Performance evaluation of multi-stage manufacturing systems operating under feedback and feedforward quality control loops

被引:3
作者
Magnanini, Maria Chiara [1 ]
Demir, Ozan [1 ]
Colledani, Marcello [1 ]
Tolio, Tullio [1 ]
机构
[1] Politecn Milan, Dept Mech Engn, Via La Masa 1, I-20156 Milan, Italy
关键词
Manufacturing system; Quality control; Decision model; NEURAL-NETWORK; DESIGN;
D O I
10.1016/j.cirp.2024.04.015
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In manufacturing, the essential product characteristics are often created through multiple stages. Coupling product data obtained through inspection and controllers based on decision models with prediction capabilities enables quality control loops, enhancing both feedback and feedforward mechanisms. This paper proposes a methodology to merge the formulation of feedback and feedforward quality control loops into a performance evaluation model for multi-stage manufacturing systems. This approach evaluates quality control loop impacts system-wide, aiding in configuring and reconfiguring quality gates. A case study illustrates how allocating inspection technologies and efficient decision models improves overall system performance through effective feedback and feedforward control loops. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of CIRP. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
引用
收藏
页码:349 / 352
页数:4
相关论文
共 16 条
[1]   Impact of quality control on production system performance [J].
Colledani, M. ;
Tolio, T. .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2006, 55 (01) :453-456
[2]   Design and management of manufacturing systems for production quality [J].
Colledani, Marcello ;
Tolio, Tullio ;
Fischer, Anath ;
Iung, Benoit ;
Lanza, Gisela ;
Schmitt, Robert ;
Vancza, Jozsef .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2014, 63 (02) :773-796
[3]  
Demir Ozan Emre, 2023, Production Processes and Product Evolution in the Age of Disruption: Proceedings of the 9th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2023) and the 11th World Mass Customization & Personalization Conference (MCPC2023). Lecture Notes in Mechanical Engineering, P247, DOI 10.1007/978-3-031-34821-1_27
[4]   Robust model-based control of multistage manufacturing processes [J].
Djurdjanovic, Dragan ;
Ul Haq, Asad ;
Magnanini, Maria Chiara ;
Majstorovic, Vidosav .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2019, 68 (01) :479-482
[5]   Incremental discovery of new defects: application to screwing process monitoring [J].
Ferhat, Mahmoud ;
Ritou, Mathieu ;
Leray, Philippe ;
Le Du, Nicolas .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2021, 70 (01) :369-372
[6]   Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement [J].
Franciosa, Pasquale ;
Sokolov, Mikhail ;
Sinha, Sumit ;
Sun, Tianzhu ;
Ceglarek, Dariusz .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2020, 69 (01) :369-372
[7]   Integrated process-system modelling and control through graph neural network and reinforcement learning [J].
Huang, Jing ;
Zhang, Jianjing ;
Chang, Qing ;
Gao, Robert X. .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2021, 70 (01) :377-380
[8]  
Izquierdo L.E., 2007, TRANSACTION NAMRISME, V35, P295
[9]   Virtual sensing and virtual metrology for spatial error monitoring of roll-to-roll manufacturing systems [J].
Jin, Xiaoning ;
Shui, Huanyi ;
Shpitalni, Moshe .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2019, 68 (01) :491-494
[10]   Optimization of selective assembly and adaptive manufacturing by means of cyber-physical system based matching [J].
Lanza, Gisela ;
Haefner, Benjamin ;
Kraemer, Alexandra .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2015, 64 (01) :399-402