Knowledge-based optimization of cold spray for aircraft component repair

In recent years, cold spraying (CS) has emerged as a promising technology for repair applications, particularly for oxidation-sensitive materials. In order to obtain an optimum repair result that fulfills the highest requirements regarding material properties, simple geometric shape restoration is not sufficient. Any additive manufacturing process results in particular features in microstructure, possible defects and respective – potentially even anisotropic – mechanical properties. To systematically tailor these microstructures and properties to the specific component and geometry requires complex routines. This work proposes the design of a knowledge-based cold spray repair system that facilitates a complete individual repair procedure for aircraft components. This system includes the elements of (i) reverse engineering to analyze, classify and generate digital data of the damaged component, (ii) pre-processing to obtain the ideal conditions for the CS process, (iii) toolpath planning to optimize robotics for the CS process, (iv) on-line monitoring to ensure process quality, (v) post-processing and (vi) performance testing of the material properties to meet the challenging requirements of the aerospace industry. By using an industrial robot and computer-aided planning of the trajectories, components are to be repaired under cold spray and geometrical conditions for ideal material deposition. The goal is to obtain repaired components that fulfill the required property profile equally well as respective new parts.

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