(Dr. Charles Ume, advisor)
"Implementation of Fiber Phased Array Ultrasound Generation System and Signal Analysis for Weld Penetration Control"
Welding is a primary technique that is used to join structural components together. When optimum weld penetration depth is not achieved, the welded components must be scraped or fixed. A fully automated robotic welding process, equipped with on-line sensors that are able to monitor and control the depth of weld penetration in real-time, would result in increased productivity and significant cost savings.
The overall purpose of this research is to develop a real-time ultrasound based system for controlling robotic weld quality by monitoring the weld pool. The concept of real-time weld quality control is quite broad and this work is narrowed down to weld penetration depth monitoring and control with laser ultrasonics. The weld penetration depth is one of the most important geometric parameters that define the weld quality, hence remains a key control quantity. This research focuses on the implementation and optimization of a fiber phased array generation unit and the development of signal analysis algorithms to extract the weld penetration depth information from ultrasonic signals. The developed system is based on using a fiber phased array to generate ultrasound, and an Electro-Magnetic Acoustic Transducer (EMAT) as a receiver. The ultrasound generated by the fiber phased array propagates through the weld pool and is picked up by the EMAT. The ultrasound Time of Flight (ToF) is measured to derive the weld penetration depth. A transient finite element (FE) model is developed to predict the temperature distribution during the gas metal arc welding (GMAW) process. An analytical model is developed to study the propagation of ultrasound in a temperature gradient during real-time welding and the curved path is numerically traced. The cross-correlation technique has been applied to estimate the ToF of the ultrasound. The analytical relationship between the ToF and the weld penetration depth, obtained by ray-tracing algorithm and geometric analysis, matches the experimentally estimated ToF very well.
The developed real-time weld depth sensing technique is efficient and can readily
be deployed for commercial applications. It will remove the major obstacle to
a fully automated robotic welding process. An on-line welding monitoring and
control system will facilitate mass production characterized by consistency,
high quality, and low costs. Such a system will increase the precision of the
welding process, resulting in quality control of the weld beads. Moreover, in-process
control will relieve human operators of tedious, repetitive, and hazardous welding
tasks, thus reducing welding-related injures.