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This paper presents a computational investigation of the validity of eddy current testing (ECT) for defects embedded in steel using parametrically designed defects. Of particular focus is the depths at which defects can be detected through ECT. Building on this we characterize interior defects by parametrically describing them and then examining the response fields through measurement. Thereby we seek to establish the depth and direction of detectable cracks. As a second step, we match measurements from eddy current excitations to computed fields through finite element optimization. This develops further our previously presented methods of defect characterization. Here rough contours of synthesized shapes are avoided by a novel scheme of averaging neighbor heights rather than using complex Bézier curves, constraints and such like. This avoids the jagged shapes corresponding to mathematically correct but unrealistic synthesized shapes in design and nondestructive evaluation.

This paper presents an unambiguous and intuitive method for identification of steering column resonant (SCR) mode of vibration. One simple but overlooked technique to determine the SCR mode in-vehicle is to provide local stiffnesses of the body locations where the Instrument Panel (IP) attaches, to the IP suppliers to be used in their design and development. This paper describes how this technique is useful in predicting the first few important in-vehicle steering column modes for different classes of vehicles, with examples presented in each class. The results obtained from such analyses are compared against those from direct in-buck simulations. This technique is not limited to its application in developing IP systems, but can easily be extended to include other systems such as seats, fuel tanks, etc. Also it is shown that a design optimization analysis may be performed using these attachment stiffnesses as design variables resulting in a system level solution.

The ability to quickly design new vehicles with optimal crashworthiness has long been a goal of automotive manufacturers and Tier 1 suppliers alike. This paper takes steps towards that goal by automating manual design iterations. The crashworthiness of an instrument panel was optimized using LS-OPT. In one design experiment, optimizing the gauges of non-styled parts in the instrument panel reduced the simulated force in a Bendix test setup by around 30%. In a second design experiment, optimizing the shape of non-styled parts in the instrument panel with a parametric preprocessor enhanced the simulated crashworthiness by around 20%. In a third design experiment, the design space was increased and an additional 7% improvement in simulated crashworthiness was found. The designs were generated several times faster and were less expensive to evaluate than with previous manual methods.

A myriad of topology optimization tools exist today in the market that use automated under-the-hood structural simulations. All the user needs is to provide is the current shape of the part, or the maximum space that the part is allowed to occupy, and the maximum loads that it will experience. Though this technology has existed for over 25 years, recent advances in Additive Manufacturing (AM) have now enabled fabrication of hitherto-infeasible parts, both quickly and inexpensively. A quick cursory literature search on successful implementation of topology optimization reveals that a majority of the attention has been focused on structural components and assemblies subjected to known service load(s) [1,2,3]. Therein lies one of the disadvantages experienced in the state-of-the-art today, especially for the military industry.