Investigating Geometry-Hardness Predictive Modelling Approaches for Laser Cladding Single Beads using 420 Stainless Steel

Paper #:
  • 2017-01-0283

Published:
  • 2017-03-28
Abstract:
Laser cladding is a novel process of surface coating, and researchers in both academia and industry are developing additive manufacturing solutions for large, metallic components using this process. There are many interlinked process parameters (e.g. laser power, laser speed and powder feed rate) associated with laser cladding. These process parameters have a direct impact on the resultant bead geometry and the microhardness profile throughout the bead zone, dilution zone and heat affected zone (HAZ). A set of single bead laser cladding experiments were done using a 4 kW fibre laser coupled with a 6-axis robotic arm for 420 martensitic stainless steel. A design of experiments approach was taken to explore a wide range of process parameter settings. The goal of this research is to determine whether robust predictive models for hardness can be developed, and if there are predictive trends that can be employed to optimize the process settings for a given bead geometry and hardness requirements. In this study, statistical correlations will be made between the process parameters, bead geometry and the microhardness profile using the analysis of variance (ANOVA), 3D meshing, and advanced finite element simulation tools (SYSWELD). Select geometry and hardness experimental data is used to calibrate and validate simulation models to predict hardness values for a wide range of settings. As the validation results show that the simulated microhardness results are in good agreement with the experimental values of within the acceptable range of cladding parameters, both experimental and simulation data is used to explore predictive model solutions. ANOVA models will be developed for the hardness. The bead width, height, penetration geometry and hardness values will be mapped along with the laser powder and travel speed to develop 3D surface maps. This research will provide a platform for a laser cladding process parameter selection and optimization.
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