Comprehensive molten pool condition-process relations modeling using CNN for wire-feed laser additive manufacturing

[1]  F. Jiang,et al.  Investigation on the process window with liner energy density for single-layer parts fabricated by wire and arc additive manufacturing , 2020 .

[2]  Jau-Woei Perng,et al.  Laser Cladding Quality Monitoring Using Coaxial Image Based on Machine Learning , 2020, IEEE Transactions on Instrumentation and Measurement.

[3]  Qiyue Wang,et al.  A tutorial on deep learning-based data analytics in manufacturing through a welding case study , 2020 .

[4]  B. Kappes,et al.  Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration , 2021, Applied Materials Today.

[5]  Kangil Kim,et al.  A Convolutional Neural Network for Prediction of Laser Power Using Melt-Pool Images in Laser Powder Bed Fusion , 2020, IEEE Access.

[6]  Milton Pereira,et al.  A convolutional neural network approach on bead geometry estimation for a laser cladding system , 2020 .

[7]  Alvaro Rodriguez-Tajes,et al.  A convolutional approach to quality monitoring for laser manufacturing , 2019, J. Intell. Manuf..

[8]  Mayorkinos Papaelias,et al.  Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning , 2019, NDT & E International.

[9]  Yung C. Shin,et al.  In-Process monitoring of porosity during laser additive manufacturing process , 2019, Additive Manufacturing.

[10]  Bintao Wu,et al.  A review of the wire arc additive manufacturing of metals: properties, defects and quality improvement , 2018, Journal of Manufacturing Processes.

[11]  Zhigang Liu,et al.  Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network , 2018, IEEE Transactions on Instrumentation and Measurement.

[12]  Qingbo He,et al.  Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis , 2017, IEEE Transactions on Instrumentation and Measurement.

[13]  Dazhong Ma,et al.  Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[14]  Lorenzo Rosasco,et al.  Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review , 2016, International Journal of Automation and Computing.

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

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

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

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

[19]  Zengxi Pan,et al.  Wire-feed additive manufacturing of metal components: technologies, developments and future interests , 2015 .

[20]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[21]  G. Tapia,et al.  A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing , 2014 .

[22]  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 .

[23]  William E. Frazier,et al.  Metal Additive Manufacturing: A Review , 2014, Journal of Materials Engineering and Performance.

[24]  Todd Palmer,et al.  Heat transfer and fluid flow in additive manufacturing , 2013 .

[25]  Jun Xiong,et al.  Vision-sensing and bead width control of a single-bead multi-layer part: material and energy savings in GMAW-based rapid manufacturing , 2013 .

[26]  Po-Wen Cheng,et al.  Comparison of feedforward and model predictive control of wind turbines using LIDAR , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[27]  Bengt Lennartson,et al.  Height control of laser metal-wire deposition based on iterative learning control and 3D scanning , 2012 .

[28]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[29]  Morten Bisgaard,et al.  Nonlinear feedforward control for wind disturbance rejection on autonomous helicopter , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Bengt Lennartson,et al.  Increased stability in laser metal wire deposition through feedback from optical measurements , 2010 .

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

[32]  Morten Bisgaard,et al.  Feedforward Control of an Autonomous Helicopter using Trim Inputs , 2009 .

[33]  Amir Khajepour,et al.  Geometry Control of the Deposited Layer in a Nonplanar Laser Cladding Process Using a Variable Structure Controller , 2008 .

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

[35]  Alireza Fathi,et al.  Clad height control in laser solid freeform fabrication using a feedforward PID controller , 2007 .

[36]  Reinhart Poprawe,et al.  Development and qualification of a novel laser-cladding head with integrated sensors , 2007 .

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

[38]  Reinhart Poprawe,et al.  Identification and qualification of temperature signal for monitoring and control in laser cladding , 2006 .

[39]  Liang Wang,et al.  Analysis of thermal phenomena in LENS™ deposition , 2006 .

[40]  D. F. de Lange,et al.  Camera based feedback control of the laser cladding process , 2006 .

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

[42]  Martin T. Hagan,et al.  An introduction to the use of neural networks in control systems , 2002 .

[43]  O. Sørensen,et al.  Additive Feed Forward Control with Neural Networks , 1999 .

[44]  W M Steen,et al.  Optical sensor to monitor and control temperature and build height of the laser direct-casting process. , 1998, Applied optics.

[45]  Ping Ge,et al.  Tracking control of a piezoceramic actuator , 1996, IEEE Trans. Control. Syst. Technol..

[46]  Snehasis Mukhopadhyay,et al.  Adaptive control using neural networks and approximate models , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[47]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[48]  Lianfa Bai,et al.  Collaborative and Quantitative Prediction for Reinforcement and Penetration Depth of Weld Bead Based on Molten Pool Image and Deep Residual Network , 2020, IEEE Access.

[49]  Huosheng Hu,et al.  RGB-DI Images and Full Convolution Neural Network-Based Outdoor Scene Understanding for Mobile Robots , 2019, IEEE Transactions on Instrumentation and Measurement.

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

[51]  Jack Beuth,et al.  Transient Changes in Melt Pool Size in Laser Additive Manufacturing Processes , 2004 .

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

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

[54]  Fritz Klocke,et al.  Process monitoring in laser surface treatment operations with reflection and temperature measurement , 1997 .