My Master’s Thesis in 3 Minutes – 3MT Competition

Conducting quality research on any field is hard, however not as hard as explaining the research to a room full of people with non-technical background, that too in just 3 minutes! As a person who is never afraid of taking challenges, I couldn’t afford to lose the opportunity to participate in the prestigious 3-Minute Thesis Competition and put myself to one of the most difficult tests I had been taken by far. The biggest challenge was not to present my thesis in front of such a large audience from all the different disciplines, but was to present it in such a palatable manner that my audience could comprehend and enjoy the flavour of my work. Then there was this challenge of compressing my entire thesis journey of almost two years to fit in just 3 minutes.  However, when there is a challenge or test, there is the chance to shine, and come out as a gladiator. My motivation to participate in this competition was to achieve both. During my graduate research assistantship in IDIR, I have been pushed to the front frequently to explain the workings of my AI models to our industrial clients in laymen term but without making them lose interest half-way. To be honest, I have found it more difficult as a task than to present my work to people from the same or similar fields. No matter how satisfied our clients say they are, I always felt that I could do better, that I am not shining as bright as I am capable of in this job, until my performance in the 3-Minute Thesis Competition.

I spent almost a week to come up with a crisp and solid script, which would include a little humour, a little twist and all the highlights of my thesis and contribution being made in past two years. After doing lots of brainstorming of ideas, and practicing the presentation for quite a few times, I was ready for the battle. Although, I was not in the top three winners, I was one of the top five presenters out of all 16 participants. I also received huge appreciation from my peers and general audience for my successful attempt to share such a highly technical work with people from different academic background in such comprehensive and coherent way.

After that competition, I really felt that I have shined in my own way. More importantly, I have gained huge confidence in myself that I can be a compelling public speaker too where necessary. I would recommend anyone who is reading this to consider taking part in such competitions whenever you feel like you are not good enough at something. Give it your best shot and you will be amazed by your own capabilities! Let the inner gladiator in you wake up and stand out in the crowd.

Now, if you have 3 more minutes, I request you to spend them on learning about my master’s thesis. I will really appreciate any kind of feedback or suggestion from you in this regard.

Please check my 3-Minute Thesis Competition video recording and the video transcript below.

Original Thesis: Real-time Automated Weld Quality Analysis from Ultrasonic B-scan using Deep Learning

3-MT Competition Title: Weld Quality Control Using Artificial Intelligence

100 years back when Henry Ford first made cheap, reliable cars, people said, “But, what’s wrong with a horse?” But with time, as cars became more affordable, they became not just essential but also the reflections of ourselves, our emotions and pride. But how many of us know that this reflection of ours is made of over 30,000 unique parts? Yes it’s a miracle that cars don’t break down more often! And how many of us know that on average, a brand new car is built every 16 seconds. In the time it takes you to put milk in a cup of tea, a whole car rolls off a production line somewhere in the world.

 

One of the essential jobs in car manufacturing is spot welding. It is the process of joining two or more metal parts by melting them on a small spot through heat. Most industries use spot welding robots for large scale car manufacturing. However, the quality inspection of the welded parts is still manual. In fact, in industries they take one sample from a batch of welded parts and tears it off to check if they are properly welded. But this process is destructive and expensive. Not only that, it allows many weld samples pass unnoticed. A better non-destructive alternative is to capture the whole welding process as ultrasonic image like the one behind me and monitor them during production. However, just imagine how tiresome this will be for a person to keep staring at thousands of boring images like this for hours after hours. Thankfully, we have machines who are willing to do all our mundane tasks without any complain.

 

This is where my research comes into play. I have trained an AI algorithm which can identify patterns visible in the weld image and decide just like a human operator whether the sample should be passed or welded again. During training, each time the algorithm gives a decision, it gets a feedback in return on how close its decision is to the operators decision. Then in the next trial it makes some numerical adjustments in the algorithm by itself and tries to give better decision. This trial and error continues until it starts to act just like human. After practicing this on thousands of images, it is now ready to give correct decision on quality of new unseen weld samples in the production within just 200 milliseconds.

 

The impact of my research is huge. Previously robots were blind, now they can see how well they are welding. It also saves human from another repetitive no-brainer job and give them more time to spend on more interesting tasks. More importantly, it will let industries save lots of time and money and make cars safer, more durable and more affordable. A prototype of my developed algorithm is on test under the production facility in one of the top car manufacturers in the world and so far they are quite satisfied with the performance.

 

Now I am very excited about the outcome of my research, not because of the reasons I just mentioned but because my work has the potential to allow more and more people express themselves through their cars. How excited are you feeling right now?

Thank you very much!