Digital Twin in Automotive Industry, Revolutionizing autos with Digital Twins! Discover virtual replicas optimizing performance, safety & innovation.
What are digital twins in the automotive industry?
A digital twin in the automotive industry is a virtual replication of an entire car, software, electrics, mechanics, software, electrics, and the physical demeanor of a vehicle. The digital twin has all real-time performance, sensor, and review data, as well as service history, configuration changes, parts replacement, and guarantee data.
What are Some Common Applications of Digital Twins in the Automotive Industry?
Some of the common applications in the automotive industry include:
Product testing
A digital twin of a product helps in defining its quality and performance by effectively experimenting with various compounds and raw materials to improve the design and optimize the performance of the product. For instance, the digital twin of a new tire can be virtually modeled and tested for various climate conditions and optimized according to the last result.
Adding manufacturing capacity
Before installing new machines for manufacturing to increase production, companies can hold digital twins to simulate the effect and advantages of the new machine on the production capability. The virtual model requires considering the characteristics of the company’s product, the material contained, historical data of production time and needed machinery, etc. and then gives insights about how the new machine can improve the production of this product.
Worker training
Companies can build a factory’s structure as a digital twin and train workers remotely without installing the tools physically. For instance, a manufacturing company in Europe can train their workers on a digital twin of the factory even before the final infrastructure installment in Mexico, to facilitate the hiring method and comprehend the training requirements of new hires.
Predictive maintenance
Machines and manufacturing equipment can be utilized to determine the maintenance requirements and enhance the health of production lines and manufacturers. In this case, these are required to hold real-time data extracted from IoT devices and sensors in the manufacturing procedure to see fault reproductions and reasons.
Sales
One of the prospective implications of this technology is with sales, where consumers can offer ideas about the products before they are released to the market. With this vehicle, a company can permit a potential buyer to review the product, explore the new features, and resemble older techniques. Via 3-D visuals of cars, manufacturers can change the features and ask for feedback from their customers before producing the automobile.
Other operations
Another application of digital twin technology is to develop a digital twin of an organization (DTO) which is a digital replication of the company itself. DTO aids companies to comprehend their strategies and optimize them. Since DTO leverages process data held in documents and IT systems, it needs process mining to remove and examine the possibility of log data to generate a DTO.
Benefits of digital twins in the automotive industry
It benefit the general automotive industry by:
Unifying data
It resolves the challenge of combining data from several sources (e.g., Recorded data of prior models, performance data, and driver manners). The manufacturer can examine and consider the data to derive any insight visibly.
Easing verifications
The companies lose time while confirming new features or designs as they have to stay for production to specify the feasibility of their designs. A digital twin can give a fast and trustworthy way of proving design success and effectiveness. It can source all the data needed to run simulations that give real results.
Avoiding failures
The technology can use data to indicate when downtimes of machines can happen by knowing from past data, thus, it can help businesses take steps to sidestep such malfunction, which allows uninterrupted production with minimal financial loss.
Predicting customer demands
Manufacturers collect customer knowledge data regarding which characteristics are primarily used by customers once the car is on the market. Utilizing digital twins, manufacturers can leverage this consumer experience data to get more useful at predicting customer needs, increasing customer experience.
What are the challenges of digital twins in the automotive industry?
Technology adoption
In the automotive industry, an enormous amount of data is developed at each step of the product life cycle of vehicles. Such big data helps build faster, more profitable, and high-quality products. Even automotive manufacturers have various levels of effective utilization of data, and it’s been calculated that companies analyze only 12% of the available data. Assume an international automotive company that operates the production of each stage of vehicles across the world. To develop a digital twin of the vehicle, the maturity of data adoption should be similar across different areas affected by the product life cycle.
