Modeling Fracture in Z-Pinned Composite Co-Cured Laminates Using Smeared Properties and Cohesive Elements in DYNA3D

The purpose of the present research was three-fold: 1) gain a more sophisticated understanding of the response of co-cured composite joints with and without through-thickness reinforcement (TTR), 2) compare the behavior of specimens reinforced with various sizes and densities of reinforcement, and 3) use experimental data to verify the existing DYNA3D smeared property model. Double cantilever beam, end-notch flexure and T-section specimens reinforced with 0.011 diameter z-pins at 2% and 4% volume densities were tested to determine the mode I, mode II and mixed mode (I and II) behavior. Results were added to preliminary research in which tests were conducted on previously mentioned specimen geometries reinforced with 0.022 diameter z-pins at similar densities. Experiments were modeled in DYNA3D using shell and cohesive elements. The energy release rate, G, determined through a curve fit developed from beam theory, was smeared across the region of reinforcement treating it as a separate material. The research validated Z-pinning as an effective means of improving the fracture toughness of polymer matrix laminated composites in mode I and mixed mode loading conditions and determined that the existing model works well in simulating the behavior in mode I tests

Modeling Reliability Growth in Accelerated Stress Testing

Qualitative accelerated test methods improve system reliability by identifying and removing initial design flaws. However, schedule and cost constraints often preclude sufficient testing to generate a meaningful reliability estimate from the data obtained in these tests. In this dissertation a modified accelerated life test is proposed to assess the likelihood of attaining a reliability requirement based on tests of early system prototypes. Assuming each prototype contains an unknown number of independent competing failure modes whose respective times to occurrence are governed by a distinct Weibull law, the observed failure data from this qualitative test are shown to follow a poly-Weibull distribution. However, using an agent-based Monte Carlo simulation, it is shown that for typical products subjected to qualitative testing, the failure observations result from a homogenous subset of the total number of latent failure modes and the failure data can be adequately modeled with a Weibull distribution. Thus, the projected system reliability after implementing corrective action to remove one or more failure modes can be estimated using established quantitative accelerated test data analysis methods. Our results suggest that a significant cost and time savings may be realized using the proposed method to signal the need to reassess a product’s design or reallocate test resources to avoid unnecessary maintenance or redesigns. Further, the proposed approach allows a significant reduction in the test time and sample size required to estimate the risk of meeting a reliability requirement over current quantitative accelerated life test techniques. Additional contributions include a numerical and analytical procedure for obtaining the maximum likelihood parameter estimates and observed Fisher information matrix components for the generalized poly-Weibull distribution. Using this procedure, we show that the poly-Weibull distribution outperforms the best-fit modified Weibull alternatives in the literature with respect to their fit of reference data sets for which the hazard rate functions are non-monotone

Impact of Prognostic Uncertainty in System Health Monitoring

Across many industries, systems are exceeding their intended design lives, whether they are ships, bridges or military aircraft. As a result failure rates can increase and unanticipated wear or failure conditions can arise. Health monitoring research and application has the potential to more safely lengthen the service life of a range of systems through utilization of sensor data and knowledge of failure mechanisms to predict component life remaining. A further benefit of health monitoring when combined across an entire platform is system health management. System health management is an enabler of condition based maintenance, which allows repair or replacement based on material condition, not a set time. Replacement of components based on condition can enable cost savings through fewer parts being used and the associated maintenance costs. The goal of this research is to show the management of system health can provide savings in maintenance and logistics cost while increasing vehicle availability through the approach of condition based maintenance. This work examines the impact of prediction accuracy uncertainty in remaining useful life prognostics for a squadron of 12 aircraft. The uncertainty in this research is introduced in the system through an uncertainty factor applied to the useful life prediction. An ARENA discrete event simulation is utilized to explore the effect of prediction error on availability, reliability, and maintenance and logistics processes. Aircraft are processed through preflight, flight, and post-flight operations, as well as maintenance and logistics activities. A baseline case with traditional time driven maintenance is performed for comparison to the condition based maintenance approach of this research. This research does not consider cost or decision making processes, instead focusing on utilization parameters of both aircraft and manpower. The occurrence and impact of false alarms on system performance is examined. The results show the potential availability, reliability, and maintenance benefits of a health monitoring system and explore the diagnostic uncertainty

Final Report of the AFIT Quality Initiative External Discovery Committee

This report summarizes the findings of the Air Force Institute of Technology’s (AFIT’s) Quality Initiative - External Discovery Team. The overarching purpose of the Quality Initiative is to create a detailed, executable investment strategy for modernizing AFIT’s instructional capabilities across five thrust areas. These activities were completed over the course of one year, beginning in June of 2016 and concluding in June of 2017. The data gathered were evaluated and several recommendations for further review were decided upon by the External Discovery Team. The following report briefly covers those recommendations and provides sources from which the recommendations were gleaned. These recommendations are meant to serve as a baseline for ways in which AFIT could begin to program resources to help improve teaching and instruction across the institution as a whole. The data presented here are meant to serve as a compliment to the Internal Discovery Team’s report that focuses on data and feedback gathered from institutions internal to AFIT