Enhancing the Sustainability of Smart Systems in Furniture Design Using Digital Twin Technology Supported by Deep Learning

Abstract:

The field of furniture design is undergoing fundamental transformations with the advancement of Fourth Industrial Revolution technologies, including the Internet of Things (IoT), Digital Twins, Cloud Computing, and Deep Learning (DL), aiming to achieve advanced environmental and economic sustainability. These domains face growing challenges that require the development of intelligent furniture design systems that balance functional and aesthetic innovation while reducing environmental impact.

This research explores the role of digital twins supported by deep learning in enhancing the sustainability of smart systems in furniture design. It involves constructing a dynamic virtual model that simulates the real-world performance of the product. This model aggregates live data from smart sensors within an IoT framework, enabling real-time monitoring of the product’s lifecycle and performance analysis. Deep learning algorithms are employed to analyze data, improving design quality, product customization, and predictive maintenance to reduce costs. Cloud computing supports data storage and processing, while big data analytics extracts precise insights to enhance design decision-making.

A descriptive-analytical methodology was adopted to examine the impact of Fourth Industrial Revolution technologies—such as digital twins(DT) , deep learning(DL), and cloud computing—on furniture design, evaluating how their integration promotes sustainability. Findings indicate that combining digital twins(DT), deep learning(DL), Internet of Things )IoT(, and data computing enables a qualitative shift toward smart, sustainable furniture design. This approach offers flexible, customized, and eco-efficient solutions, supporting the circular economy and sustainable development. It empowers designers and manufacturers to make informed decisions, improve product quality, and minimize waste, strengthening User experience and the furniture industry’s position within smart manufacturing trends.