Dawn White

Developing intelligent control for spray forming processes

A program to implement real-time sensing and control of spray-formed preform conditions is underway at the David Taylor Research Center. The objective of the program is to develop sensor and control technologies that can be used to monitor critical process conditions and modify operational parameters during the spray forming of components. This task has been divided into two phases. The first entails developing sensors and controls to monitor and correct simulated process conditions. In the second phase, the selected sensors and controls will be combined with actuators, thereby integrating research center equipment and enabling the production of nonsymmetric preforms.

Process modeling with neural networks for pulsed GMAW braze welds

In this work, an improved weld-bead geometry and reduced sheet metal distortion were desired in a weld-brazing process used to finish exterior sheet metal prior to painting. Numerical modeling techniques are widely used to model welding processes, but accurate methods for modeling pulsed-welding processes are lacking. Instead, a continuous empirical model of the welding process was developed using a statistically designed experiment and a neural network for data analysis. Graphical methods were used to simulate the process, and robust parameter design techniques were used to analyze response surfaces produced by the model. Using the response surfaces, possible welding procedures were generated and tested. A new process that resulted in improved joint quality was implemented in production.