Final thoughts

The past few months, we’ve worked hard and today we’ve handed in our thesis. One of the output results was a paper about this blog, which we share with all of our readers now.

We hope you’ve enjoyed reading and commenting on our posts the past few months, we tried our best to find interesting topics to write about, bring them to you in an entertaining way.

Thanks for reading!

Jeroen & Nico

link: Our Statement Paper


Computers doing it for you.

Our last blog is an odd tangent of some sorts as it isn’t really about Non Destructive Testing, although it’s something I really wanted to include in our blog series: Computer Vision.
In a way it is related, everything is being automated and N.D.T. is no exception to this rule.

If you’re a reader from our academic institute (and studying electro mechanics), you’re currently receiving lessons and lab sessions about vision systems. In my own opinion these lessons only touch the subject very superficially on a fundamental soft-/hardware level and don’t provide a true reflection of what’s currently possible.

Within industry you might immediately think of quality control, checking for visual defects on cookies or if all bottles have a bottle cap. On the other side Hollywood might have you think anything is possible with AI and vision systems, like Iron Man’s “Jarvis” or what the terminator is capable of. So, what is the state of the union within vision systems today?

t2%20%2816%29From our own experience; it’s not all that easy and straightforward as you might think. We’ve been messing around with basic object recognition and boy, that stuff is all very, very young and in full development. If you want to make a complex vision system today, prepare to deal with a lot of scattered open source projects and basic binary kits because there’s no true easy and straightforward package out there, yet.


Last year Google (who else) made some huge strides forward in object recognition which they showcased at “ImageNet’s visual recognition challenge”.   By using something they call a “neural network” they can now swiftly and accurately recognize random (unknown) objects within various scenes. Which is pretty amazing since most vision systems out there today require you to “teach” them the specific object to look for first. Even then scaling, rotation and lighting can mess everything up. So in some sorts they’ve achieved the basic object recognition skills of a 3-year old for AI systems, which might sound a little low brow but certainly isn’t! You can read about the competition and what it really implies here: . With the prospect of self-driving cars you kind of expect them to be experts in vision systems anyway right?

The video below is a TED talk that discusses this neural network a little more in detail. Basically they fed some supercomputer a HUGE load of labeled image data from which it can now determine what it sees in an image. Of course object recognition is only a small part of what vision systems truly hold within, discussing everything would require a blog of its own. But observing that this is another leap towards a ‘seeing’ AI that in the future can truly understand and see relations is a pretty amazing thing.
Paraphrased From the talk:

Like the little child the computer doesn’t just say cat and bed but says it’s a cat laying on a bed

So back to NDT: if QC is fully automated and you can’t even see if the AI was wrong after the check, would you still trust it?

Cheating aging

In my previous post I talked about using NDT methods to discover new things in old paintings. During my research about that, I came across something that is almost the opposite of this: making something new older by using NDT methods. Scientists have discovered that it’s possible to age wine at an accelerated rate using ultrasonic waves. Making wine has been around for centuries, without much changes to the basic process, letting grapes and water ferment so the sugars turn into alcohol. This process takes a while, so a large part of winemaking was storing your liquids for an appropriate time in the right containers.

Wine Barrels
Wine aging the traditional way

In the last decade however, scientists shook things up by accelerating this process. Applying 20 kHz ultrasonic waves can induce cavitation in the wine: small bubbles that implode and create very localized high temperature and pressure. This simulates the aging of wine. Biggest difference is that this method of aging can be finished in less than 10 minutes.

Wine aging the non-traditional way
Wine aging the non-traditional way

This seems like a great new innovation, making wine easier and faster, increasing productivity,…  but there is of course a catch: the results aren’t always predictable. Using the same ingredients, same production methods, 1 bottle can turn out great while a different bottle is worthless. In my opinion, this method makes wine less special. Winemaking is some sort of art, requiring knowledge  about which sorts of barrels to use to store the wine, how long the wine should rest before being bottled,… making the critical part of winemaking something that can be achieved in minutes instead of months/years opens the doors for everyone to start producing wine, with no way of telling whether the bottle of wine you just bought was made with love and patience or in just a few minutes with cold, hard science. Do you think science can be a threat to traditional ways that have been around for ages? And would that be a bad thing? Sources:

True invisibility.

As mentioned in my last post, we’re not seeing invisibility cloaks any time soon. But at Rochester University (USA), they have been working on devices that truly allow you to ignore stuff and directly look at what lies behind.

Most methods of making things invisible rely on bending the light around the object you wish to obscure, like the negative refraction discussed previously that required complicated and expensive meta-materials. This method also bends light, but just uses cheap, off she shelf optic lenses you can get anywhere.

The image below shows their cheapest and simplest design. The concept here is that light is bent and forced to pass trough a fine focal area in the middle, ignoring all that lies around it. It can be scaled up or down to whatever size is desired, but is only applicable to “dough-nut shapes”, they do mention to have developed methods that obscure objects entirely without this requirement but these are more complicated.

2014-03-07-howell-cloaking-200-crop-630x228This method, according to them, is extraordinary as it is the first device to provide such extended cloaking for visual light.

From a continuous range of viewing angles, the hand remains cloaked, and the grids seen through the device match the background on the wall (about 2 m away), in color, spacing, shifts, and magnification.

Here’s the link to the full scientific paper: Paper

One can quickly see this method is applicable on two levels, either to hide something you don’t want to see, or to particularly see something that would normally be blocked. I can see this being used as a new category of N.D.T. in the future.  While researching the topic i found someone who proposed it to be used for surgeons, to block out their own hands and tools so they can better see the organs they’re operating on. Sounds like an amazing idea to me !

What to you think? Is it a viable tool? Or is the featured four lens system too complicated to ever be used anywhere?

Here’s the full article from the university itself: Link

Searching for the lost da Vinci

A mural painted by Leonardo da Vinci that is about the width of 3 last suppers next to each other, on a location that is well known, the tower of Sienna, and yet, the painting you can see there, is not one from da Vinci. How is that possible? That’s what kept engineer Maurizio Seracini awake at night for several decades.

It was believed the fresco was lost forever because since after da Vinci created the mural, the room it was in, was renovated by another artist (Vasari). There were however accounts that Vasari didn’t destroy da Vinci’s painting, but constructed a wall in front of it and left the masterpiece of Leonardo intact.
Using a wide range of non destructive testing methods (and 1 destructive test) Seracini was able to prove the masterpiece is still in place, but of course, it is still inaccessible to the public, because they didn’t want to destroy a 400 year old work of art, the lay bare a different, unfinished piece of art.

This was just 1 example of how using modern NDT methods like infrared, X-rays,… can help discovering new details or even better, new paintings.

In the blog post and accompanying TED-talk, Seracini explains how he and his team have helped create a better understanding of some of the worlds most famous paintings, as this striking example


This painting, called Lady with the unicorn, seems to depict a noblewoman, holding a unicorn in her hands. Using CT-scans however, as seen on the right part of the image, we can see that the lady is actually holding a puppy. Digging even deeper into the painting it was discovered that the original artist (Raphael) didn’t draw or paint a unicorn nor a puppy, he just painted the lady and the addition of a puppy and unicorn was done by a different artist, years or even decades later.

I find it extremely fascinating that using these techniques, we are able to reveal details, unseen for centuries, or even discover things that were never intended by the original artist. I would call this to the early stages of Photoshop, people editing paintings as they see fit.

For more interesting case studies on what details were revealed in this and a range of other paintings, check out this TED-talk or the blog post that goes with it.