Prediction of melt pool temperature in directed energy deposition using machine learning
暂无分享,去创建一个
[1] Zhenghui Sha,et al. Data-Driven Predictive Modeling of Tensile Behavior of Parts Fabricated by Cooperative 3D Printing , 2020, J. Comput. Inf. Sci. Eng..
[2] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[3] Yong Yu,et al. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures , 2019, Neural Computation.
[4] Qiuhong Jiang,et al. Influence of energy density on macro/micro structures and mechanical properties of as-deposited Inconel 718 parts fabricated by laser engineered net shaping , 2019, Journal of Manufacturing Processes.
[5] Mohammad Marufuzzaman,et al. In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes , 2019, IISE Trans..
[6] Tao Li,et al. Effects of deposition variables on molten pool temperature during laser engineered net shaping of Inconel 718 superalloy , 2019, The International Journal of Advanced Manufacturing Technology.
[7] Christoph Leyens,et al. Analysis of Melt Pool Characteristics and Process Parameters Using a Coaxial Monitoring System during Directed Energy Deposition in Additive Manufacturing , 2019, Materials.
[8] Kornel Ehmann,et al. Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks , 2018, Manufacturing Letters.
[9] P. Hooper. Melt pool temperature and cooling rates in laser powder bed fusion , 2018, Additive Manufacturing.
[10] Michael Veilleux,et al. A thermal-mechanical finite element workflow for directed energy deposition additive manufacturing process modeling , 2018 .
[11] Zhibo Luo,et al. A survey of finite element analysis of temperature and thermal stress fields in powder bed fusion Additive Manufacturing , 2018 .
[12] M. Marufuzzaman,et al. Porosity prediction: Supervised-learning of thermal history for direct laser deposition , 2018 .
[13] Pascal Laheurte,et al. Functionally graded Ti6Al4V-Mo alloy manufactured with DED-CLAD® process , 2017 .
[14] Jack Beuth,et al. Prediction of lack-of-fusion porosity for powder bed fusion , 2017 .
[15] D. Przestacki,et al. Determination of emissivity coefficient of heat-resistant super alloys and cemented carbide , 2016 .
[16] Seyfolah Saedodin,et al. Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy , 2016 .
[17] Zemin Wang,et al. Role of molten pool mode on formability, microstructure and mechanical properties of selective laser melted Ti-6Al-4V alloy , 2016 .
[18] Kornel Ehmann,et al. Anisotropic properties of directed energy deposition (DED)-processed Ti–6Al–4V , 2016 .
[19] Hong-Chao Zhang,et al. Environmental benefits of remanufacturing: A case study of cylinder heads remanufactured through laser cladding , 2016 .
[20] Karen M. Taminger,et al. A coupled finite element cellular automaton model to predict thermal history and grain morphology of Ti-6Al-4V during direct metal deposition (DMD) , 2016 .
[21] A. Khajepour,et al. Effect of real-time cooling rate on microstructure in Laser Additive Manufacturing , 2016 .
[22] Klaus-Dieter Thoben,et al. Machine learning in manufacturing: advantages, challenges, and applications , 2016 .
[23] N. Shamsaei,et al. An overview of Direct Laser Deposition for additive manufacturing; Part II: Mechanical behavior, process parameter optimization and control , 2015 .
[24] Jürgen Schmidhuber,et al. LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[25] G. Tapia,et al. A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing , 2014 .
[26] 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 .
[27] Donald B. Hondongwa,et al. A Review of the Pinned Photodiode for CCD and CMOS Image Sensors , 2014, IEEE Journal of the Electron Devices Society.
[28] Darin Thomas,et al. Additive Manufacturing for the Aerospace Industry , 2012 .
[29] Jean-Pierre Kruth,et al. Determination of geometrical factors in Layerwise Laser Melting using optical process monitoring , 2011 .
[30] Dichen Li,et al. Numerical simulation of thermal behavior during laser direct metal deposition , 2011 .
[31] L. Tang,et al. Melt Pool Temperature Control for Laser Metal Deposition Processes—Part I: Online Temperature Control , 2010 .
[32] 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 .
[33] J. Schoenung,et al. In situ thermal imaging and three-dimensional finite element modeling of tungsten carbide–cobalt during laser deposition , 2009 .
[34] Andrew J. Pinkerton,et al. Combining wire and coaxial powder feeding in laser direct metal deposition for rapid prototyping , 2006 .
[35] Lin Li,et al. Modelling the geometry of a moving laser melt pool and deposition track via energy and mass balances , 2004 .
[36] C. Doumanidis,et al. Geometry Modeling and Control by Infrared and Laser Sensing in Thermal Manufacturing with Material Deposition , 2001 .
[37] Sepp Hochreiter,et al. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[38] S. Hochreiter,et al. Long Short-Term Memory , 1997, Neural Computation.
[39] J. Mazumder,et al. Heat transfer model for cw laser material processing , 1980 .
[40] P. Klemens. Heat balance and flow conditions for electron beam and laser welding , 1976 .
[41] E. Reutzel,et al. Thermo-mechanical model development and validation of directed energy deposition additive manufacturing of Ti–6Al–4V , 2015 .
[42] A. Pinkerton. Laser direct metal deposition: theory and applications in manufacturing and maintenance , 2010 .