Electrostatic Discharge (ESD) is a unique issue in the electronics industry that can cause failures of electrical components and complete electronic systems. There is an entire industry that is focused on developing ESD compliant tooling using traditional manufacturing methods. This research work evaluates the feasibility to fabricate a PEEK-Carbon Nanotube composite filament for Fused Filament Fabrication (FFF) Additive Manufacturing that is ESD compliant. In addition, it demonstrates that the FFF process can be used to print tools with the required accuracy, ESD compliance and mechanical properties necessary for the electronics industry at a low rate production level. Current Additive Manufacturing technology can print high temperature polymers, such as PEEK, with the required mechanical properties but they are not ESD compliant and require post processing to create a product that is. There has been some research conducted using mixed multi-wall and single wall carbon nanotubes in a PEEK polymers, which improves mechanical properties while reducing bulk resistance to the levels required to be ESD compliant. This previous research has been used to develop a PEEK-CNT polymer matrix for the Fused Filament Fabrication additive manufacturing process

Cooperative education has a long-standing tradition within engineering education. As part of the experiential education field, it carries many success stories. Several universities offer a robust cooperative education track. In recent years, Arizona State University has made the decision to formalize a cooperative education program. Arizona State University, like many other institutions, has long since provided career support and promoted internships as an excellent work experience option before graduation. The decision to formalize a cooperative education program speaks to a need for a more rigorous path to work experience for engineering students. This paper is an investigation into the barriers and enablers behind a young cooperative education program. These results indicate that while students do benefit from the program, growth of the program may be tied to creating a meaningful distinction between cooperative education and other learning opportunities.

Foveal sensors employ a small region of high acuity (the foveal region) surrounded by a periphery of lesser acuity. Consequently, the output map that describes their sensory acuity is nonlinear, rendering the vast corpus of linear system theory inapplicable immediately to the state estimation of a target being tracked by such a sensor. This thesis treats the adaptation of the Kalman filter, an iterative optimal estimator for linear-Gaussian dynamical systems, to enable its application to the nonlinear problem of foveal sensing. Results of simulations conducted to evaluate the effectiveness of this algorithm in tracking a target are presented, culminating in successful tracking for motion in two dimensions.

Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.