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We present an approach to model the dynamic of a copper flotation process. The conventional approach of system identification is applied to model the dynamics. In this research, experiments are performed to collect process data of determined input and output variables. It is followed by data pre-processing to handle outliers and to remove high frequency disturbances. Simulation and validation responses of linear estimated models, which captured the dynamic of the process, are presented. The long term goal is to use estimated models to design a models-based control system.

Virtual manufacturing has gained considerable importance in the last decade. To obtain reliable predictions in a virtual environment, the factors that influence the outcome of a manufacturing operation need to be carefully modeled and integrated in a simulation platform. The dynamic behavior of the Computer Numerical Control (CNC) system, which has a profound influence on the final part geometry and tolerance integrity, is among these factors. Classical CNC drive identification techniques are usually time consuming and need to be performed by an engineer qualified in dynamics and control theory. These techniques require the servo loop or the trajectory interpolator to be disconnected in order to inject the necessary identification signals, causing downtime to the machine. Hence, these techniques are usually not practical for constructing virtual models of existing CNC machine tools in a manufacturing environment. This thesis presents an alternative strategy for constructing virtual drive models with minimal intervention and downtime to the machinery. The proposed technique, named “rapid identification”, consists of executing a short G-code experiment and collecting input/output data using the motion capture feature available on most CNC controllers. The data is then processed to reverse engineer the equivalent tracking and disturbance transfer functions and friction characteristics of the machine. It is shown that virtual drive models constructed this way can be used to predict the real machine’s contouring performance for large class of drive systems, controlled with different control techniques. In the proposed scheme, the excitation is delivered by smoothly interpolated motion commands. Hence, convergence of parameters to their true values is not guaranteed. When the real system contains pole-zero cancellations, namely due to feedforward control action, this also results in a loss of identifiability. In order to guarantee the stability of the identified drive models, the pole locations are constrained with frequency and damping ratio limits. Hence, the rapid identification task is cast as a constrained minimization problem. Two solution strategies have been developed. In the first approach, Lagrange Multipliers (LM) technique is applied, which yields successful estimation results. However, implementation of LM is computationally intensive and requires the use of a dedicated symbolic solver. This limits the portability for industrial implementation. In the second approach, a Genetic Algorithm (GA) search technique is developed, which is a more practical but slightly approximate alternative. The GA allows parameter bounds to be incorporated in a natural manner and converges to 2-3% vicinity of the LM solution in one-tenth of the computation time. The GA solution can be easily ported to different computation platforms. Both LM and GA identification techniques were validated in simulations and experiments conducted on virtual and real machine tool drives. It is shown that although the parameters estimated using the rapid identification scheme do not always match their true values, the key tracking and disturbance rejection characteristics of the drives are successfully captured in the frequency range of the CNC motion commands. Therefore, the drive models constructed with rapid identification can be used to predict the contouring accuracy of real machine tools in a virtual process planning environment. This thesis presents an alternative strategy for constructing virtual drive models with minimal intervention and downtime to the machinery. The proposed technique, named “rapid identification”, consists of executing a short G-code experiment and collecting input/output data using the motion capture feature available on most CNC controllers. The data is then processed to reverse engineer the equivalent tracking and disturbance transfer functions and friction characteristics of the machine. It is shown that virtual drive models constructed this way can be used to predict the real machine’s contouring performance for large class of drive systems, controlled with different control techniques. In the proposed scheme, the excitation is delivered by smoothly interpolated motion commands. Hence, convergence of parameters to their true values is not guaranteed. When the real system contains pole-zero cancellations, namely due to feedforward control action, this also results in a loss of identifiability. In order to guarantee the stability of the identified drive models, the pole locations are constrained with frequency and damping ratio limits. Hence, the rapid identification task is cast as a constrained minimization problem. Two solution strategies have been developed. In the first approach, Lagrange Multipliers (LM) technique is applied, which yields successful estimation results. However, implementation of LM is computationally intensive and requires the use of a dedicated symbolic solver. This limits the portability for industrial implementation. In the second approach, a Genetic Algorithm (GA) search technique is developed, which is a more practical but slightly approximate alternative. The GA allows parameter bounds to be incorporated in a natural manner and converges to 2-3% vicinity of the LM solution in one-tenth of the computation time. The GA solution can be easily ported to different computation platforms. Both LM and GA identification techniques were validated in simulations and experiments conducted on virtual and real machine tool drives. It is shown that although the parameters estimated using the rapid identification scheme do not always match their true values, the key tracking and disturbance rejection characteristics of the drives are successfully captured in the frequency range of the CNC motion commands. Therefore, the drive models constructed with rapid identification can be used to predict the contouring accuracy of real machine tools in a virtual process planning environment.

An investigation of the relationship between corporate sponsorship activities and human resource constructs was conducted through an online questionnaire with employees of a southern U.S. energy provider. Specifically, three sponsorship-related constructs, fan identification with a sponsored sport property, employee involvement with the sponsorship, and employee attitude toward the sponsorship were hypothesized to be positively related to employee organization identification and job satisfaction. Social identification theory (SIT) provided the theoretical foundation of this study. Through a series of hypotheses, the three sponsorship-related constructs were hypothesized to exert both direct and indirect effects on employee organizational identification and job satisfaction. Testing the process of missing data for approximately 80 of the total 427 respondents showed that data was missing at random (MAR). Thus, missing data values were imputed using regression techniques available in AMOS 16.0 software. Structural equation modeling (SEM) was employed to evaluate the path of predicted relationships. Assessment of the measurement model fit for the entire model showed that all but one indicator, for involvement with the sponsorship, loaded on latent variables as expected. In addition to comparing the results of the SEM analysis of the imputed data set (n = 427) to the data set with only complete responses (n = 308), a random sample (n = 200) was also analyzed, in order to assess the impact of sample size on fitting the data to the models. A competing models approach to SEM analysis showed that four nested models differed only marginally on a couple goodness-of-fit indices. The principle of parsimony was thus utilized to select and evaluate the fit of the appropriate model. Evaluation of the hypotheses showed that fan identification and involvement with the sponsorship did not exert direct effects on employee organization identification and job satisfaction, but did influence these human resource constructs in an indirect manner. Additionally, an unpredicted, indirect relationship between organization prestige and job satisfaction was also established. Lastly, theoretical and managerial implications are discussed, along with the identification of several recommendations to guide future research relating corporate sponsorship with the internal audience.

Methods are presented for parameter identification of an annular gas seal on a flexiblerotor test rig. Dynamic loads are applied by magnetic bearings (MBs) that support the rotor. MB forces are measured using fiber-optic strain gauges that are bonded to the poles of the MBs. In addition to force and position measurements, a finite element (FE) rotor model is required for the identification algorithms. The FE rotor model matches free-free characteristics of the test rotor. The addition of smooth air seals to the system introduces stiffness and damping terms for identification that are representative of reaction forces in turbomachines. Tests are performed to experimentally determine seal stiffness and damping coefficients for different running speeds and preswirl conditions. Stiffness and damping coefficients are determined using a frequency domain identification method. This method uses an iterative approach to minimize error between theoretical and experimental transfer functions. Several time domain approaches are also considered; however, these approaches do not produce valid identification results. Stiffness coefficients are measured using static test results and an MB current and position based model. Test results produce seal coefficients with low uncertainties for the frequency domain identification method. Static test uncertainties are an order of magnitude larger, and time domain attempts fail to produce sealIn addition to the primary identification research, an investigation of the relationships between MB force, strain, and magnetic field is conducted. The magnetic field of an MB is modeled using commercial FE software. The magnetic field model is used to predict strain measurements for quasi-static test conditions. The strain predictions are compared with experimental strain measurements. Strain predictions agree with experimental measurements, although strain is typically over-predicted. coefficient measurements.

Rotor-bearing system characteristics, such as natural frequencies, mode shapes, stiffness and damping coefficients, are essential to diagnose and correct vibration problems during system operation. Of the above characteristics, accurate identification of bearing force parameters, i.e. stiffness and damping coefficients, is one of the most difficult to achieve. Field identification by imbalance response measurements is a simple and often reliable way to determine synchronous speed force coefficients. An enhanced method to estimate bearing support force coefficients in flexible rotor-bearing systems is detailed. The estimation is carried out from measurements obtained near bearing locations from two linearly independent imbalance tests. An earlier approach assumed rotordynamic measurements at the bearing locations, which is very difficult to realize in practice. The enhanced method relaxes this constraint and develops the procedure to estimate bearing coefficients from measurements near the bearing locations. An application of the method is presented for a test rotor mounted on two-lobe hydrodynamic bearings. Imbalance response measurements for various imbalance magnitudes are obtained near bearing locations and also at rotor mid-span. At shaft speeds around the bending critical speed, the displacements at the rotor mid-span are an order of magnitude larger than the shaft displacements at the bearing locations. The enhanced identification procedure renders satisfactory force coefficients in the rotational speed range between 1,000 rpm and 4,000 rpm. The amount of imbalance mass needed to conduct the tests and to obtain reliable shaft displacement measurements influences slightly the magnitude of the identified force coefficients. The effect of increasing the number of rotor sub-elements in the finite-element modeling of the shaft is noted. Sensitivity of the method and derived parameters to noise in the measurements is also quantified.

Thesis (Ph. D.)--Drexel University, 2004. / Includes abstract and vita. Includes bibliographical references (leaves 170-176).

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb. 19, 2008). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dr. Luis Occeña, Thesis Supervisor. Includes bibliographical references.

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences