Hybridized Artificial Neural Network-Based Expert Systems for Modelling of Robotic- Wire and Arc Additive Manufacturing Process

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  R. Manivannan,et al.  Development of Y-shape hybrid frame model using wire and arc additive manufacturing process , 2021, Materials Today: Proceedings.

[3]  Valdemar R. Duarte,et al.  Current Status and Perspectives on Wire and Arc Additive Manufacturing (WAAM) , 2019, Materials.

[4]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[5]  J. Yoganandh,et al.  Effect of process parameters on clad bead geometry and its shape relationships of stainless steel claddings deposited by GMAW , 2010 .

[6]  Shekhar Srivastava,et al.  Process parameter optimization of gas metal arc welding on IS:2062 mild steel using response surface methodology , 2017 .

[7]  Jeff M. Barrett,et al.  Multi-variable statistical models for predicting bead geometry in gas metal arc welding , 2019, The International Journal of Advanced Manufacturing Technology.

[8]  Ana María Camacho,et al.  Preliminary development of a Wire and Arc Additive Manufacturing system (WAAM) , 2017 .

[9]  Sheng Zhu,et al.  Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic MAG welding process , 2011 .

[10]  R. Urbanic,et al.  Using Predictive Modeling and Classification Methods for Single and Overlapping Bead Laser Cladding to Understand Bead Geometry to Process Parameter Relationships , 2016 .

[11]  Wang Jiaxin,et al.  Weld Bead Geometry Prediction of Additive Manufacturing Based on Neural Network , 2018 .

[12]  Eric Coatanéa,et al.  Graph-Based Metamodeling for Characterizing Cold Metal Transfer Process Performance , 2019 .

[13]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[14]  V. Dhinakaran,et al.  Wire Arc Additive Manufacturing Perspectives and Recent Developments , 2020, IOP Conference Series: Materials Science and Engineering.

[15]  I. Gibson,et al.  Wire and arc additive manufacturing: Opportunities and challenges to control the quality and accuracy of manufactured parts , 2021 .

[16]  Abolfazl Foorginejad,et al.  Modeling of Weld Bead Geometry Using Adaptive Neuro-Fuzzy Inference System (ANFIS) in Additive Manufacturing , 2020 .

[18]  Chee Kai Chua,et al.  3D Printing and Additive Manufacturing: Principles and Applications (with Companion Media Pack) - Fourth Edition of Rapid Prototyping , 2014 .

[19]  H. Henein,et al.  Concurrent geometry- and material-based process identification and optimization for robotic CMT-based wire arc additive manufacturing , 2020 .

[20]  Wang Guilan,et al.  Optimization of surface appearance for wire and arc additive manufacturing of Bainite steel , 2017 .

[21]  A. Molotnikov,et al.  Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications , 2021, Materials & Design.

[22]  Yong Li,et al.  Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives , 2019, Engineering.

[23]  Ao Liu,et al.  Optimization of geometry quality model for wire and arc additive manufacture based on adaptive multi-objective grey wolf algorithm , 2020, Soft Comput..

[24]  Yanling Xu,et al.  Bead Geometry Prediction for Multi-layer and Multi-bead Wire and Arc Additive Manufacturing Based on XGBoost , 2019 .

[25]  Santanu Das,et al.  Effect of Heat Input on Geometry of Austenitic Stainless Steel Weld Bead on Low Carbon Steel , 2019 .

[26]  Rupinder Singh,et al.  Mechanical and morphological investigations of 3D printed recycled ABS reinforced with bakelite–SiC–Al2O3 , 2019, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

[27]  Moncef Gabbouj,et al.  Particle Swarm Optimization , 2014 .

[28]  Paul Witherell,et al.  A Review of Machine Learning Applications in Additive Manufacturing , 2019, Volume 1: 39th Computers and Information in Engineering Conference.

[29]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[30]  Jinglong Li,et al.  A prediction model of layer geometrical size in wire and arc additive manufacture using response surface methodology , 2017 .

[31]  Nikolaus Hansen,et al.  Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.

[32]  Rohit Kshirsagar,et al.  Prediction of Bead Geometry Using a Two-Stage SVM–ANN Algorithm for Automated Tungsten Inert Gas (TIG) Welds , 2019, Journal of Manufacturing and Materials Processing.

[33]  Z. Pan,et al.  Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning , 2021, Journal of Intelligent Manufacturing.

[34]  Zengxi Pan,et al.  Bead modelling and implementation of adaptive MAT path in wire and arc additive manufacturing , 2016 .

[35]  Narendra Kumar,et al.  Analysing the influence of raster angle, layer thickness and infill rate on the compressive behaviour of EVA through CNC-assisted fused layer modelling process , 2021, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

[36]  Anoop Kumar Sood,et al.  Intelligent process model for bead geometry prediction in WAAM , 2018 .

[37]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[38]  John H. Holland,et al.  Genetic Algorithms and Adaptation , 1984 .

[39]  Shanben Chen,et al.  A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system , 2020, Journal of Manufacturing Systems.

[40]  Erfu Yang,et al.  Bubble density gradient with laser detection: A wake-homing scheme for supercavitating vehicles , 2018, Advances in Mechanical Engineering.

[41]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[42]  Alain Bernard,et al.  Weld bead modeling and process optimization in Hybrid Layered Manufacturing , 2011, Comput. Aided Des..

[43]  Akhil Garg,et al.  Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing , 2019, J. Intell. Manuf..

[44]  Lin Wu,et al.  Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis , 2012, Journal of Intelligent Manufacturing.

[45]  Omar Ahmed Mohamed,et al.  Optimization of fused deposition modeling process parameters: a review of current research and future prospects , 2015, Advances in Manufacturing.

[46]  Ahmed El-Shafie,et al.  RBF-NN-based model for prediction of weld bead geometry in Shielded Metal Arc Welding (SMAW) , 2018, Neural Computing and Applications.