Why Traditional Shock & Vibe Testing Still Fails – And What Google's Open‑Source Fix Means for the Rest of Us

I spent three years shipping electronic assemblies with what I thought was bulletproof packaging. We padded them with foam, reinforced the corners, ran standard ISTA drop tests – everything by the book. Then a $200K server arrived at a customer site with a hairline fracture that didn't cause a failure until six months later. By then, the root cause was buried under a dozen other variables, and the company ate the warranty cost without ever knowing why.

That experience sent me down a rabbit hole that eventually led to Ken Leung's talk at ISTA 2026. Ken leads shock and vibration testing at Google – 15 years of shipping racks, servers, and switches across the globe. And what he’s doing is quietly dismantling 50 years of testing dogma.

The Real Problem: We've Been Measuring the Wrong Thing

The industry standard – accelerometers on a shaker table plus a drop test – hasn't changed since the 1970s. Same profiles, same pass/fail criteria. The problem is that an accelerometer tells you what’s happening on the box, not what’s happening inside the product. Ken puts it bluntly: "Most people don't really know physically what's happening to the real product."

That hit home for me. I remember a project where we doubled the foam cushioning to protect a heavy heat sink. Turns out, the soft foam made the heat sink wobble like a bobblehead – actually increasing the damage. We made it worse by adding more material, exactly the opposite of what we intended.

Ken’s point is that without seeing the internal dynamics, you're essentially guessing. The accelerometer gives you pages of spectral data, but you still don't know where the failure originated – the solder joint? The mounting bracket? The interface between two metals? And which direction the stress came from?

What Google Found – and Open-Sourced

Ken's team started pointing high‑speed cameras at products during transport simulations. Not just one camera – multiple angles, synchronized, capturing thousands of frames per second. Then they applied computer vision and 3D measurement to track every component inside the product.

The result is a video record that anybody with a basic engineering degree can watch and immediately agree on what broke and why. No more guessing from spectral plots. As Ken described it, you can see "was it a drop? Was it a vibe? And how did it break?"

Two or three seconds of testing can produce terabytes of data – but the insight is condensed into a visual story. And then they went a step further: Ken is open‑sourcing all of it on GitHub. The camera setups, the analysis algorithms, even the raw data from thousands of tests. The project shows up when you search for "Google open source vibration project".

This matters because one lab’s test profile in Asia won't match the same vehicle vibration in North America. Real‑world conditions are incredibly complex. An open playbook lets everyone – from a student to a CPG packaging engineer – build on the same body of evidence rather than reinventing the wheel each time.

The Hidden Cost of "Good Enough" Testing

The question I kept asking myself was: how much money are we collectively losing to incomplete testing?

Consider a datacenter server that’s supposed to last five years but fails at three. The failure happens long after the product development team has moved on – nobody knows why, and tracking it down costs millions in teardown and root‑cause analysis. Or consider an AI training cluster that costs millions of dollars in power alone – a single mid‑training crash can force a complete restart, wasting weeks and millions.

Ken made a point that stuck with me: "It's better that it doesn't work when you plug it in, than it fails three years later." Immediate failure is easy to diagnose. Latent failure is a time bomb that most companies never connect back to packaging.

Where This Applies Beyond Datacenter Hardware

You might think this is only relevant for $100K server racks. But material stress is universal. Whether you're shipping a medical device with delicate optics or a consumer electronics product with hundreds of solder joints, the physics are the same. The tools Ken is building – high‑speed imaging plus AI analysis – give you the ability to see how forces propagate through the structure, not just at the box boundary.

For the CPG world, especially high‑value or fragile products (think electronics, glass bottles with tight tolerances, complex assemblies), this approach could transform how we design packaging. Instead of adding more dunnage and hoping it works, you can see what the product actually experiences and optimize from there.

What I've Changed in My Own Work

After Ken’s presentation, I revisited our own test protocols. We now run a high‑speed camera on at least one sample per product family – even a modest setup costs less than a single warranty claim. The footage has already caught two issues that our standard accelerometer tests missed: a capacitor resonance that only appears at a specific vibration frequency, and a cable that chafed against a sharp edge during random vibration.

Neither would have shown up in a pass/fail drop test. Both would have caused field failures within the first year.

The open‑source GitHub project gave us a starting point for the camera setup and analysis scripts. Ken's team has documented the brand and price of the cameras they use, so we didn't have to start from scratch.

The Bigger Lesson: Stop Testing the Box, Start Testing the Product

For 50 years, packaging engineers have treated the package as the system. Ken is pushing us to treat the product inside as the system, and the package as just one variable. That shift in perspective requires new tools – but the tools are now free and accessible.

I'm not saying every popcorn kettle or cereal box needs a camera rig. But if you're shipping anything with tight tolerances, high value, or long expected life – and especially if you've had unexplained field failures – it's worth looking inside. The answer has probably been there all along. We just weren't watching.