What is a digital twin?
A digital twin is a virtual representation of a physical product or process that is used to understand and predict the performance characteristics of its physical equivalent. Digital twins are used throughout the product lifecycle to simulate, predict and optimize the product and production system before investing in prototypes and physical assets.
By incorporating multi-physics simulation, data analytics and machine learning capabilities, digital twins can demonstrate the impact of design changes, use cases, environmental conditions and countless other variables. This avoids the need for physical prototypes, thereby reducing development time and improving the quality of the finished product or process.
To ensure accurate modeling throughout the life of a product or its production, digital twins use data from sensors installed on physical objects to determine real-time object performance, operating conditions and changes over time. With this data, the digital twin evolves and is continuously updated to reflect any changes to its physical counterpart throughout the product lifecycle. This creates a closed feedback loop in a virtual environment that allows companies to continuously optimize their products, production and performance for minimal cost.
The possible applications of a digital twin depend on the stage of the product lifecycle they model. In general, there are three types of digital twins: product, production and performance, which are explained below. The combination and integration of the three digital twins as they evolve together is known as a digital lattice. The term “lattice” is used because it is interwoven throughout all stages of product lifecycles and production systems and brings together data from all these stages.
Digital product twins can be used to virtually validate product performance, in our case, machinery and production lines, and also show how products actually perform in the physical world. This “digital twin of a product” provides a connection between the virtual and physical world that allows you to analyze the performance of a product under various conditions and make adjustments in the virtual world to ensure that the next physical product performs exactly as planned in the field. It doesn’t matter if the systems and materials are complex: digital product twins help to circumvent this complexity to make the best possible decisions. This avoids the need for multiple prototypes, reduces overall development time, improves the quality of the final manufactured product, and allows for faster iterations in response to customer feedback.
The cycle time for product commissioning is usually almost linear, from idea and design, to mechanical manufacturing, to electrical installation, and then programming.
In contrast, with the use of the digital twin we can overlap different stages of execution. It is possible to start programming the line or machine before manufacturing it or during its design to verify its correct operation. This reduces time by approximately 30% and could reduce prototyping costs, if any.
A digital production twin can help validate how efficiently a manufacturing process will work on the shop floor before anything actually goes into production. By simulating the process with a digital twin and analyzing why things happen using the digital lattice, companies can create a production methodology that maintains its efficiency under various conditions.
Production can be further optimized by creating digital product twins of all manufacturing equipment. Using data from digital product and production twins, companies can avoid costly equipment downtime and even predict when preventive maintenance will be necessary. This constant flow of accurate information enables faster, more efficient and more reliable manufacturing operations.
Smart products and plants generate massive amounts of data on their utilization and efficiency. The digital performance twin captures this data from running products and plants and analyzes it to provide useful information for informed decision making.
Thanks to digital performance twins, companies can:
Generate new business opportunities
Gain insights to improve virtual models
Capture, aggregate and analyze operational data
Improve product and production system efficiency.
Regardless of the above classification, we can classify the digital twin according to its scope of use with respect to the product cycle.
Pre-sales to analyze a solution before selling it.
Planning and design. A model can be created to verify that the designs, functional analysis are correct for the scope of the project. It is also feasible to emulate the control software (PLC, robots, etc.) to validate it.
After-sales. To correct incidences in the software, improve the performance of the line or analyze possible changes in the lines without the need to implement it physically.
This problem is minimized with the use of the digital twin.
The sale of lines has a series of problems that the digital twin model can solve without the need to physically replicate it in the facilities. The main ones are:
Size of the lines to be installed. The dimensions of these are not feasible to mount in INEMUR’s facilities to be able to test its control and efficiency with what is sold.
Absence of intermediate machines. On certain occasions, machines are purchased from third party suppliers and/or provided directly by the end customer, which means that they cannot be integrated into the line to be tested in its entirety.
Inability to reproduce production speeds. In most cases it is not feasible to reproduce production conditions and/or speeds. There is not enough product or it is unfeasible to introduce by hand the products to be produced.
This causes the machinery to be installed at the customer’s facilities without being fully tested, which results in:
High start-up time due to the need for more time to debug the control programs.
Loss of time in replacement of parts that do not work as intended.
More qualified or experienced personnel to start up the machinery.
The digital twin technology is in full evolution and adoption by different sectors of the world economy and the industrial sector is not being indifferent. The concept of the digital twin is being heard more and more and this is motivated by the rise and settlement of Industry 4.0. and that the technology is becoming more mature and allows more sophisticated methods. Morgan Stanley indicates that the cybersecurity market, a prerequisite for the connected industry, will reach $183 billion by 2020. Gartner gives even higher figures for the IoT industry. It predicts that half of all large industrial companies will have adopted the digital twin model, providing them with 10% efficiency gains.
In other words, industry is more connected than ever before, and in an increasingly secure way. IoT projects are more scalable and are starting to take off. There has also been strong investment in automation, robotics, sensorization… However, much of these efforts have been directed at solving specific problems. Now, the challenge is to connect all the efforts to maximize their benefit, and that is where digital twins can come into play.
According to a study by Morning Consult and IBM Spain is pioneering in Europe in the adoption of artificial intelligence (AI) technology, where the concept of digital model has a place, in which it is said that 82% of Spanish companies are implementing or exploring the incorporation of artificial intelligence technologies to their processes. An average of 9 points above the average of the major European countries (Germany, France, Italy, United Kingdom and Spain).
Their implementation is focusing on the areas of security (42%), process automation (31%) and customer service (29%). The barriers that are hindering further progress, Spanish companies point out in the first place the lack of expert knowledge (39%), highlighting the training and adaptation challenge that the development of artificial intelligence represents for companies and society as a whole. Despite this barrier, the vast majority of Spanish companies (72%) say that confidence in the technology is the factor that is contributing most to creating a favorable culture for the adoption of AI in their organizations.