{"id":51974,"date":"2023-09-13T10:51:31","date_gmt":"2023-09-13T14:51:31","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=51974"},"modified":"2026-03-26T06:32:38","modified_gmt":"2026-03-26T10:32:38","slug":"simcenter-testlab-neo-2306-cad-frf","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/simcenter-testlab-neo-2306-cad-frf\/","title":{"rendered":"Listening to your future product: CAD-centric FRF testing and Virtual Point Transformation"},"content":{"rendered":"\n

What\u2019s New in Simcenter Testlab Neo<\/strong><\/strong> 2306?<\/strong><\/p>\n\n\n\n

In the ever-evolving landscape of product development in the context of noise and vibration, a remarkable transformation is underway – one that is reshaping the way industries conceptualize, design, and test their innovations. <\/p>\n\n\n\n

Gone are the days when physical prototypes dominated the scene, consuming time, and resources, and often leading to extensive trial-and-error cycles. Instead, a paradigm shift is taking place, championing the era of digital twins and predictive capabilities. This allows component suppliers to demonstrate how their products will sound without having to physically integrate them into their customer vehicles. <\/p>\n\n\n\n

At the forefront of this transformation lies the concept of “System NVH Prediction” – a revolutionary approach that demands accurate and efficient Frequency Response Function (FRF) measurements. These transfer functions are used to represent each sub-system in a modular way. <\/p>\n\n\n\n

This article delves into the fascinating journey of this ongoing evolution of reducing their reliance on physical prototypes. Join us as we unravel the pivotal role of FRF measurement in this journey toward enhanced efficiency, precision, and unprecedented foresight.<\/p>\n\n\n\n

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Figure 1 \u2013 System NVH Prediction Flowchart<\/p>\n\n\n\n

Mirror your test setup virtually<\/h2>\n\n\n\n

Accurate measurement of setup geometry stands as a cornerstone for reliable testing, yet practical measurement constraints often introduce potential inaccuracies. Inaccessible measurement locations limit data collection comprehensiveness. <\/p>\n\n\n\n

The reduced prototype availability and the necessity to compare test and simulation results for validation, alongside preparing and fully documenting the setup adds layers of complexity. <\/p>\n\n\n\n

Finally, one needs to ensure uniformity in sensor naming and positioning across the entire organization – between simulation and test.<\/p>\n\n\n\n

\"Figure<\/figure>\n\n\n\n


Figure 2\u2013 Virtual instrumentation of a gearbox<\/p>\n\n\n\n

Enter virtual instrumentation \u2013 a transformative approach enabled using CAD models to drive the data acquisition process and analysis. Through this approach, engineers can overcome these challenges. Since prototypes are only available for a limited time, the CAD geometry allows the engineer to prepare and optimize his measurement setup upfront. <\/p>\n\n\n\n

The virtual instrumentation process involves integrating the use of CAD models into the entire data acquisition process, virtually instrumenting the test objects and specifying the sensor data. This allows the engineer to mirror and prepare real-world test setups<\/p>\n\n\n\n

\"Figure<\/figure>\n\n\n\n

Figure 3 \u2013 CAD centric transfer function measurement process<\/p>\n\n\n\n

The wealth of information extracted from these setups aids the measurement engineer in precisely defining the optimal setup for the required FRF characterization. Highlighting potential issues, minimizing errors, and enhancing the alignment between intended and realized configurations.  <\/p>\n\n\n\n

Throughout the measurement process from the setup, impact acquisition and validation, the virtual instrumentation can be used as guidance.<\/p>\n\n\n\n