AI and Automation Unlock Smarter NVH Testing with Fewer Engineers

Automation and AI are Transforming NVH Testing, Enabling Smaller Teams to Handle Complex Challenges

In the high-stakes environment of automotive and industrial development, labs and production floors are shifting their perspective on the global NVH testing market, moving away from viewing it as a conventional engineering discipline and embracing it as a highly complex, fast-moving landscape. This evolution is driven by the realization that keeping pace with a rapidly expanding market requires a fundamental overhaul of traditional testing methodologies. Organizations navigating the changing dynamics of the NVH testing market are finding that as the workforce thins due to retirement and staff reductions, they must adapt quickly. To remain competitive in this growing NVH testing market, development teams must adopt scalable technologies that maximize their existing capacity and safeguard institutional knowledge.

In the article “AI and Automation Unlock Smarter NVH Testing with Fewer Engineers,” written by ACS’ Randy Rozema, Director, Acoustics and Vibration, and Peter Schaldenbrand, Senior Application Engineer, Siemens Digital Industries Software, the focus is on how industry leaders must leverage advanced digital tools to overcome severe labor shortages. For engineering managers, the integration of AI and automation extends far beyond mere technical optimization; it is a critical strategy for survival and scalability. One of the most effective ways to combat the thinning talent pool is to deploy AI and automation directly into daily workflows to standardize repetitive tasks and run automated data quality checks. By embedding AI and automation into software-based test templates, smaller teams can drastically reduce manual overhead and process massive batches of data without requiring proportional increases in hands-on engineering time.

One of the most critical elements of this technological shift mentioned in the Design News article is addressing the unique acoustic demands introduced by the global transition to electrification, which has made comprehensive electric vehicle NVH testing a primary focus for manufacturers. The absence of traditional internal combustion engine noise exposes subtle sounds like motor whine and interior rattles, drastically increasing the complexity of electric vehicle NVH testing protocols. In addition, the higher frequency ranges and electromagnetic interference inherent to electric motors mean that successful electric vehicle NVH testing requires robust data acquisition hardware to gather clean data sets. To manage these expanded testing scopes without a larger workforce, organizations are turning to automated test rigs and digital twins to streamline the prototyping process.

To sustain this accelerated environment, advanced analytical capabilities are essential to prevent post-test data analysis from becoming a massive bottleneck. AI-trained algorithms are transforming time-intensive tasks like modal curve fitting, compressing analysis timelines from an entire week to a single day. Rather than sifting through gigabytes of spectrum data manually, junior engineers can utilize these intelligent tools to flag specific anomalies, while senior experts focus on high-level design decisions and critical interpretation. Ultimately, by combining human oversight with autonomous execution, leaders ensure that their testing programs achieve higher throughput and faster time-to-market, turning acoustic optimization into a decisive competitive advantage.

Read the full article here.