In-process measurement of melt pool cross-sectional geometry and grain orientation in a laser directed energy deposition additive manufacturing process

Abstract Understanding the behaviour of melt pool during laser directed energy deposition (L-DED) is essential for the prediction and control of process quality. Previous effort was focused on the observation of melt pool surface characteristics. In this paper, a coaxial imaging system was employed to determine the melt pool cross sectional geometry and to predict solidified grain orientation during a high deposition rate L-DED process. The image processing procedure, deposition track cross-sectional profile prediction and the relationship between melt pool shape and melt pool dynamics, and grain growth orientation were investigated. Results show that sharp melt pool edges can be obtained so that melt pool width can be predicted with an accuracy of more than 95%. The estimation method of melt pool length has an accuracy of 90%. With the experimental melt pool width and depth data, the cross-sectional profiles of deposited track are predicted at an accuracy of 92% and a good match with experimental data is obtained. The melt pool formation is found to be able to allow the prediction of crystal growth directions during solidification.

[1]  Prasanna Rangarajan,et al.  Hyper least squares fitting of circles and ellipses , 2011, Comput. Stat. Data Anal..

[2]  Reinhart Poprawe,et al.  Improvement of material performance of Inconel 718 formed by high deposition-rate laser metal deposition , 2016 .

[3]  Li Meng,et al.  Microstructure and mechanical properties of the coatings in laser cladding and laser-induction hybrid cladding of full-scale rail , 2018, SPIE/COS Photonics Asia.

[4]  Reinhart Poprawe,et al.  Investigations on ultra-high-speed laser material deposition as alternative for hard chrome plating and thermal spraying , 2016 .

[5]  R. Poprawe,et al.  Characterization of the process control for the direct laser metallic powder deposition , 2006 .

[6]  Nobuyuki Abe,et al.  Dynamic observation of formation process in laser cladding using high speed video camera , 2013 .

[7]  David Travis,et al.  Effective Color Displays: Theory and Practice , 1991 .

[8]  Malcolm Bibby,et al.  Modelling Gas Metal Arc Weld Geometry Using Artificial Neural Network Technology , 1999 .

[9]  R. Poprawe,et al.  Experimental study of effects of main process parameters on porosity, track geometry, deposition rate, and powder efficiency for high deposition rate laser metal deposition , 2015 .

[10]  Konrad Wegener,et al.  Analysis of melt pool dynamics in laser cladding and direct metal deposition by automated high-speed camera image evaluation , 2018 .

[11]  Frank W. Liou,et al.  Empirical Modeling and Vision Based Control for Laser Aided Metal Deposition Process , 2001 .

[12]  Jean-Yves Hascoët,et al.  Prediction and analytical description of the single laser track geometry in direct laser fabrication from process parameters and energy balance reasoning , 2012 .

[13]  M. L. Griffith,et al.  Understanding thermal behavior in the LENS process , 1999 .

[14]  Xin Min Zhang,et al.  Influences of Processing Parameters on Dilution Ratio of Laser Cladding Layer during Laser Metal Deposition Shaping , 2012 .

[15]  Ronald Guillén,et al.  Analysis and prediction of single laser tracks geometrical characteristics in coaxial laser cladding process , 2012 .

[16]  Yaoyu Ding,et al.  Development of sensing and control system for robotized laser-based direct metal addition system , 2016 .

[17]  Lijun Song,et al.  Phase congruency melt pool edge extraction for laser additive manufacturing , 2017 .

[18]  Lijun Song,et al.  Feedback Control of Melt Pool Temperature During Laser Cladding Process , 2011, IEEE Transactions on Control Systems Technology.

[19]  Porosity Detection of Laser Based Additive Manufacturing Using Melt Pool Morphology Clustering , 2016 .

[20]  Wanhua Zhao,et al.  Investigation of the overlapping parameters of MPAW‐based rapid prototyping , 2006 .

[21]  Lijun Song,et al.  Control of melt pool temperature and deposition height during direct metal deposition process , 2012 .

[22]  A. Gasser,et al.  Influence of solution heat treatment on microstructure and tensile properties of Inconel 718 formed by high-deposition-rate laser metal deposition , 2018 .

[23]  Lin Wu,et al.  Modeling of bead section profile and overlapping beads with experimental validation for robotic GMAW-based rapid manufacturing , 2013 .

[24]  Radovan Kovacevic,et al.  Optical monitoring of high power direct diode laser cladding , 2014 .

[25]  Ingomar Kelbassa,et al.  Correlations of Melt Pool Geometry and Process Parameters During Laser Metal Deposition by Coaxial Process Monitoring , 2014 .

[26]  Mohammad Marufuzzaman,et al.  In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes , 2019, IISE Trans..

[27]  Ehsan Toyserkani,et al.  An image-based feature tracking algorithm for real-time measurement of clad height , 2007, Machine Vision and Applications.

[28]  Alain Bernard,et al.  Weld bead modeling and process optimization in Hybrid Layered Manufacturing , 2011, Comput. Aided Des..

[29]  Radovan Kovacevic,et al.  Sensing, modeling and control for laser-based additive manufacturing , 2003 .

[30]  L. Tang,et al.  Melt Pool Temperature Control for Laser Metal Deposition Processes—Part I: Online Temperature Control , 2010 .

[31]  V. Ocelík,et al.  On the geometry of coating layers formed by overlap , 2014 .

[32]  Anna Valente,et al.  Vision-based melt pool monitoring system setup for additive manufacturing , 2019, Procedia CIRP.

[33]  P. Hooper.  Melt pool temperature and cooling rates in laser powder bed fusion , 2018, Additive Manufacturing.

[34]  James G. Harris,et al.  The influence of stellite 6 particle size on the inter-track porosity in multi-track cladding , 2006 .

[35]  Sheng Zhu,et al.  Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic MAG welding process , 2011 .

[36]  Julie M. Schoenung,et al.  Observations of particle-melt pool impact events in directed energy deposition , 2018, Additive Manufacturing.

[37]  M. Brandt,et al.  Melt pool temperature control using LabVIEW in Nd:YAG laser blown powder cladding process , 2006 .

[38]  Ashish Kumar Nath,et al.  Online monitoring of thermo-cycles and its correlation with microstructure in laser cladding of nickel based super alloy , 2017 .

[39]  Konstantinos Salonitis,et al.  An analytical model of the laser clad geometry , 2007 .

[40]  Shengfeng Zhou,et al.  Laser induction hybrid rapid cladding of WC particles reinforced NiCrBSi composite coatings , 2010 .

[41]  Reinhart Poprawe,et al.  Investigation on the direct laser metallic powder deposition process via temperature measurement , 2006 .

[42]  A. Nassar,et al.  Intra-layer closed-loop control of build plan during directed energy additive manufacturing of Ti–6Al–4V , 2015 .

[43]  Reinhart Poprawe,et al.  The Influence of the Powder Stream on High-Deposition-Rate Laser Metal Deposition with Inconel 718 , 2017 .

[44]  Yongjun Huang,et al.  Analysis of laser-induction hybrid cladding processing conditions , 2007, International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT).