Manufacturing is a complex, intricate process with dozens of moving parts. Should an error occur, your business is liable to incur massive losses. Fortunately, with the rise of IIoT and burgeoning technologies, potential errors and/or system failures are being quelled before they become disastrous. Below we’ll walk you through the concept of digital twins, a technology that facilitates preventative testing, and the benefits it can offer your business in the long run.
A digital twin is a digital representation of a physical object or system. In different terms, a digital twin is “a computer program that takes real-world data about a physical object or system as inputs and produces as outputs predictions or simulations of how that physical object or system will be affected by those inputs." Essentially, a digital twin allows for additional testing, whether of a machine or system, in a totally digital space without any risk to actual devices.
By compiling data from real-world sensors, the twin can simulate the physical object or system in real-time, offering acute insight into performance and potential problems. Because of the importance of constant, real-time data, IoT sensors are contingent on the success and accuracy of digital twin simulations. So as IoT sensors become more and more refined, so too will the predictive abilities of digital twins. In fact, digital twins are already being utilized now to predict different outcomes based on variable data. This means that simulations can be run and rerun, testing for the best possible solution to a problem.
Digital twins have been used to great success in numerous manufacturing jobs. For example, an industrial manufacturer began implementing digital twin technology into its business after facing a handful of quality issues in the field, resulting in expensive maintenance and extremely high warranty liability. By using a digital twin, this business combined their as-designed bill of materials with their as-manufactured bill of materials (this includes all analogous information produced during manufacturing). This synthesis allowed them to run additional analytics and garner specific insights into production issues affecting their product’s quality. Because of this, the team was able to alter and improve the assembly process by nearly 25%.
Manufacturing Process Digital Twin Model