Digital Twin: Use Case Automotive Audio 

January 16, 2020:

 

For years, Gartner has considered the digital twin to be one of the most important technology trends [1] and claims that digital twins are slowly entering mainstream use [2]. In product development, production and service, the use of the technology promises enormous benefits through the possibility of monitoring the behavior of products and production processes with the help of a digital twin, developing new customer-oriented services or drawing conclusions for product improvements from the product data. Digital twins can change and accelerate the entire engineering process.  

However, the digital twin concept is neither standardized nor clearly defined. Often there is a different understanding of what exactly a digital twin is. Gartner defines a digital twin as a software design pattern that represents a physical object with the objective of understanding the asset’s state, responding to changes, improving business operations and adding value. [2] 

The description of Deloitte Consulting is more detailed. Deloitte Consulting states that a digital twin can be defined, fundamentally, as an evolving digital profile of the historical and current behavior of a physical object or process that helps optimize business performance. Deloitte Consulting claims, that the digital twin is based on massive, cumulative, real-time, real-world data measurements across an array of dimensions. These measurements can create an evolving profile of the object or process in the digital world that may provide important insights on system performance, leading to actions in the physical world such as a change in product design or manufacturing process. [3] 

From both definitions it can be interpreted that the focus on digital twins is to provide insight into how a product could perform more effectively, data about new products and increased efficiency and to improve enterprise decision making by providing information on development, reliability or maintenance.  

According to Gartner the complexity of digital twins vary based on the use case, the vertical industry and the business objective. In some cases, there are simple, functional digital twins that are based on clearly defined functional or technical parameters. Other cases may require physics-based high-fidelity digital twins. In still other cases, there are compound systems composed of other digital twins that need to be integrated. [4] 

In this article we feature with the 

1. Digital Product Twin in automotive audio, which serves to virtually develop the sound system and put it into operation and to improve it with the results of the simulation data.

2. Digital Service Twin in automotive audio, which can be used to accompany the real product in operation and enables services such as predictive or prescriptive maintenance 

 

The Digital Twin of Sound Systems in Vehicles 

The Digital Product Twin of sound systems in vehicles helps to explore the impact of different design alternatives and allows simulations and tests to ensure that product designs meet the requirements. The Digital twin enables to deal with complex product requirements and regulatory requirements. 

Digital Twin developed by Mvoid Group
To realize the digital audio sample in the development phase, the digital twin developed by Mvoid Group considers all essential parameters necessary for a virtual listening experience. In the first phase, a product development environment is created with the help of multiphysical simulations. Methods and tools of numerical acoustics are used to calculate the best possible position of the loudspeakers and sound fields as well as the radiation of vibrating structures in the vehicle cabin (Mvoid methodology, level 1 – 3).  

A prerequisite for the digital representability of the virtual audio system is the data quality and completeness of the CAD data. The decisive factor is how well and how detailed the data from the design program is converted in the virtual model. For more information in this regard we recommend to read our insight: Virtual development of sound systems – the quality of data is crucial 

In the second phase, the virtual sound system is made audible through virtual tuning and auralization (Mvoid methodology, level 4 – 5). In this phase, the acoustics experts already get a realistic impression of the listening experience and performance of the sound system and can find the right configuration. 

The next step focuses on measurements of physical prototypes and products. A multi-channel measurement system guarantees a smooth transition from virtual prototypes to physical prototypes. It offers a robust, repeatable process that provides accurate, detailed insight into mechanical and performance characteristics of audio components and systems, including in-situ room acoustic interaction. Critical to understanding manufacturing and assembly execution of design, this level converges the implementation of Virtual Product Development into real audio products through a robust validation process. Further, manufacturing variance may be monitored and if necessary, adapted to minimize production variance from a reference target. 

In our opinion, it is also conceivable to use a Digital Service Twin, which can accompany the real product in operation and enables services such as predictive or prescriptive maintenance. The sound system could be tested during maintenance with the help of the digital service twin at a time interval to be defined. In this way, for example, the effects of wear can be analyzed. Wear usually changes the geometry of components, which also affects the sound system. A regular calibration of the sound system can reduce effects of wear and tear. 

 

Selected examples for the application of digital twins in Automotive Audio 

Figure: Simulation Meshes for Low to Mid to High Frequencies by Means of Geometrical Acoustics (left) and FEA (right)

 

  • Analysis of performance variances in production vehicles
    That audio systems in production vehicles show a deviation from vehicle to vehicle [6], is known. The analysis of performance variances in production vehicles is a complex problem compounded by measurement uncertainty in real vehicles that obfuscates variance as well as the true root cause of variance. Utilizing a digital product twin where only a single desired change is implemented at any one time, variables can be analyzed.  

 

  • Comparisons of loudspeakers within manufacturing tolerances
    When analyzing the effect on a full system when samples of loudspeakers with slightly different but allowable production tolerances are compared, the simple act of substituting a single loudspeaker may introduce overlooked changes not attributable to the device parameter differences. For example, removing the door trim, disconnecting a loudspeaker, replacing the loudspeaker, connection and door trim can perturb the system in an unexpected manner. These differences can be measured objectively compared to a reference system. Moreover a digital twin allows system comparisons with loudspeakers that have a lower or higher sensitivity.   

At this point it should be noted that there is no specification at system level to check whether a system complies with a standard with a specific data metric for acoustic performance. Furthermore, there is no detail or method for quantifying metrics of the reference system to quantify whether a production system conforms within a certain tolerance of the reference system. The general practice is to provide customer specifications for components only. 

 

  • Outlook/Thinkable: Maintenance of sound systems in vehicles
    When customers complain to their auto dealer about the quality of the audio system in their vehicle, service technicians follow procedures to analyze and resolve the complaint. But the lack of robust audio system and acoustics diagnostics process that measures and compares system performance against known metrics leads to a practice of replacing components to see if a complaint or problem is alleviated.
     

A study by Mvoid showed that the analysis of an installation deviation of missing woofer seals had a significant effect on the sound system. The system performance was reduced in a way that can trigger a warranty claim. A digital twin can analyze the sound system and help to find and solve the problem 

Furthermore, the investigation of wear and tear and its impact on the sound system is thinkable.  

 

Benefits of Digital Twins  

Graphic based on Gartner

 

Higher efficiency: The (permanent) monitoring of the physical audio system by the digital twin enables the determination of the optimal parameters and thus ensures a perfect listening experience. 

Higher quality: Data from the entire process chain (from the initial concept through the further development process to production) allow comprehensive and continuous quality assurance, also for future sound systems in next generation vehicle models. 

Reduced risks: (Permanent) monitoring of the sound system by the digital twin detects anomalies and potential faults at an early stage.  

More flexibility: Compared to previous, conventional solutions, the usage of digital twins enables significantly more flexible control options. 

Greater transparency: Continuously collected data on physical sound systems make processes visible and analyzable. This creates a broad database of sound systems and enables the collection and interpretation of additional empirical values. 

Stronger growth: The high and increasing availability of data (in conjunction with analytics functionalities) helps to identify new potentials, such as maintenance scenarios for sound systems. 

 

The extent to which additional benefits can be achieved by the digital twin depends to a large extent on how consistently the concept of the digital twin can be implemented. Gartner claims, that digital twins have huge potential benefits, but creating and maintaining them can be both risky and difficult [5]. Well-documented practices, tutorials and training on 3D modeling, virtual tuning, auralization as well as measurement techniques and best practices can help to avoid digital twin failure and help to make engineers more productive – and for ensuring models are as flexible as possible for the purposes of editing and augmenting.  

In addition, the size of the development environment (platform) represents a determining component: The quality of insights about identical or similar elements increases with the number of integrated sound systems respectively loudspeaker. These additional empirical values on e.g. the behavior of various materials, product life or maintenance cycles enable optimized business models that can be better monetarized (for example: maintenance service – Shift in revenue from hardware sales to service models). 

 

Conclusion 

Modern technologies and concepts, such as the digital twin, ensure that competitive advantages are realized and new perspectives for future product developments are possible. At Mvoid, we believe that digital twins, which cover the life cycle of a product and form the basis for associated products and services, will be a business necessity in the future. 

Mvoid has developed an environment to establish a digital twin for sound systems in vehicles. The basis is the multiphysical simulation (Mvoid methodology, levels 1 to 3), the virtual tuning and auralization (Mvoid methodology, levels 4 and 5) as well as measurement methods of physical products (Mvoid methodology, level 6). 

Automobile manufacturers and consumers benefit equally from intelligent digital twins.   

 

References: 

[1] Gartner, Gartner Top 10 Strategic Technology Trends for 2019, Oct 15,2018: https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019/ , viewed online on Dec 3rd, 2019

[2] Gartner, Press Release: Gartner Survey Reveals Digital twins Are Entering Mainstream Use, Feb. 20, 2019: https://www.gartner.com/en/newsroom/press-releases/2019-02-20-gartner-survey-reveals-digital-twins-are-entering-mai, viewed online on Dec 6th, 2019 

[3] Deloitte Consulting: Industry 4.0 and the digital twin – Manufacturing meets its match, May 12, 2017: https://www2.deloitte.com/us/en/insights/focus/industry-4-0/digital-twin-technology-smart-factory.html, viewed online on Dec 6th, 2019. 

[4] Gartner: Prepare for the Impact of Digital twins, Sep. 18, 2017:  

https://www.gartner.com/smarterwithgartner/prepare-for-the-impact-of-digital-twins/  , 18.09.2017, viewed online on Dec 03rd, 2019 

[5] Gartner: Confront Key Challenges to Boos Digital twin Succes, March 13, 2018: https://www.gartner.com/smarterwithgartner/confront-key-challenges-to-boost-digital-twin-success/ , viewed onlin on Dec 6th, 2019 

[6] S. Hutt, “Audio System Variance in Production Vehicles”, AES 48th International Conference Automotive Audio, 2012 

 

Picture Source:
Head: Adobe #237293169
All other pictures, Mvoid Technologies GmbH