MIMO Model Predictive Control of Bead Geometry in Wire Arc Additive Manufacturing

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Publicado en:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings (2021)
Autor principal: Mu, Haochen
Otros Autores: Pan, Zengxi, Li, Yuxing, He, Fengyang, Polden, Joseph, Xia, Chunyang
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The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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001 2595725897
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024 7 |a 10.1109/CYBER53097.2021.9588331  |2 doi 
035 |a 2595725897 
045 2 |b d20210101  |b d20211231 
084 |a 228229  |2 nlm 
100 1 |a Mu, Haochen  |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 
245 1 |a MIMO Model Predictive Control of Bead Geometry in Wire Arc Additive Manufacturing 
260 |b The Institute of Electrical and Electronics Engineers, Inc. (IEEE)  |c 2021 
513 |a Conference Proceedings 
520 3 |a Conference Title: 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)Conference Start Date: 2021, July 27 Conference End Date: 2021, July 31 Conference Location: Jiaxing, ChinaGeometric properties of material deposited by the wire arc additive manufacturing (WAAM) process often deviates from desired setpoints. To improve the accuracy and repeatability of the WAAM process, an effective control strategy to maintain desired deposition geometry that operates robustly under various welding conditions is required. In this work, a control strategy utilizing multi-input multi-output (MIMO) model-predictive control (MPC) is presented. This approach, based on linear autoregressive (ARX) modelling, aims to improve the accuracy and flexibility of deposited bead geometry in the WAAM process. The MPC controller updates welding parameters between successive layers by minimizing a cost function based on sequences of input variables. Measurements of deposited bead geometry are made by laser scanner and input to the linear ARX model, which then makes future bead geometry predictions. Weighting coefficients of the ARX model are trained iteratively throughout the manufacturing process. Experimental results show that the derived control strategy can reduce fluctuations in a part's height by 400% and maintain the fluctuation within an acceptable range. In addition, the fluctuations in bead width along a single weld seam was also improved by more than 50%. 
653 |a Predictive control 
653 |a Wire 
653 |a Arc deposition 
653 |a Autoregressive models 
653 |a Sequences 
653 |a Welding parameters 
653 |a Laser beam welding 
653 |a Manufacturing 
653 |a Cost function 
653 |a Geometry 
653 |a Geometric accuracy 
653 |a Additive manufacturing 
653 |a MIMO (control systems) 
653 |a Welding 
653 |a Manufacturing industry 
653 |a Fluctuations 
653 |a Environmental 
700 1 |a Pan, Zengxi  |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 
700 1 |a Li, Yuxing  |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 
700 1 |a He, Fengyang  |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 
700 1 |a Polden, Joseph  |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 
700 1 |a Xia, Chunyang  |u School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong,Wollongong,NSW,Australia,2522 
773 0 |t The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings  |g (2021) 
786 0 |d ProQuest  |t Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/2595725897/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch