Matching Items (94)
Description
Nowadays product reliability becomes the top concern of the manufacturers and customers always prefer the products with good performances under long period. In order to estimate the lifetime of the product, accelerated life testing (ALT) is introduced because most of the products can last years even decades. Much research has been done in the ALT area and optimal design for ALT is a major topic. This dissertation consists of three main studies. First, a methodology of finding optimal design for ALT with right censoring and interval censoring have been developed and it employs the proportional hazard (PH) model and generalized linear model (GLM) to simplify the computational process. A sensitivity study is also given to show the effects brought by parameters to the designs. Second, an extended version of I-optimal design for ALT is discussed and then a dual-objective design criterion is defined and showed with several examples. Also in order to evaluate different candidate designs, several graphical tools are developed. Finally, when there are more than one models available, different model checking designs are discussed.
ContributorsYang, Tao (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Borror, Connie (Committee member) / Rigdon, Steve (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nonregular screening designs can be an economical alternative to traditional resolution IV 2^(k-p) fractional factorials. Recently 16-run nonregular designs, referred to as no-confounding designs, were introduced in the literature. These designs have the property that no pair of main effect (ME) and two-factor interaction (2FI) estimates are completely confounded. In this dissertation, orthogonal arrays were evaluated with many popular design-ranking criteria in order to identify optimal 20-run and 24-run no-confounding designs. Monte Carlo simulation was used to empirically assess the model fitting effectiveness of the recommended no-confounding designs. The results of the simulation demonstrated that these new designs, particularly the 24-run designs, are successful at detecting active effects over 95% of the time given sufficient model effect sparsity. The final chapter presents a screening design selection methodology, based on decision trees, to aid in the selection of a screening design from a list of published options. The methodology determines which of a candidate set of screening designs has the lowest expected experimental cost.
ContributorsStone, Brian (Author) / Montgomery, Douglas C. (Thesis advisor) / Silvestrini, Rachel T. (Committee member) / Fowler, John W (Committee member) / Borror, Connie M. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Ingestion of high temperature mainstream gas into the rotor-stator cavities of a gas turbine is one of the major problems faced by the turbine designers. The ingested gas heats up rotor disks and induces higher thermal stresses on them, giving rise to durability concern. Ingestion is usually reduced by installing seals on the rotor and stator rims and by purging the disk cavity by secondary air bled from the compressor discharge. The geometry of the rim seals and the secondary air flow rate, together, influence the amount of gas that gets ingested into the cavities. Since the amount of secondary air bled off has a negative effect on the gas turbine thermal efficiency, one goal is to use the least possible amount of secondary air. This requires a good understanding of the flow and ingestion fields within a disk cavity. In the present study, the mainstream gas ingestion phenomenon has been experimentally studied in a model single-stage axial flow gas turbine. The turbine stage featured vanes and blades, and rim seals on both the rotor and stator. Additionally, the disk cavity contained a labyrinth seal radially inboard which effectively divided the cavity into a rim cavity and an inner cavity. Time-average static pressure measurements were obtained at various radial positions within the disk cavity, and in the mainstream gas path at three axial locations at the outer shroud spread circumferentially over two vane pitches. The time-average static pressure in the main gas path exhibited a periodic asymmetry following the vane pitch whose amplitude diminished with increasing distance from the vane trailing edge. The static pressure distribution increased with the secondary air flow rate within the inner cavity but was found to be almost independent of it in the rim cavity. Tracer gas (CO2) concentration measurements were conducted to determine the sealing effectiveness of the rim seals against main gas ingestion. For the rim cavity, the sealing effectiveness increased with the secondary air flow rate. Within the inner cavity however, this trend reversed -this may have been due to the presence of rotating low-pressure flow structures inboard of the labyrinth seal.
ContributorsThiagarajan, Jayanth kumar (Author) / Roy, Ramendra P (Thesis advisor) / Lee, Taewoo (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation explores different methodologies for combining two popular design paradigms in the field of computer experiments. Space-filling designs are commonly used in order to ensure that there is good coverage of the design space, but they may not result in good properties when it comes to model fitting. Optimal designs traditionally perform very well in terms of model fitting, particularly when a polynomial is intended, but can result in problematic replication in the case of insignificant factors. By bringing these two design types together, positive properties of each can be retained while mitigating potential weaknesses. Hybrid space-filling designs, generated as Latin hypercubes augmented with I-optimal points, are compared to designs of each contributing component. A second design type called a bridge design is also evaluated, which further integrates the disparate design types. Bridge designs are the result of a Latin hypercube undergoing coordinate exchange to reach constrained D-optimality, ensuring that there is zero replication of factors in any one-dimensional projection. Lastly, bridge designs were augmented with I-optimal points with two goals in mind. Augmentation with candidate points generated assuming the same underlying analysis model serves to reduce the prediction variance without greatly compromising the space-filling property of the design, while augmentation with candidate points generated assuming a different underlying analysis model can greatly reduce the impact of model misspecification during the design phase. Each of these composite designs are compared to pure space-filling and optimal designs. They typically out-perform pure space-filling designs in terms of prediction variance and alphabetic efficiency, while maintaining comparability with pure optimal designs at small sample size. This justifies them as excellent candidates for initial experimentation.
ContributorsKennedy, Kathryn (Author) / Montgomery, Douglas C. (Thesis advisor) / Johnson, Rachel T. (Thesis advisor) / Fowler, John W (Committee member) / Borror, Connie M. (Committee member) / Arizona State University (Publisher)
Created2013
Description
During the initial stages of experimentation, there are usually a large number of factors to be investigated. Fractional factorial (2^(k-p)) designs are particularly useful during this initial phase of experimental work. These experiments often referred to as screening experiments help reduce the large number of factors to a smaller set. The 16 run regular fractional factorial designs for six, seven and eight factors are in common usage. These designs allow clear estimation of all main effects when the three-factor and higher order interactions are negligible, but all two-factor interactions are aliased with each other making estimation of these effects problematic without additional runs. Alternatively, certain nonregular designs called no-confounding (NC) designs by Jones and Montgomery (Jones & Montgomery, Alternatives to resolution IV screening designs in 16 runs, 2010) partially confound the main effects with the two-factor interactions but do not completely confound any two-factor interactions with each other. The NC designs are useful for independently estimating main effects and two-factor interactions without additional runs. While several methods have been suggested for the analysis of data from nonregular designs, stepwise regression is familiar to practitioners, available in commercial software, and is widely used in practice. Given that an NC design has been run, the performance of stepwise regression for model selection is unknown. In this dissertation I present a comprehensive simulation study evaluating stepwise regression for analyzing both regular fractional factorial and NC designs. Next, the projection properties of the six, seven and eight factor NC designs are studied. Studying the projection properties of these designs allows the development of analysis methods to analyze these designs. Lastly the designs and projection properties of 9 to 14 factor NC designs onto three and four factors are presented. Certain recommendations are made on analysis methods for these designs as well.
ContributorsShinde, Shilpa (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Committee member) / Fowler, John (Committee member) / Jones, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
Shock loading is a complex phenomenon that can lead to failure mechanisms such as strain localization, void nucleation and growth, and eventually spall fracture. Studying incipient stages of spall damage is of paramount importance to accurately determine initiation sites in the material microstructure where damage will nucleate and grow and to formulate continuum models that account for the variability of the damage process due to microstructural heterogeneity. The length scale of damage with respect to that of the surrounding microstructure has proven to be a key aspect in determining sites of failure initiation. Correlations have been found between the damage sites and the surrounding microstructure to determine the preferred sites of spall damage, since it tends to localize at and around the regions of intrinsic defects such as grain boundaries and triple points. However, considerable amount of work still has to be done in this regard to determine the physics driving the damage at these intrinsic weak sites in the microstructure. The main focus of this research work is to understand the physical mechanisms behind the damage localization at these preferred sites. A crystal plasticity constitutive model is implemented with different damage criteria to study the effects of stress concentration and strain localization at the grain boundaries. A cohesive zone modeling technique is used to include the intrinsic strength of the grain boundaries in the simulations. The constitutive model is verified using single elements tests, calibrated using single crystal impact experiments and validated using bicrystal and multicrystal impact experiments. The results indicate that strain localization is the predominant driving force for damage initiation and evolution. The microstructural effects on theses damage sites are studied to attribute the extent of damage to microstructural features such as grain orientation, misorientation, Taylor factor and the grain boundary planes. The finite element simulations show good correlation with the experimental results and can be used as the preliminary step in developing accurate probabilistic models for damage nucleation.
ContributorsKrishnan, Kapil (Author) / Peralta, Pedro (Thesis advisor) / Mignolet, Marc (Committee member) / Sieradzki, Karl (Committee member) / Jiang, Hanqing (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2013
Description
A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the error standard deviation. A comprehensive comparison of the proposed method and the existing KMW-Shewhart method for monitoring linear profiles is conducted. In addition, the effect that the number of observations within a sample has on the performance of the proposed method is investigated. The proposed method was also compared to the T^2 method discussed in Kang and Albin (2000) for multivariate, polynomial, and nonlinear profiles. A simulation study shows that overall the proposed P-value method performs satisfactorily for different profile types.
ContributorsAdibi, Azadeh (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Li, Jing (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
As robots are increasingly migrating out of factories and research laboratories and into our everyday lives, they should move and act in environments designed for humans. For this reason, the need of anthropomorphic movements is of utmost importance. The objective of this thesis is to solve the inverse kinematics problem of redundant robot arms that results to anthropomorphic configurations. The swivel angle of the elbow was used as a human arm motion parameter for the robot arm to mimic. The swivel angle is defined as the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Using kinematic data recorded from human subjects during every-day life tasks, the linear sensorimotor transformation model was validated and used to estimate the swivel angle, given the desired end-effector position. Defining the desired swivel angle simplifies the kinematic redundancy of the robot arm. The proposed method was tested with an anthropomorphic redundant robot arm and the computed motion profiles were compared to the ones of the human subjects. This thesis shows that the method computes anthropomorphic configurations for the robot arm, even if the robot arm has different link lengths than the human arm and starts its motion at random configurations.
ContributorsWang, Yuting (Author) / Artemiadis, Panagiotis (Thesis advisor) / Mignolet, Marc (Committee member) / Santos, Veronica J (Committee member) / Arizona State University (Publisher)
Created2013
Description
Accelerated life testing (ALT) is the process of subjecting a product to stress conditions (temperatures, voltage, pressure etc.) in excess of its normal operating levels to accelerate failures. Product failure typically results from multiple stresses acting on it simultaneously. Multi-stress factor ALTs are challenging as they increase the number of experiments due to the stress factor-level combinations resulting from the increased number of factors. Chapter 2 provides an approach for designing ALT plans with multiple stresses utilizing Latin hypercube designs that reduces the simulation cost without loss of statistical efficiency. A comparison to full grid and large-sample approximation methods illustrates the approach computational cost gain and flexibility in determining optimal stress settings with less assumptions and more intuitive unit allocations.
Implicit in the design criteria of current ALT designs is the assumption that the form of the acceleration model is correct. This is unrealistic assumption in many real-world problems. Chapter 3 provides an approach for ALT optimum design for model discrimination. We utilize the Hellinger distance measure between predictive distributions. The optimal ALT plan at three stress levels was determined and its performance was compared to good compromise plan, best traditional plan and well-known 4:2:1 compromise test plans. In the case of linear versus quadratic ALT models, the proposed method increased the test plan's ability to distinguish among competing models and provided better guidance as to which model is appropriate for the experiment.
Chapter 4 extends the approach of Chapter 3 to ALT sequential model discrimination. An initial experiment is conducted to provide maximum possible information with respect to model discrimination. The follow-on experiment is planned by leveraging the most current information to allow for Bayesian model comparison through posterior model probability ratios. Results showed that performance of plan is adversely impacted by the amount of censoring in the data, in the case of linear vs. quadratic model form at three levels of constant stress, sequential testing can improve model recovery rate by approximately 8% when data is complete, but no apparent advantage in adopting sequential testing was found in the case of right-censored data when censoring is in excess of a certain amount.
Implicit in the design criteria of current ALT designs is the assumption that the form of the acceleration model is correct. This is unrealistic assumption in many real-world problems. Chapter 3 provides an approach for ALT optimum design for model discrimination. We utilize the Hellinger distance measure between predictive distributions. The optimal ALT plan at three stress levels was determined and its performance was compared to good compromise plan, best traditional plan and well-known 4:2:1 compromise test plans. In the case of linear versus quadratic ALT models, the proposed method increased the test plan's ability to distinguish among competing models and provided better guidance as to which model is appropriate for the experiment.
Chapter 4 extends the approach of Chapter 3 to ALT sequential model discrimination. An initial experiment is conducted to provide maximum possible information with respect to model discrimination. The follow-on experiment is planned by leveraging the most current information to allow for Bayesian model comparison through posterior model probability ratios. Results showed that performance of plan is adversely impacted by the amount of censoring in the data, in the case of linear vs. quadratic model form at three levels of constant stress, sequential testing can improve model recovery rate by approximately 8% when data is complete, but no apparent advantage in adopting sequential testing was found in the case of right-censored data when censoring is in excess of a certain amount.
ContributorsNasir, Ehab (Author) / Pan, Rong (Thesis advisor) / Runger, George C. (Committee member) / Gel, Esma (Committee member) / Kao, Ming-Hung (Committee member) / Montgomery, Douglas C. (Committee member) / Arizona State University (Publisher)
Created2014
Description
In the three phases of the engineering design process (conceptual design, embodiment design and detailed design), traditional reliability information is scarce. However, there are different sources of information that provide reliability inputs while designing a new product. This research considered these sources to be further analyzed: reliability information from similar existing products denominated as parents, elicited experts' opinions, initial testing and the customer voice for creating design requirements. These sources were integrated with three novels approaches to produce reliability insights in the engineering design process, all under the Design for Reliability (DFR) philosophy. Firstly, an enhanced parenting process to assess reliability was presented. Using reliability information from parents it was possible to create a failure structure (parent matrix) to be compared against the new product. Then, expert opinions were elicited to provide the effects of the new design changes (parent factor). Combining those two elements resulted in a reliability assessment in early design process. Extending this approach into the conceptual design phase, a methodology was created to obtain a graphical reliability insight of a new product's concept. The approach can be summarized by three sequential steps: functional analysis, cognitive maps and Bayesian networks. These tools integrated the available information, created a graphical representation of the concept and provided quantitative reliability assessments. Lastly, to optimize resources when product testing is viable (e.g., detailed design) a type of accelerated life testing was recommended: the accelerated degradation tests. The potential for robust design engineering for this type of test was exploited. Then, robust design was achieved by setting the design factors at some levels such that the impact of stress factor variation on the degradation rate can be minimized. Finally, to validate the proposed approaches and methods, different case studies were presented.
ContributorsMejia Sanchez, Luis (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Jesus R (Committee member) / See, Tung-King (Committee member) / Arizona State University (Publisher)
Created2014