February 23, 2023    
1:00 pm - 2:30 pm


Bookings closed


Essex Hall Room 122
401 Sunset Avenue, Windsor, Ontario, N9B 3P4
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The School of Computer Science is pleased to present…


Real-Time In-Process Ultrasonic M-Scan Segmentation Using Deep Learning for Adaptive Resistance Spot Welding


MSc Thesis Proposal by: Vlad Tusinean


Date: Thursday February 23rd, 2023

Time: 1:00 pm – 2:30 pm

Location: Essex Hall, Room 122


  1. Two-part attendance mandatory (sign-in sheet, QR Code)
  2. Arrive 5-10 minutes prior to the event starting – LATECOMERS WILL NOT BE ADMITTED. Note that due to demand, if the room has reached capacity, even if you are “early” admission is not guaranteed.
  3. Please be respectful of the presenter by NOT knocking on the door for admittance once the door has been closed whether the presentation has begun or not (If the room is at capacity, overflow is not permitted (ie. sitting on floors) as this is a violation of the Fire Safety code).
  4. Be respectful of the decision of the advisor/host of the event if you are not given admittance. The School of Computer Science has numerous events occurring in the near future.



Advances in ultrasonic imaging techniques allow for the real-time non-destructive evaluation (NDE) of resistance spot welds (RSW) as they progress. In real-time RSW ultrasonic NDE data analysis, the most important features to characterize are the existence and positions of key interfaces – the top and bottom of the welded stack, and the top and bottom of the molten nugget – which allow for the estimation of resultant weld nugget size, position, and penetration into the welded stack. Deep learning has established the state of the art for many tasks in computer vision and natural language processing, and consequently it has seen increased use for related tasks in NDE (e.g., feature extraction, signal processing, sequence processing, etc.).

The objective of this proposed work is to develop an AI system that characterizes ultrasonic RSW NDE data in real-time such that it can be used to provide closed-loop feedback to a weld controller in an adaptive welding system. Preliminary work shows the feasibility of a UNet-based convolutional LSTM to conduct semantic segmentation of ultrasonic data. The resultant image masks from segmentation allow for the calculation of total nugget penetration and the estimation of lateral nugget shape. These automated measurements can be fed back to a weld controller to allow weld schedule adaptation, moving automotive manufacturing another step closer towards the ultimate goal of zero-defect manufacturing.


Keywords: Deep Learning, Semantic Segmentation, Convolutional Long Short-Term Memory, Non-destructive Evaluation, Ultrasound


MSc Thesis Committee:

Internal Reader: Dr. Jianguo Lu

External Reader: Dr. Steven Rehse

Advisors: Dr. Roman Maev and Dr. Robin Gras



This event is fully booked.

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