Transforming Product Design, Manufacturing, and Service with Digital Twin and Big Data Insights

Abstract

The big data-driven manufacturing age is here, ushering in next generation information technology into industry and
production. Product lifecycle data may be acquired during the whole product lifetime, including design, production, and
service. However, much of the study on this topic focuses on actual items rather than virtual models. Furthermore,
manufacturing firms are left with meaningless, fragmented, and isolated data throughout the product lifecycle as a result of
the absence of convergence between the physical and virtual spaces of products. In the design, production, and servicing
stages of a product, these issues cause a lack of intelligence, efficiency, and sustainability. To back up product design,
production, and service, however, you'll need data on the physical product, data on the virtual product, and data that links
the two. Based on our previous research on big data in PLM, we are now investigating and emphasizing ways to create and
utilize converged cyber-physical data to improve product lifecycle. This will drive smarter, more efficient product design,
manufacturing, and service. An innovative approach to product design, production, and service delivery based on digital
twins is put forward in this study. The study delves into the specific ways and frameworks for using digital twins in product
design, production, and service. In addition, we provide three examples to show how digital twins may be used in the future
at different stages of a product's lifecycle.