1. Automation of Engineering Design Aids using Neural Networks Siripong Malasri and Jittapong Malasri Christian Brothers University Kriangsiri Malasri Georgia Tech MAESC ’05 – May 13, 2005

2. Presentation Overview Introduction Artificial Neural Networks The Stress Concentration Problem Software Development Data preparation Network training and validation Standalone application development Conclusions and Future Work

3. Introduction Traditional design aids Look-up tables Graphical plots Shortcomings Inaccurate interpolation/extrapolation Difficult to smoothly integrate with computer applications

4. Neural Networks Have been used to recognize patterns and project trends in data Backpropagation model – can be trained to generate desired input-output relationships

5. Stress Concentration (1) Objective Calculate the peak stress in a notched beam cross-section subject to a bending moment Possible approaches Finite-element analysis Experimental procedures Determine a stress concentration factor from a design aid

6. Stress Concentration (2) Stress concentration factor, C Function of the ratios a/h 2 and h 1 /h 2 Peak stress at notch: M = bending moment applied I = cross-sectional moment of inertia c = distance from N.A.

7. Software – Data Preparation Training data obtained from a published graphical design aid Inputs: a/h 2, h 1 /h 2 Output: C 46 training pairs, 15 calibration pairs, 15 validation pairs

8. Software – Network Training NeuroShell 2 software Backpropagation network with 2 input neurons, 8 hidden neurons, and 1 output neuron Excellent results from trained network

9. Software – Standalone Program Interface developed in Visual Basic Network code generated from NeuroShell 2

10. Conclusions and Future Work Excellent network estimates of the stress concentration factor for this particular application Standalone executable is portable to any Windows computer Future work: comprehensive stress analysis program with a variety of cross-sections