XIONG XIANQING, FU SIJIE, YUE XINYI, et al. Construction and Technology of Digital Twin Model in Customized-Furniture Intelligent Manufacturing Workshop. [J]. Chinese journal of wood science and technology, 2024, 38(6): 69-78.
DOI:
XIONG XIANQING, FU SIJIE, YUE XINYI, et al. Construction and Technology of Digital Twin Model in Customized-Furniture Intelligent Manufacturing Workshop. [J]. Chinese journal of wood science and technology, 2024, 38(6): 69-78. DOI: 10.12326/j.2096-9694.2024066.
Construction and Technology of Digital Twin Model in Customized-Furniture Intelligent Manufacturing Workshop
Combining intelligent furniture manufacturing with the current digital workshop situation
the key to improving the operational efficiency of customized furniture workshops is to enhance the level of cyber-physical integration and build a digital twin model. To this end
this study proposes an architectural model
operating mechanism
and key technologies for a digital twin workshop in intelligent furniture manufacturing. It discusses the application of a digital twin workshop in customized furniture manufacturing. It analyzes the process from four aspects: the selection of process parameters and decision-making in intelligent furniture manufacturing
dynamic workshop scheduling for the flexible manufacturing of customized furniture
failure prediction and health management of equipment
and logistics and distribution. It promotes the rapid establishment of a digital workshop for customized furniture manufacturing. This study provides a theoretical foundation for the construction and application of a digital twin workshop in customized furniture manufacturing.
TAO F, LIU W R, LIU J H, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18.
XIONG X Q, YANG L J, XU X T, et al. Status of research and application of intelligent manufacturing technology for solid wood furniture in China[J]. Journal of Forestry Engineering, 2024, 9(5): 27-35.
YANG J A, WU Z H. Digital transformation path and system architecture for traditional home furniture companies[J]. Chinese Journal of Wood Science and Technology, 2022, 36(6): 32-40.
LIU L Y, DU H X, WANG H F, et al. Construction and application of digital twin system for production process in workshop[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1536-1545.
OUYANG Z Z, WU Y Q, TAO T, et al. Construction of furniture digital twin shop-floor (FDTS) and prospect of key technologies for “made in China 2025”[J]. Furniture & Interior Design, 2022, 29(8): 1-7.
李治达, 吴新凤. 家具制造产业数字孪生研究[J]. 居舍, 2023, (35): 15-18.
LI Z D, WU X F. Research on digital twin in furniture manufacturing industry[J]. Dwelling, 2023, (35): 15-18.
XUE J X, XU W, WANG J X. Load balancing of small-panel production line for panelized customized furniture based on value stream diagram[J]. Journal of Forestry Engineering, 2023, 8(6):176-185.
YANG J A, WU Z H, XIA T Q, et al. Construction thought and layout method of digital machining workshop for solid wood furniture[J]. Chinese Journal of Wood Science and Technology, 2023, 37(5): 12-19.
HOU X P, WU Z H, LIU H B, et al. Information interconnection and intercommunication method for woodworking equipment based on open platform communications unified architecture(OPC UA)[J]. Chinese Journal of Wood Science and Technology, 2022, 36(6): 95-102.
ZHANG Z K, SHAO Z F, WANG L P, et al. Digital workshop information model and its standardization[J]. Journal of Tsinghua University (Science and Technology), 2017, 57(2): 128-133, 140.
SÖDERBERG R, WÄRMEFJORD K, CARLSON J S, et al. Toward a digital twin for real-time geometry assurance in individualized production[J]. CIRP Annals, 2017, 66(1): 137-140.
WANG G K, XIONG X Q, YANG L J, et al. Analysis of current situation and development of splitting software for digital manufacturing of panel furniture[J]. Journal of Forestry Engineering, 2024, 9(3): 175-183.
XIONG X Q, ZHANG M, YUE X Y, et al. Research progress in the application of big data technology to smart home manufacturing[J]. World Forestry Research, 2023, 36(2): 74-81.
WU X H, ZHU J G. Discussion on the digital twin model of intelligent workshop of custom panel furniture[J]. Chinese Journal of Wood Science and Technology, 2023, 37(1): 25-32.
JONES D, SNIDER C, NASSEHI A, et al. Characterising the Digital Twin: a systematic literature review[J]. CIRP Journal of Manufacturing Science and Technology, 2020, 29: 36-52.
QI Q L, TAO F, HU T L, et al. Enabling technologies and tools for digital twin[J]. Journal of Manufacturing Systems, 2021, 58: 3-21.
XIONG X Q, CAO M, MA Q R, et al. Research on group classification and processing method of special-shaped parts of solid wood furniture[J]. Journal of Forestry Engineering, 2023, 8(6): 186-192.
HAN J, WU Z H. Production planning and scheduling based on MES for customized panel furniture companies[J]. Chinese Journal of Wood Science and Technology, 2019, 33(3): 32-35.
AGRAWAL A, FISCHER M, SINGH V. Digital twin: from concept to practice[J]. Journal of Management in Engineering, 2022, 38(3): 06022001.
FANG Y, LIU J, LYU R Q, et al. Research on monitoring technology of equipment processing based on digital twin[J]. Aeronautical Manufacturing Technology, 2021, 64(4): 91-96.
LU G Z, XIONG X Q, LU D T, et al. Research on quality control method of furniture sealing based on SPC[J]. Journal of Forestry Engineering, 2023, 8(1): 195-201.
LI X Y, HUANG J P, LI J H, et al. Research and development trend of intelligent shop dynamic scheduling[J]. Scientia Sinica (Technologica), 2023, 53(7): 1016-1030.
GUO J P, ZHAO N, SUN L, et al. Modular based flexible digital twin for factory design[J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1189-1200.
WANG C C, CHIEN C H, TRAPPEY A J C. On the application of ARIMA and LSTM to predict order demand based on short lead time and on-time delivery requirements[J]. Processes, 2021, 9(7): 1157.
CUI P H, WANG J Q, LI Y. Data-driven modelling, analysis and improvement of multistage production systems with predictive maintenance and product quality[J]. International Journal of Production Research, 2022, 60(22): 6848-6865.