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Real-time automated analysis of weld quality from ultrasonic b-scan using deep learning

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  • Tue, 10/23/2018 - 11:00am - 1:00pm

Real-time automated analysis of weld quality from ultrasonic b-scan using deep learning

MSc Thesis Proposal by:

Zarreen Naowal Reza

Date:  Tuesday, October 23rd, 2018
Time:  11: 00 am – 1: 00 pm
Location: 3105, Lambton Tower

Abstract: Resistance spot welding is a widely used process for joining metals using electrically generated heat or Joule heating. It is one of the most commonly used techniques in automotive industry to weld sheet metals to form a car body. Although, industrial robots are used as automated spot welders in massive scale in the industries, the weld quality inspection process still requires human involvement to decide if a weld should be passed as acceptable or not. Not only it is a tedious and error-prone job, but also it costs industries lots of time and money. Therefore, making this process automated and real-time will have high significance in spot welding as well as the field of Non-destructive Testing (NDT). Research team in IDIR (Institute of Diagnostic Imaging and Research) have built techniques to obtain ultrasonic b-scans in order to visualize the welding development as greyscale 2D image. They have proved that by extracting and interpreting relevant patterns from these b-scans, weld quality can be determined correctly. Current works combining conventional image processing techniques are unable to extract these patterns from a wide variety of weld shapes with production-level satisfaction. Therefore, in this thesis, we propose to apply SSD, a single-shot detection deep convolutional neural network for real-time embedded detection of components of cross-sectional weld shape from ultrasonic b-scans and interpret them to numeric parameters which are used as features to classify welds as good or bad in real-time.

Thesis Committee:
Internal Reader: Dr. Boubakeur Boufama
External Reader: Dr. Jonathan Wu
Advisors: Dr. Dan Wu and Dr. Roman Maev

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