Digital twin for product design collaboration: a systematic literature review

Product lifecycle management (PLM) and digital twin are two interrelated ideas increasingly employed in today's manufacturing and engineering. Integration of digital twins with PLM provides various advantages, including collaboration, better productivity, improved product quality, increased innovation, and shorter time-to-market. Organizations may better identify and handle possible issues, enhance performance, and improve decision-making across the product lifecycle by utilizing digital twin technologies inside a PLM framework. Therefore, this article takes an approach to identify the main trends of the digital twin applied in the context of PLM, as well as to identify research gaps. The method used to conduct this systematic literature review was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) with a total of 157 articles being considered for this research. In addition, this article identifies proposed models of digital twin architectures offered as a service and how the product design concept can be driven by the digital twin in decision-making. Another contribution to define how it may be possible to automate the search for product information using augmented reality and artificial intelligence. The prospects for the integration of digital twins and PLM systems are promising, given the continuous advancements in technology. A notable area of potential growth is the application of artificial intelligence (AI) and machine learning to enhance the automation and optimization of processes within the digital twin framework. In conclusion, the integration of AI and machine learning with digital twins and PLM systems is ready to drive substantial innovations in manufacturing and engineering, promoting improved operational efficiencies and product performance.