Calibration and Validation Framework for Selective Laser Melting Process Based on Multi-Fidelity Models and Limited Experiment Data

There are significant quality and reliability problems for components/products made by additive manufacturing (AM) due to various reasons. Selective laser melting (SLM) process is one of the popular AM techniques and it suffers from low quality and reliability issue as well. Among many reasons, the lack of accurate and efficient models to simulate the SLM process could be the most important one because reliability and quality quantification rely on accurate models; otherwise, a large number of experiments should be conducted for reliability and quality assurance. To date, modeling techniques for the SLM process are either computationally expensive based on finite element (FE) modeling or economically expensive requiring a significant amount of experiment data for data-driven modeling. This paper proposes the integration of FE and data-driven modeling with systematic calibration and validation framework for the SLM process based on limited experiment data. Multi-fidelity models are the FE model for the SLM process and a machine learning model constructed based on the FE model instead of real experiment data. The machine learning model, after incorporation of the learned physics from the FE model, is then further improved based on limited real experiment data through the calibration and validation framework. The proposed work enables the development of highly efficient and accurate models for melt pool prediction of the SLM process under various configurations. The effectiveness of the framework is demonstrated by real experiment data under 14 different printing configurations.

[1]  Guan Zhou,et al.  Optimization of an auxetic jounce bumper based on Gaussian process metamodel and series hybrid GA-SQP algorithm , 2018 .

[2]  James Hensman,et al.  Locating acoustic emission sources in complex structures using Gaussian processes , 2008 .

[3]  Bo Song,et al.  Effects of processing parameters on microstructure and mechanical property of selective laser melted Ti6Al4V , 2012 .

[4]  Liang Gao,et al.  A combined projection-outline-based active learning Kriging and adaptive importance sampling method for hybrid reliability analysis with small failure probabilities , 2019, Computer Methods in Applied Mechanics and Engineering.

[5]  Byeng D. Youn,et al.  Adaptive-sparse polynomial chaos expansion for reliability analysis and design of complex engineering systems , 2011 .

[6]  Frank Walther,et al.  Effects of Defects in Laser Additive Manufactured Ti-6Al-4V on Fatigue Properties , 2014 .

[7]  Ying Xiong,et al.  A better understanding of model updating strategies in validating engineering models , 2009 .

[8]  L. Froyen,et al.  Binding Mechanisms in Selective Laser Sintering and Selective Laser Melting , 2004 .

[9]  Liang Gao,et al.  A novel projection outline based active learning method and its combination with Kriging metamodel for hybrid reliability analysis with random and interval variables , 2018, Computer Methods in Applied Mechanics and Engineering.

[10]  Zhen Hu,et al.  Uncertainty quantification and management in additive manufacturing: current status, needs, and opportunities , 2017, The International Journal of Advanced Manufacturing Technology.

[11]  Brent Stucker,et al.  Influence of Defects on Mechanical Properties of Ti-6Al-4V Components Produced by Selective Laser Melting and Electron Beam Melting , 2015 .

[12]  Boris Wilthan,et al.  Thermophysical Properties of Solid and Liquid Ti-6Al-4V (TA6V) Alloy , 2006 .

[13]  J. Kruth,et al.  Effects of build orientation and heat treatment on the microstructure and mechanical properties of selective laser melted Ti6Al4V lattice structures , 2015 .

[14]  H. Maier,et al.  On the mechanical behaviour of titanium alloy TiAl6V4 manufactured by selective laser melting: Fatigue resistance and crack growth performance , 2013 .

[15]  Ren-Jye Yang,et al.  Time dependent model bias correction for model based reliability analysis , 2017 .

[16]  B. Stucker,et al.  A review of thermal analysis methods in Laser Sintering and Selective Laser Melting , 2012 .

[17]  W. Oberkampf,et al.  Model validation and predictive capability for the thermal challenge problem , 2008 .

[18]  Zhibo Luo,et al.  A survey of finite element analysis of temperature and thermal stress fields in powder bed fusion Additive Manufacturing , 2018 .

[19]  J. Beuth,et al.  Thermal conductivity of metal powders for powder bed additive manufacturing , 2018 .

[20]  Zhimin Xi,et al.  Model-Based Reliability Analysis With Both Model Uncertainty and Parameter Uncertainty , 2019, Journal of Mechanical Design.

[21]  B. Sudret,et al.  An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis , 2010 .

[22]  Jianbo Yu,et al.  State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble , 2018, Reliab. Eng. Syst. Saf..

[23]  John J. Lewandowski,et al.  Melt Pool Characterization for Selective Laser Melting of Ti-6Al-4V Pre-Alloyed Powder , 2014 .

[24]  Zhen Hu,et al.  A Data-Driven Approach for Process Optimization of Metallic Additive Manufacturing Under Uncertainty , 2019, Journal of Manufacturing Science and Engineering.

[25]  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.

[26]  Brent Stucker,et al.  Analysis of defect generation in Ti–6Al–4V parts made using powder bed fusion additive manufacturing processes , 2014 .

[27]  Sankaran Mahadevan,et al.  Uncertainty Quantification in Metallic Additive Manufacturing Through Physics-Informed Data-Driven Modeling , 2019, JOM.

[28]  Ren-Jye Yang,et al.  Metamodeling development for vehicle frontal impact simulation , 2001, DAC 2001.

[29]  D. Mynors,et al.  A three-dimensional finite element analysis of the temperature field during laser melting of metal powders in additive layer manufacturing , 2009 .

[30]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[31]  Byeng D. Youn,et al.  A framework of model validation and virtual product qualification with limited experimental data based on statistical inference , 2015 .

[32]  Alaa Elwany,et al.  Numerical and experimental analysis of heat distribution in the laser powder bed fusion of Ti-6Al-4V , 2018, IISE Trans..

[33]  R. Grandhi,et al.  Polynomial Chaos Expansion with Latin Hypercube Sampling for Estimating Response Variability , 2003 .

[34]  J. Kruth,et al.  A study of the microstructural evolution during selective laser melting of Ti–6Al–4V , 2010 .

[35]  Seung-gyo Jang,et al.  Reliability-based design optimization under sampling uncertainty: shifting design versus shaping uncertainty , 2018 .

[36]  Liang Gao,et al.  A new method for reliability analysis of structures with mixed random and convex variables , 2019, Applied Mathematical Modelling.

[37]  Chao Hu,et al.  A comparative study of probability estimation methods for reliability analysis , 2012 .

[38]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[39]  Chandrika Kamath,et al.  Observation of keyhole-mode laser melting in laser powder-bed fusion additive manufacturing , 2014 .

[40]  Thomas W. Eagar,et al.  Temperature fields produced by traveling distributed heat sources , 1983 .

[41]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[42]  L. Froyen,et al.  Selective laser melting of iron-based powder , 2004 .