In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes

ABSTRACT One major challenge of implementing Directed Energy Deposition (DED) Additive Manufacturing (AM) for production is the lack of understanding of its underlying process–structure–property relationship. Parts manufactured using the DED technologies may be too inconsistent and unreliable to meet the stringent requirements for many industrial applications. The objective of this research is to characterize the underlying thermo-physical dynamics of the DED process, captured by melt pool signals, and predict porosity during the build. Herein we propose a novel porosity prediction method based on the temperature distribution of the top surface of the melt pool as an AM part is being built. Self-Organizing Maps (SOMs) are then used to further analyze the two-dimensional melt pool image streams to identify similar and dissimilar melt pools. X-ray tomography is used to experimentally locate porosity within the Ti-6Al-4V thin-wall specimen, which is then compared with predicted porosity locations based on the melt pool analysis. Results show that the proposed method based on the temperature distribution of the melt pool is able to predict the location of porosity almost 96% of the time when the appropriate SOM model using a thermal profile is selected. Results are also compared with a previous study, that focuses only on the shape and size of the melt pool. We find that the incorporation of thermal distribution significantly improves the accuracy of porosity prediction. The significance of the proposed methodology based on the melt pool profiles is that this can lead the way toward in situ monitoring and minimize or even eliminate pores within the AM parts.

[1]  V. K. Giri,et al.  Analysis and Interpretation of Bearing Vibration Data Using Principal Component Analysis and Self - Organizing Map , 2016 .

[2]  Jack Beuth,et al.  Synchrotron-Based X-ray Microtomography Characterization of the Effect of Processing Variables on Porosity Formation in Laser Power-Bed Additive Manufacturing of Ti-6Al-4V , 2017 .

[3]  Mohammad Marufuzzaman,et al.  Botnet detection using graph-based feature clustering , 2017, Journal of Big Data.

[4]  R. Kovacevic,et al.  Modelling and measuring the thermal behaviour of the molten pool in closed-loop controlled laser-based additive manufacturing , 2003 .

[5]  Online Melt Pool Temperature Control for Laser Metal Deposition Processes , 2009 .

[6]  D. Sandwell.  BIHARMONIC SPLINE INTERPOLATION OF GEOS-3 AND SEASAT ALTIMETER DATA , 1987 .

[7]  T. Jayakumar,et al.  Intelligent modeling for estimating weld bead width and depth of penetration from infra-red thermal images of the weld pool , 2015, J. Intell. Manuf..

[8]  C. Doumanidis,et al.  Geometry Modeling and Control by Infrared and Laser Sensing in Thermal Manufacturing with Material Deposition , 2001 .

[9]  Nima Shamsaei,et al.  Data indicating temperature response of Ti–6Al–4V thin-walled structure during its additive manufacture via Laser Engineered Net Shaping , 2016, Data in brief.

[10]  Bernd Fritzke.  Growing Grid — a self-organizing network with constant neighborhood range and adaptation strength , 1995, Neural Processing Letters.

[11]  Farshad Tajeripour,et al.  Porosity detection by using improved local binary patterns , 2012 .

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

[13]  F. Walther,et al.  Computed tomography for characterization of fatigue performance of selective laser melted parts , 2015 .

[14]  S. K. Moon,et al.  Characteristic length of the solidified melt pool in selective laser melting process , 2017 .

[15]  A. Rubenchik,et al.  Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones , 2015, 1512.02593.

[16]  Joost Duflou,et al.  On-line monitoring and process control in selective laser melting and laser cutting , 2007 .

[17]  E. Reutzel,et al.  Thermo-mechanical model development and validation of directed energy deposition additive manufacturing of Ti–6Al–4V , 2015 .

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

[19]  S. L. Semiatin,et al.  Microstructure and texture evolution during solidification processing of Ti–6Al–4V , 2003 .

[20]  Zoubeir Lafhaj,et al.  Relationship between ultrasonic Rayleigh wave propagation and capillary porosity in cement paste with variable water content , 2013 .

[21]  Vincent Garnier,et al.  Acoustic techniques for concrete evaluation: Improvements, comparisons and consistency , 2013 .

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

[23]  Jack Beuth,et al.  Process Scaling and Transient Melt Pool Size Control in Laser-Based Additive Manufacturing Processes 328 , 2003 .

[24]  Brian Stephen Wong,et al.  Measurement and characterization of porosity in aluminium selective laser melting parts using X-ray CT , 2015 .

[25]  S. L. Semiatin,et al.  The laser additive manufacture of Ti-6Al-4V , 2001 .

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

[27]  J. Hunt,et al.  Steady state columnar and equiaxed growth of dendrites and eutectic , 1984 .

[28]  Yun Peng,et al.  Melt pool shape and dilution of laser cladding with wire feeding , 2000 .

[29]  Jack Beuth,et al.  Prediction of lack-of-fusion porosity for powder bed fusion , 2017 .

[30]  Xu Han,et al.  Melt-pool motion, temperature variation and dendritic morphology of Inconel 718 during pulsed- and continuous-wave laser additive manufacturing: A comparative study , 2017 .

[31]  Dave Hale.  Image-guided blended neighbor interpolation , 2009 .

[32]  Frank W. Liou,et al.  Control of Laser Cladding for Rapid Prototyping-A Review , 2001 .

[33]  N. Shamsaei,et al.  An overview of Direct Laser Deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and control , 2015 .

[34]  Erzsébet Merényi.  Advances in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, Houston, Texas, USA, January 6-8, 2016 , 2016, WSOM.

[35]  Carosena Meola,et al.  Flash Thermography to Evaluate Porosity in Carbon Fiber Reinforced Polymer (CFRPs) , 2014, Materials.

[36]  Jean-Pierre Kruth,et al.  In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system , 2014 .

[37]  Kuang-Chao Fan,et al.  Development of auto defect classification system on porosity powder metallurgy products , 2010 .

[38]  Jack L. Beuth,et al.  CONTROLLING MELT POOL DIMENSIONS OVER A WIDE RANGE OF MATERIAL DEPOSITION RATES IN ELECTRON BEAM ADDITIVE MANUFACTURING , 2010 .

[39]  Zoubeir Lafhaj,et al.  Assessment of porosity of mortar using ultrasonic Rayleigh waves , 2009 .

[40]  K. Osakada,et al.  Finite element analysis of melting and solidifying processes in laser rapid prototyping of metallic powders , 1999 .

[41]  Karen M. Taminger,et al.  Integrated control of solidification microstructure and melt pool dimensions in electron beam wire feed additive manufacturing of Ti-6Al-4V , 2014 .

[42]  E. W. Reutzel,et al.  Sensing defects during directed-energy additive manufacturing of metal parts using optical emissions spectroscopy , 2015 .

[43]  A. Nassar,et al.  Physics-Based Multivariable Modeling and Feedback Linearization Control of Melt-Pool Geometry and Temperature in Directed Energy Deposition , 2017 .

[44]  Abbas Keramati,et al.  Webpage Clustering - Taking the Zero Step: a Case Study of an Iranian Website , 2014, J. Web Eng..

[45]  Huan Qi,et al.  Numerical simulation of heat transfer and fluid flow in coaxial laser cladding process for direct metal deposition , 2006 .

[46]  Ryan B. Wicker,et al.  Analysis and correction of defects within parts fabricated using powder bed fusion technology , 2015 .

[47]  Huang Weidong,et al.  Research on molten pool temperature in the process of laser rapid forming , 2008 .

[48]  Robert Schmitt,et al.  Industrial applications of computed tomography , 2014 .

[49]  Robert F. Singer,et al.  In situ flaw detection by IR‐imaging during electron beam melting , 2012 .

[50]  Adam Woźniak,et al.  Study of porosity measurement using the computer tomograph , 2012 .

[51]  Jiang Hsieh,et al.  Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Fourth Edition , 2022 .

[52]  N. Shamsaei,et al.  An overview of Direct Laser Deposition for additive manufacturing; Part I: Transport phenomena, modeling and diagnostics , 2015 .

[53]  Lin Li,et al.  Modelling the geometry of a moving laser melt pool and deposition track via energy and mass balances , 2004 .

[54]  Zoubeir Lafhaj,et al.  Correlation between porosity, permeability and ultrasonic parameters of mortar with variable water / cement ratio and water content , 2006 .

[55]  Igor Solodov,et al.  Characterization of porosity and defect imaging in ceramic tile using ultrasonic inspections , 2012 .

[56]  Olivier Durand,et al.  Non-contact, automated surface wave measurements for the mechanical characterisation of concrete , 2012 .

[57]  Thierry Engel,et al.  Laser cladding: the relevant parameters for process control , 1994, Other Conferences.

[58]  C. Gáspár.  Multigrid technique for biharmonic interpolation with application to dual and multiple reciprocity method , 2004, Numerical Algorithms.

[59]  Robert Schmitt,et al.  Computed tomography for dimensional metrology , 2011 .

[60]  A. Hoadley,et al.  Finite element simulation of laser surface treatments including convection in the melt pool , 1994 .

[61]  Terry Wohlers,et al.  Wohlers report 2016 , 2016 .

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

[63]  B. Stucker,et al.  Influence of processing parameters on the evolution of melt pool, porosity, and microstructures in Ti-6Al-4V alloy parts fabricated by selective laser melting , 2017, Progress in Additive Manufacturing.

[64]  John J. Lewandowski,et al.  Overview of Materials Qualification Needs for Metal Additive Manufacturing , 2016 .

[65]  Dian Pratiwi,et al.  The Use of Self Organizing Map Method and Feature Selection in Image Database Classification System , 2012, ArXiv.