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INTEGRATED SERVOMECHANISM AND PROCESS CONTROL FOR MACHINING PROCESSES
Title
INTEGRATED SERVOMECHANISM AND PROCESS CONTROL FOR MACHINING PROCESSES.
Creator
Tang, Yan, Xu, Chengying, University of Central Florida
Abstract / Description
In this research, the integration of the servomechanism control and process control for machining processes has been studied. As enabling strategies for next generation quality control, process monitoring and open architecture machine tools will be implemented on production floor. This trend brings a new method to implement control algorithm in machining processes. Instead of using separate modules for servomechanism control and process control individually, the integrated controller is...
Show moreIn this research, the integration of the servomechanism control and process control for machining processes has been studied. As enabling strategies for next generation quality control, process monitoring and open architecture machine tools will be implemented on production floor. This trend brings a new method to implement control algorithm in machining processes. Instead of using separate modules for servomechanism control and process control individually, the integrated controller is proposed in this research to simultaneously achieve goals in servomechanism level and the process level. This research is motivated by the benefits brought by the integration of servomechanism control and process control. Firstly, the integration simplifies the control system design. Secondly, the integration promotes the adoption of process control on production floor. Thirdly, the integration facilitates portability between machine tools. Finally, the integration provides convenience for both the servomechanism and process simulation in virtual machine tool environment. The servomechanism control proposed in this research is based on error space approach. This approach is suitable for motion control for complex contour. When implement the integration of servomechanism control and process control, two kinds of processes may be encountered. One is the process whose model parameters can be aggregated with the servomechanism states and the tool path does not need real time offset. The other is the process which does not have direct relationship with the servomechanism states and tool path may need to be modified real time during machining. The integration strategies applied in error space are proposed for each case. Different integration strategies would propagate the process control goal into the motion control scheme such that the integrated control can simultaneously achieve goals of both the servomechanism and the process levels. Integrated force-contour-position control in turning is used as one example in which the process parameters can be aggregated with the servomechanism states. In this case, the process level aims to minimize cutting force variation while the servomechanism level is to achieve zero contour error. Both force variation and contour error can be represented by the servomechanism states. Then, the integrated control design is formulated as a linear quadratic regulator (LQR) problem in error space. Force variation and contour error are treated as part of performance index to be minimized in the LQR problem. On the other hand, the controller designed by LQR in error space can guarantee the asymptotic tracking stability of the servomechanism for complex contour. Therefore, the integrated controller can implement the process control and the servomechanism control simultaneously. Cutter deflection compensation for helical end milling processes is used as one example in which the process cannot be directly associated with the servomechanism states. Cutter deflection compensation requires real-time tool path offset to reduce the surface error due to cutter deflection. Therefore, real time interpolation is required to provide reference trajectory for the servomechanism controller. With the real time information about surface error, the servomechanism controller can not only implement motion control for contour requirement, but also compensation for the dimensional error caused by cutter deflection. In other words, the real time interpolator along with the servomechanism controller can achieve the goals of both the servomechanism and process level. In this study, the cutter deflection in helical end milling processes is analyzed first to illustrate the indirect relationship between cutter deflection and surface accuracy. Cutter deflection is examined for three kinds of surfaces including straight surface, circular surface, and curved surface. The simulation-based deflection analysis will be used to emulate measurement from sensors and update the real-time interpolator to offset tool path. The controller designed through pole placement in error space can guarantee the robust tracking performance of the updated reference trajectory combining both contour and tool path offset required for deflection compensation. A variety of cutting conditions are simulated to demonstrate the compensation results. In summary, the process control is integrated with the servomechanism control through either direct servomechanism controller design without tool path modification or servomechanism control with real time interpolation responding to process variation. Therefore, the process control can be implemented as a module within machine tools. Such integration will enhance the penetration of process control on production floor to increase machining productivity and product quality.
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Date Issued
2009
Identifier
CFE0002758, ucf:48116
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002758
Modeling and Contour Control of Multi-Axis Linear Driven Machine Tools
Title
Modeling and Contour Control of Multi-Axis Linear Driven Machine Tools.
Creator
Zhao, Ran, Lin, Kuo-Chi, Xu, Chengying, Bai, Yuanli, Das, Tuhin, An, Linan, University of Central Florida
Abstract / Description
In modern manufacturing industries, many applications require precision motion control of multi-agent systems, like multi-joint robot arms and multi-axis machine tools. Cutter (end effector) should stay as close as possible to the reference trajectory to ensure the quality of the final products. In conventional computer numerical control (CNC), the control unit of each axis is independently designed to achieve the best individual tracking performance. However, this becomes less effective when...
Show moreIn modern manufacturing industries, many applications require precision motion control of multi-agent systems, like multi-joint robot arms and multi-axis machine tools. Cutter (end effector) should stay as close as possible to the reference trajectory to ensure the quality of the final products. In conventional computer numerical control (CNC), the control unit of each axis is independently designed to achieve the best individual tracking performance. However, this becomes less effective when dealing with multi-axis contour following tasks because of the lack of coordination among axes. This dissertation studies the control of multi-axis machine tools with focus on reducing the contour error. The proposed research explicitly addresses the minimization of contour error and treats the multi-axis machine tool as a multi-input-multi-output (MIMO) system instead of several decoupled single-input-single-output (SISO) systems. New control schemes are developed to achieve superior contour following performance even in the presence of disturbances. This study also extends the applications of the proposed control system from plane contours to regular contours in R3. The effectiveness of the developed control systems is experimentally verified on a micro milling machine.
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Date Issued
2014
Identifier
CFE0005287, ucf:50552
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005287
Navigation of an Autonomous Differential Drive Robot for Field Scouting in Semi-structured Environments
Title
Navigation of an Autonomous Differential Drive Robot for Field Scouting in Semi-structured Environments.
Creator
Freese, Douglas, Xu, Yunjun, Lin, Kuo-Chi, Kauffman, Jeffrey L., Behal, Aman, University of Central Florida
Abstract / Description
In recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary...
Show moreIn recent years, the interests of introducing autonomous robots by growers into agriculture fields are rejuvenated due to the ever-increasing labor cost and the recent declining numbers of seasonal workers. The utilization of customized, autonomous agricultural robots has a profound impact on future orchard operations by providing low cost, meticulous inspection. Different sensors have been proven proficient in agrarian navigation including the likes of GPS, inertial, magnetic, rotary encoding, time of flight as well as vision. To compensate for anticipated disturbances, variances and constraints contingent to the outdoor semi-structured environment, a differential style drive vehicle will be implemented as an easily controllable system to conduct tasks such as imaging and sampling.In order to verify the motion control of a robot, custom-designed for strawberry fields, the task is separated into multiple phases to manage the over-bed and cross-bed operation needs. In particular, during the cross-bed segment an elevated strawberry bed will provide distance references utilized in a logic filter and tuned PID algorithm for safe and efficient travel. Due to the significant sources of uncertainty such as wheel slip and the vehicle model, nonlinear robust controllers are designed for the cross-bed motion, purely relying on vision feedback. A simple image filter algorithm was developed for strawberry row detection, in which pixels corresponding to the bed center will be tracked while the vehicle is in controlled motion. This incorporated derivation and formulation of a bounded uncertainty parameter that will be employed in the nonlinear control. Simulation of the entire system was subsequently completed to ensure the control capability before successful validation in multiple commercial farms. It is anticipated that with the developed algorithms the authentication of fully autonomous robotic systems functioning in agricultural crops will provide heightened efficiency of needed costly services; scouting, disease detection, collection, and distribution.
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Date Issued
2018
Identifier
CFE0007401, ucf:52743
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007401
Virtual resistance based DC-link voltage regulation for Microgrid DG inverters.
Title
Virtual resistance based DC-link voltage regulation for Microgrid DG inverters.
Creator
Shinde, Siddhesh, Batarseh, Issa, Mikhael, Wasfy, Kutkut, Nasser, University of Central Florida
Abstract / Description
This research addresses the practical issues faced by Microgrid Distributed Generation (DG) inverters when operated in islanded mode. A Microgrid (MG) is an interconnection of domestic distributed loads and low voltage distributed energy sources such as micro-turbine, wind-turbine, PVs and storage devices. These energy sources are power limited in nature and constrain the operation of DG inverters to which they are coupled. DG inverters operated in islanded mode should maintain the power...
Show moreThis research addresses the practical issues faced by Microgrid Distributed Generation (DG) inverters when operated in islanded mode. A Microgrid (MG) is an interconnection of domestic distributed loads and low voltage distributed energy sources such as micro-turbine, wind-turbine, PVs and storage devices. These energy sources are power limited in nature and constrain the operation of DG inverters to which they are coupled. DG inverters operated in islanded mode should maintain the power balance between generation and demand. If DG inverter operating in islanded mode drains its source power below a certain limit or if it is incapable of supplying demanded power due to its hardware rating, it turns on its safety mechanism and isolates itself from the MG. This, in turn, increases the power demand on the rest of the DG units and can have a catastrophic impact on the viability of the entire system. This research presents a Virtual Resistance based DC Link Voltage Regulation technique which will allow DG inverters to continue to source their available power even when the power demand by the load is higher than their capacity without shutting off and isolating from the MG.
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Date Issued
2016
Identifier
CFE0006503, ucf:51403
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006503
Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications
Title
Cost-Sensitive Learning-based Methods for Imbalanced Classification Problems with Applications.
Creator
Razzaghi, Talayeh, Xanthopoulos, Petros, Karwowski, Waldemar, Pazour, Jennifer, Mikusinski, Piotr, University of Central Florida
Abstract / Description
Analysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive...
Show moreAnalysis and predictive modeling of massive datasets is an extremely significant problem that arises in many practical applications. The task of predictive modeling becomes even more challenging when data are imperfect or uncertain. The real data are frequently affected by outliers, uncertain labels, and uneven distribution of classes (imbalanced data). Such uncertainties createbias and make predictive modeling an even more difficult task. In the present work, we introduce a cost-sensitive learning method (CSL) to deal with the classification of imperfect data. Typically, most traditional approaches for classification demonstrate poor performance in an environment with imperfect data. We propose the use of CSL with Support Vector Machine, which is a well-known data mining algorithm. The results reveal that the proposed algorithm produces more accurate classifiers and is more robust with respect to imperfect data. Furthermore, we explore the best performance measures to tackle imperfect data along with addressing real problems in quality control and business analytics.
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Date Issued
2014
Identifier
CFE0005542, ucf:50298
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005542
Mahalanobis kernel-based support vector data description for detection of large shifts in mean vector
Title
Mahalanobis kernel-based support vector data description for detection of large shifts in mean vector.
Creator
Nguyen, Vu, Maboudou, Edgard, Nickerson, David, Schott, James, University of Central Florida
Abstract / Description
Statistical process control (SPC) applies the science of statistics to various process control in order to provide higher-quality products and better services. The K chart is one among the many important tools that SPC offers. Creation of the K chart is based on Support Vector Data Description (SVDD), a popular data classifier method inspired by Support Vector Machine (SVM). As any methods associated with SVM, SVDD benefits from a wide variety of choices of kernel, which determines the...
Show moreStatistical process control (SPC) applies the science of statistics to various process control in order to provide higher-quality products and better services. The K chart is one among the many important tools that SPC offers. Creation of the K chart is based on Support Vector Data Description (SVDD), a popular data classifier method inspired by Support Vector Machine (SVM). As any methods associated with SVM, SVDD benefits from a wide variety of choices of kernel, which determines the effectiveness of the whole model. Among the most popular choices is the Euclidean distance-based Gaussian kernel, which enables SVDD to obtain a flexible data description, thus enhances its overall predictive capability. This thesis explores an even more robust approach by incorporating the Mahalanobis distance-based kernel (hereinafter referred to as Mahalanobis kernel) to SVDD and compare it with SVDD using the traditional Gaussian kernel. Method's sensitivity is benchmarked by Average Run Lengths obtained from multiple Monte Carlo simulations. Data of such simulations are generated from multivariate normal, multivariate Student's (t), and multivariate gamma populations using R, a popular software environment for statistical computing. One case study is also discussed using a real data set received from Halberg Chronobiology Center. Compared to Gaussian kernel, Mahalanobis kernel makes SVDD and thus the K chart significantly more sensitive to shifts in mean vector, and also in covariance matrix.
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Date Issued
2015
Identifier
CFE0005676, ucf:50170
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005676
Analysis of Remote Tripping Command Injection Attacks in Industrial Control Systems Through Statistical and Machine Learning Methods
Title
Analysis of Remote Tripping Command Injection Attacks in Industrial Control Systems Through Statistical and Machine Learning Methods.
Creator
Timm, Charles, Caulkins, Bruce, Wiegand, Rudolf, Lathrop, Scott, University of Central Florida
Abstract / Description
In the past decade, cyber operations have been increasingly utilized to further policy goals of state-sponsored actors to shift the balance of politics and power on a global scale. One of the ways this has been evidenced is through the exploitation of electric grids via cyber means. A remote tripping command injection attack is one of the types of attacks that could have devastating effects on the North American power grid. To better understand these attacks and create detection axioms to...
Show moreIn the past decade, cyber operations have been increasingly utilized to further policy goals of state-sponsored actors to shift the balance of politics and power on a global scale. One of the ways this has been evidenced is through the exploitation of electric grids via cyber means. A remote tripping command injection attack is one of the types of attacks that could have devastating effects on the North American power grid. To better understand these attacks and create detection axioms to both quickly identify and mitigate the effects of a remote tripping command injection attack, a dataset comprised of 128 variables (primarily synchrophasor measurements) was analyzed via statistical methods and machine learning algorithms in RStudio and WEKA software respectively. While statistical methods were not successful due to the non-linearity and complexity of the dataset, machine learning algorithms surpassed accuracy metrics established in previous research given a simplified dataset of the specified attack and normal operational data. This research allows future cybersecurity researchers to better understand remote tripping command injection attacks in comparison to normal operational conditions. Further, an incorporation of the analysis has the potential to increase detection and thus mitigate risk to the North American power grid in future work.
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Date Issued
2018
Identifier
CFE0007257, ucf:52193
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007257
An Unsupervised Consensus Control Chart Pattern Recognition Framework
Title
An Unsupervised Consensus Control Chart Pattern Recognition Framework.
Creator
Haghtalab, Siavash, Xanthopoulos, Petros, Pazour, Jennifer, Rabelo, Luis, University of Central Florida
Abstract / Description
Early identification and detection of abnormal time series patterns is vital for a number of manufacturing.Slide shifts and alterations of time series patterns might be indicative of some anomalyin the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less...
Show moreEarly identification and detection of abnormal time series patterns is vital for a number of manufacturing.Slide shifts and alterations of time series patterns might be indicative of some anomalyin the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem. Despite the profound advantageof unsupervised methodology for less manual data labeling their use is limited due to thefact that their performance is not robust enough for practical purposes. In this study we propose the use of a consensus clustering framework. Computational results show robust behavior compared to individual clustering algorithms.
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Date Issued
2014
Identifier
CFE0005178, ucf:50670
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005178
Modeling and Simulation of All-electric Aircraft Power Generation and Actuation
Title
Modeling and Simulation of All-electric Aircraft Power Generation and Actuation.
Creator
Woodburn, David, Wu, Xinzhang, Batarseh, Issa, Georgiopoulos, Michael, Haralambous, Michael, Chow, Louis, University of Central Florida
Abstract / Description
Modern aircraft, military and commercial, rely extensively on hydraulic systems. However, there is great interest in the avionics community to replace hydraulic systems with electric systems. There are physical challenges to replacing hydraulic actuators with electromechanical actuators (EMAs), especially for flight control surface actuation. These include dynamic heat generation and power management.Simulation is seen as a powerful tool in making the transition to all-electric aircraft by...
Show moreModern aircraft, military and commercial, rely extensively on hydraulic systems. However, there is great interest in the avionics community to replace hydraulic systems with electric systems. There are physical challenges to replacing hydraulic actuators with electromechanical actuators (EMAs), especially for flight control surface actuation. These include dynamic heat generation and power management.Simulation is seen as a powerful tool in making the transition to all-electric aircraft by predicting the dynamic heat generated and the power flow in the EMA. Chapter 2 of this dissertation describes the nonlinear, lumped-element, integrated modeling of a permanent magnet (PM) motor used in an EMA. This model is capable of representing transient dynamics of an EMA, mechanically, electrically, and thermally.Inductance is a primary parameter that links the electrical and mechanical domains and, therefore, is of critical importance to the modeling of the whole EMA. In the dynamic mode of operation of an EMA, the inductances are quite nonlinear. Chapter 3 details the careful analysis of the inductances from finite element software and the mathematical modeling of these inductances for use in the overall EMA model.Chapter 4 covers the design and verification of a nonlinear, transient simulation model of a two-step synchronous generator with three-phase rectifiers. Simulation results are shown.
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Date Issued
2013
Identifier
CFE0005074, ucf:49975
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005074