Implementing Digital Twins in Smart Manufacturing

The manufacturing industry landscape is continuously evolving with technological advances, and the digital twin is especially changing how companies implement smart manufacturing.

Fremont, CA: Recent years have witnessed the emergence of multiple technologies crucial in advancing smart manufacturing and the Industrial Internet of Things. These technologies include Big Data, advanced analytics, artificial intelligence (AI) and machine learning (ML), operational intelligence, advanced robotics, next-generation material science, and generative design for additive manufacturing. Although all of these technologies are transforming the face of production, the digital twin has the most immediate and profound effect on how organizations use smart manufacturing.

The concept of the digital twin is not entirely novel. This entails integrating virtual engineering models with the real product or equipment in an environment that permits modification and optimization of the as-designed and as-built product. However, due to the development and growth of enabling technologies, there is a resurgence of interest in adopting the digital twin and its potential benefits. Manufacturers can save the time and expense associated with assembling, installing, and validating industrial production systems by using digital twins that reflect the product and production systems. In addition, implementing digital twins for asset management often results in demonstrable advantages for equipment maintenance in the field.

Digital twin in manufacturing is a virtual replica of the as-designed, as-built, and as-maintained physical product, enhanced by real-time process data and analytics based on precise configurations of the physical product, production processes, or equipment. Essentially, this is the operational context of the digital twin required for performance optimization. In contrast to the conceptual character of virtual models, real-time and operational data is a digital depiction of actual physical events. CAD models describe the digital fit, shape, and function of the physical counterpart of the digital twin. To execute analytics applications that define the status and behavior of the performance-based digital twin and enable optimization and process improvement, operational and asset data is collected in real-time.

Based on actual deployments, manufacturers are contemplating new business models in which they offer services instead of products and then use the digital twin to monitor and optimize the service's availability and performance. Customers are offered to use the product/equipment in addition to full maintenance and operational optimization based on the predictive powers of the digital twin. As a more controllable and viable business model, the manufacturer retains equipment ownership and provides maintenance services based on a digital twin.