Uncertainty quantification and reduction in metal additive manufacturing

Uncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in the practical AM process requires overcoming two main challenges: (1) the inaccurate knowledge of uncertainty sources and (2) the intrinsic uncertainty associated with the computational model. Here, we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology (NIST). We first construct a surrogate model, based on high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model. The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, representing a significant advance toward physics-based quality control of AM products.

[1]  H L Wei,et al.  Evolution of solidification texture during additive manufacturing , 2015, Scientific Reports.

[2]  H. Yeung,et al.  A meltpool prediction based scan strategy for powder bed fusion additive manufacturing , 2020, Additive Manufacturing.

[3]  Sankaran Mahadevan,et al.  Uncertainty quantification in prediction of material properties during additive manufacturing , 2017 .

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

[5]  S. S. Al-Bermani,et al.  The Origin of Microstructural Diversity, Texture, and Mechanical Properties in Electron Beam Melted Ti-6Al-4V , 2010 .

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

[7]  Philip B. Prangnell,et al.  Effect of build geometry on the β-grain structure and texture in additive manufacture of Ti6Al4V by selective electron beam melting , 2013 .

[8]  Mohammed Al Kindi,et al.  Influence of Laser Processing Strategy and Remelting on Surface Structure and Porosity Development during Selective Laser Melting of a Metallic Material , 2019, Metallurgical and Materials Transactions A.

[9]  Sankaran Mahadevan,et al.  Uncertainty aggregation and reduction in structure–material performance prediction , 2018 .

[10]  Oleksandr Semeniuta,et al.  Optimization of Process Parameters for Powder Bed Fusion Additive Manufacturing by Combination of Machine Learning and Finite Element Method: A Conceptual Framework , 2018 .

[11]  Rianne E. Laureijs,et al.  Metal Additive Manufacturing: Cost Competitive Beyond Low Volumes , 2016 .

[12]  Li Ma,et al.  Single-Track Melt-Pool Measurements and Microstructures in Inconel 625 , 2018, 1802.05827.

[13]  Adam Trendowicz,et al.  Model Development and Validation , 2013 .

[14]  Oksana A. Chkrebtii,et al.  Uncertainty Quantification Accounting for Model Discrepancy Within a Random Effects Bayesian Framework , 2020, Integrating Materials and Manufacturing Innovation.

[15]  Paul Witherell,et al.  A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations. , 2019, Journal of manufacturing science and engineering.

[16]  Zhimin Xi,et al.  Calibration and Validation Framework for Selective Laser Melting Process Based on Multi-Fidelity Models and Limited Experiment Data , 2020 .

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

[18]  Paul Witherell,et al.  Identifying uncertainty in laser powder bed fusion additive manufacturing models. , 2016, Journal of mechanical design.

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

[20]  Sudeepta Mondal,et al.  Investigation of Melt Pool Geometry Control in Additive Manufacturing Using Hybrid Modeling , 2020, Metals.

[21]  Paul D. Arendt,et al.  Quantification of model uncertainty: Calibration, model discrepancy, and identifiability , 2012 .

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

[23]  Alaa Elwany,et al.  Uncertainty Propagation Analysis of Computational Models in Laser Powder Bed Fusion Additive Manufacturing Using Polynomial Chaos Expansions , 2018, Journal of Manufacturing Science and Engineering.

[24]  Sung-Hyuk Cha.  Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .

[25]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[26]  Shawn P. Moylan,et al.  Measurement Science Needs for Real-time Control of Additive Manufacturing Powder Bed Fusion Processes , 2015 .

[27]  Jack Beuth,et al.  Integrated melt pool and microstructure control for Ti–6Al–4V thin wall additive manufacturing , 2015 .

[28]  Brian Williams,et al.  A Bayesian calibration approach to the thermal problem , 2008 .

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

[30]  K. Mumtaz,et al.  Melting of thin wall parts using pulse shaping , 2009 .

[31]  Arlindo Silva,et al.  Design of a Prototype for the In Situ Forming of a Liquid-Infused Preform Process , 2019, Journal of Mechanical Design.

[32]  Chandrika Kamath,et al.  Data mining and statistical inference in selective laser melting , 2016, The International Journal of Advanced Manufacturing Technology.

[33]  Farrokh Mistree,et al.  A procedure for robust design: Minimizing variations caused by noise factors and control factors , 1996 .

[34]  Stan A David,et al.  Physical processes in fusion welding , 1995 .

[35]  Jon C. Helton,et al.  Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems , 2002 .

[36]  Alkan Donmez,et al.  Interlaboratory Study for Nickel Alloy 625 Made by Laser Powder Bed Fusion to Quantify Mechanical Property Variability , 2016, Journal of Materials Engineering and Performance.

[37]  Liang Hou,et al.  Additive manufacturing and its societal impact: a literature review , 2013 .

[38]  J. Gagné.  Literature Review , 2018, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[39]  Bo Cheng,et al.  On Process Temperature in Powder-Bed Electron Beam Additive Manufacturing: Model Development and Validation , 2014 .

[40]  Jack Beuth,et al.  Location specific solidification microstructure control in electron beam melting of Ti-6Al-4V , 2018 .

[41]  Li Ma,et al.  Application of Finite Element, Phase-field, and CALPHAD-based Methods to Additive Manufacturing of Ni-based Superalloys. , 2017, Acta materialia.

[42]  K. Schmidt.  Conceptual Framework for , 2002 .

[43]  William E. Luecke,et al.  Interlaboratory mechanical property study for Cobalt-Chromium alloy Made by Laser Powder-Bed-Fusion Additive Manufacturing , 2018 .

[44]  Zhen Hu,et al.  A sequential calibration and validation framework for model uncertainty quantification and reduction , 2020 .

[45]  Olga Wodo,et al.  Data-driven modeling of thermal history in additive manufacturing , 2020, Additive Manufacturing.

[46]  Wei Chen,et al.  Quality utility : a Compromise Programming approach to robust design , 1999 .