Investigating Process Parameters and Microhardness Predictive Modeling Approaches for Single Bead 420 Stainless Steel Laser Cladding 2017-01-0283
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. There are many interlinked process parameters associated with laser cladding, which may have an impact on the resultant microhardness profile throughout the bead zone. A set of single bead laser cladding experiments were done using a 4 kW fiber laser coupled with a 6-axis robotic arm for 420 martensitic stainless steel powder. 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 set of process parameters and microhardness requirements. In this study, statistical correlations are made between the process parameters and the microhardness using the analysis of variance (ANOVA), multiple regressions analysis, 3D surface mapping regression, contour plot regression equipped with statistical software SPSS and Minitab. Selected microhardness experimental data are used to run multiple regressions to determine the optimum predictive microhardness model for a wide range of settings. The regression model shows that the predicted microhardness results are in good agreement with the experimental values of within the acceptable range of cladding parameters. The individual effects for each process parameter were determined quantitatively and qualitatively. This research will provide a platform for a laser cladding process parameter selection and optimization.
Citation: Alam, M., Nazemi, N., Urbanic, R., Saqib, S. et al., "Investigating Process Parameters and Microhardness Predictive Modeling Approaches for Single Bead 420 Stainless Steel Laser Cladding," SAE Technical Paper 2017-01-0283, 2017, https://doi.org/10.4271/2017-01-0283. Download Citation
Mohammad K. Alam, Navid Nazemi, Ruth Jill Urbanic, Syed Saqib, Afsaneh Edrisy
University of Windsor, CAMufacturing Solutions Inc.
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