluate the reconstruction algorithm efficiency and repair part quality. Microstructure analysis and Vickers hardness test were carried out to evaluate the repaired part quality. The coincidence of scanning points between repaired model and the nominal model is high. The repair experiment confirmed the strong efficiency of this repair algorithm for complex geometry repair. A 3D finite element model was also developed to simulate the repair process and provide critical deformation and residual stress of the repaired parts. The predicted temperature and residual stress results were compared and showed a good agreement with the experimental measurements. These results further validated that the proposed repair algorithm and simulation model are suitable and efficient for the automated repair of damaged components.
L. Li et al., "Component Repair using Additive Manufacturing: Experiments and Thermal Modeling," International Journal of Advanced Manufacturing Technology, vol. 119, no. 1 thru 2, pp. 719 - 732, Springer, Mar 2022.
The definitive version is available at https://doi.org/10.1007/s00170-021-08265-y
Mechanical and Aerospace Engineering
Keywords and Phrases
Damage detection and reconstruction; Deformation and stress; Direct metal deposition; Microstructure analysis; Repair
International Standard Serial Number (ISSN)
Article - Journal
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01 Mar 2022
National Science Foundation, Grant CMMI 1625736