Design and modelling of personalised multifunctional furniture based on smart networks and cloud rendering platforms
Year: 2026
Type: article
Abstract: This study proposes an integrated design and modeling method for personalized multifunctional furniture based on intelligent networks and a cloud-rendering platform. A multisource requirement perception model is developed to transform users’ functional preferences, anthropometric dimensions, spatial constraints, aesthetic preferences, and budgets into structured design features. Parametric modeling, functional-module configuration, motion-constraint analysis, and cloud-based collaborative verification are then integrated to support the automatic generation and interactive modification of furniture schemes. A dynamic testing platform is established to evaluate the correspondence between physical furniture operations and cloud-rendered model states. The results show that the median dynamic errors of the m-sequence and OPRM schemes are approximately − 5.3% and − 5.5%, respectively. The OPRM error distribution ranges approximately from − 6.2% to − 4.8%, indicating greater adaptability to complex movements but higher variability. OPRM reduces the testing and model-calibration time from approximately 200 min to 50 min, corresponding to a reduction of about 75%. Its error fluctuations are mainly concentrated within the first one or two measurement cycles and subsequently converge toward a stable state. The proposed method consequently supports rapid personalized scheme generation, dynamic model correction, and reliable cloud-based visualization of multifunctional furniture.
Source: Scientific Reports
Authors: Huanhuan Zhang, Qing Cao, Moqian Xu
Language: English
Subfield: Architecture
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7223265224