Effect of heat accumulation on the single track formation during laser metal deposition and development of a framework for analyzing new process strategies
Laser metal deposition (LMD) is getting more attention as a 3D printing technique recently. Compared to the cladding application, the process of building 3D structures is more challenging to control when it comes to complex structures. Changing heat transfer mechanisms over the building height and at critical part geometries can lead to instabilities during the process. This paper studies the effect of the surface temperature on single track geometry formation during the LMD process. The geometrical characteristic of the track formation is a key factor that needs to be controlled and monitored in any deposition process for a stable build job. To quantify the effect of the surface temperature on the track geometry, a stepwise preheated substrate from 25 to 500 °C is used, and the track height, width, and degree of dilution are evaluated. This experiment mimics the varying temperature of the prebuilt layers that occur during additive manufacturing of freeform structures with many layers. Understanding the influence of the surface temperature over the layer geometry gives an idea of build geometry variation due to the accumulated heat input along the build direction. This helps in defining process strategies and process parameters for the laser metal deposition of components with increased accuracy and reproducibility. Advanced process strategies that are essential for a successful build job need to be incorporated into a data preparation tool for robot code generation. Commercial software packages are available for regular additive path planning using industrial robots. However, when several process strategies and process parameters need to be adapted and varied along the build direction, one is pushed to the limits of the software capabilities. Therefore, an automated data preprocessing toolbox based on MATLAB has been developed for the implementation of process strategies through user-definable rules and algorithms. This eliminates manual robot code preparation and correction. Implementation and the level of automation of this process preparation toolbox have also been discussed in this paper.