Reference trajectory modification based on spatial iterative learning for contour control of 2-axis NC systems

Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This paper presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories

Flexible adaptation of iterative learning control with applications to synthetic bone graft manufacturing

Additive manufacturing processes are powerful tools; they are capable of fabricating structures without expensive structure specific tooling -- therefore structure designs can efficiently change from run-to-run -- and they can integrate multiple distinct materials into a single structure. This work investigates one such additive manufacturing process, micro-Robotic Deposition ($\mu$RD), and its utility in fabricating advanced architecture synthetic bone grafts. These bone grafts, also known as synthetic bone scaffolds, are highly porous three-dimensional structures that provide a matrix to support the natural process of bone remodeling. Ideally, the synthetic scaffold will stimulate complete bone healing in a skeletal defect site and also resorb with time so that only natural tissue remains. The objective of this research is to develop methods to integrate different regions with different porous microstructures into a single scaffold; there is evidence that scaffolds with designed regions of specific microstructures can be used to elicit a strong and directed bone ingrowth response that improves bone ingrowth rate and quality. The key contribution of this work is the development of a control algorithm that precisely places different build materials in specified locations, thereby the fabrication of advanced architecture scaffolds is feasible. Under previous control methods, designs were relegated to be composed of a single material. The control algorithm developed in this work is an adaptation of Iterative Learning Control (ILC), a control method that is typically best suited for mass manufacturing applications. This adaptation reorients the ILC framework such that it is more amenable to additive manufacturing systems, such as $\mu$RD. Control efficacy is demonstrated by the fabrication of advanced architecture scaffolds. Scaffolds with contoured forms, multiple domains with distinct porous microstructures, and hollow cavities are feasible when the developed controller is used in conjunction with a novel manufacturing workflow in which scaffolds are filled within patterned molds that support overhanging features. An additional application demonstrates controller performance on the robot positioning problem; this work has implications for additive manufacturing in general

Five-axis contour error control based on spatial iterative learning

In this paper, a contour error control strategy based on spatial iterative learning control (sILC) is developed for repetitive processing of five-axis computer numerical control (CNC) machine tools. A curve approximation method with an adaptive moving window is developed to achieve accurate tool position and orientation contour error estimation, and a five-axis contour error control strategy based on sILC is proposed. The compensation method is derived using an sILC algorithm to modify the geometric reference path instead of modifying the controller. The experimental results show that the proposed control strategy reduces contour errors of five-axis CNC machine tools, and it outperforms the traditional tracking error control

Modeling and Contour Control of Multi-Axis Linear Driven Machine Tools

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 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

Hierarchical control of complex manufacturing processes

The need for changing the control objective during the process has been reported in many systems in manufacturing, robotics, etc. However, not many works have been devoted to systematically investigating the proper strategies for these types of problems. In this dissertation, two approaches to such problems have been suggested for fast varying systems. The first approach, addresses problems where some of the objectives are statically related to the states of the systems. Hierarchical Optimal Control was proposed to simplify the nonlinearity caused by adding the statically related objectives into control problem. The proposed method was implemented for contour-position control of motion systems as well as force-position control of end milling processes. It was shown for a motion control system, when contour tracking is important, the controller can reduce the contour error even when the axial control signals are saturating. Also, for end milling processes it was shown that during machining sharp edges where, excessive cutting forces can cause tool breakage, by using the proposed controller, force can be bounded without sacrificing the position tracking performance. The second approach that was proposed (Hierarchical Model Predictive Control), addressed the problems where all the objectives are dynamically related. In this method neural network approximation methods were used to convert a nonlinear optimization problem into an explicit form which is feasible for real time implementation. This method was implemented for force-velocity control of ram based freeform extrusion fabrication of ceramics. Excellent extrusion results were achieved with the proposed method showing excellent performance for different changes in control objective during the process --Abstract, page iv

Contour error compensation based on feed rate adjustment

To improve the performance of computer numerical control (CNC) machining, especially for large-curvature trajectories, this paper presents a contour error compensation algorithm based on reference trajectory modification. In order to estimate the contour error accurately and efficiently, a contour error estimation model is established. The reference trajectory is modified on the basis of the estimated contour error and partitioned into different segments, which adopt different feed rates according to a corner detection algorithm. The effectiveness of this contour error compensation algorithm is verified by experiments on a CNC machine tool

Dynamic Model Identification and Trajectory Correction for Virtual Process Planning in Multi-Axis Machine Tools

In today’s industry, the capability to effectively reduce production time and cost gives a manufacturer a vital advantage against its competitors. Specifically, in the machining industry, the ability to simulate the dynamic performance of machine tools, and the physics of cutting processes, is critical to taking corrective actions, achieving process and productivity improvements, thereby enhancing competitiveness. In this context, being able to estimate mathematical models which describe the dynamic response of machine tools to commanded tool trajectories and external disturbance forces plays a key role in establishing virtual and intelligent manufacturing capability. These models can also be used in virtual simulations for process improvement, such as compensating for dynamic positioning errors by making small corrections to the commanded trajectory. This, in turn, can facilitate further productivity improvement and part quality in multi-axis manufacturing operations, such as machining. This thesis presents new methods for identifying the positioning response and friction characteristics of machine tool servo drives in a nonintrusive manner, and an approach for enhancing dynamic positioning accuracy through commanded trajectory correction via Iterative Learning Control (ILC). As the first contribution, the linear transfer functions correlating the positioning response to the commanded trajectory and friction disturbance inputs are identified using a new pole search method in conjunction with least squares (LS) projection. It is validated that this approach can work with in-process collected data, and demonstrates superior convergence and numerical characteristics, and model prediction accuracy, compared to an earlier ‘rapid identification’ approach based on the application of classical Least Squares for the full model. Effectiveness of the new method is demonstrated in simulations, and in experimental case studies for planar motion on two different machine tools, a gear grinding machine and a 5-axis machining center. Compared to the earlier approach, which could predict servo errors with 10-68% closeness, the new method improves the prediction accuracy to 0.5-2%. In the simulation of feed drives used in multi-axis machines, high fidelity prediction of the nonlinear stick-slip friction plays an important role. Specifically, time-dependent (i.e., dynamic) friction models help to improve the accuracy of virtual predictions. While many elaborate models have been proposed for this purpose, such as the generalized Maxwell-slip (GMS) model, their parameters can be numerous and difficult to identify from limited field data. In this thesis, as the second contribution, a new and highly efficient method of parameterizing the pre-sliding (hysteretic) portion of the GMS friction model is presented. This approach drastically reduces the number of unknown variables to identify, by estimating only the affective breakaway force, breakaway displacement, and ‘shape factor’ describing the shape of the pre-sliding virgin curve. Reduction in the number of unknowns enables this ‘reduced parameter’ GMS model to be identified much more easily from in-process data, compared to the fully parameterized GMS model, and the time-dependent friction dynamics can still be simulated accurately. Having improved the positioning response transfer function estimation and friction modeling, as the third contribution of this thesis, these two elements are combined together in a 3-step process. First, the servo response is estimated considering simplified Coulomb friction dynamics. Then, the friction model is replaced and identified as a reduced parameter GMS model. In the third step, the transfer function poles and zeros, and the reduced parameter GMS model, are concurrently optimized to replicate the observed experimental response with even greater fidelity. This improvement has been quantified as 12-44% in RMS and 28-54% in MAX values. This approach is successful in servo systems with predominantly rigid body behavior. However, its extension to a servo system with vibratory dynamics did not produce an immediately observed improvement. This is attributed to the dominance of vibrations in response to the commanded trajectory, and further investigation is recommended for future research. Having an accurate model of a multi-axis machine’s feed drive response allows for the dynamic positioning errors, which can lead to workpiece inaccuracy or defects, to be predicted and corrected ahead of time. For this purpose, ILC has been investigated. It is shown that through ILC, 1-2 orders of magnitude reduction in the servo errors is possible. While ILC is already available in certain commercial CNC systems, its training cycle (which is performed during the operation of the machine tool) can lead to part defects and wasted productive machining time. The new idea proposed in this thesis is to perform ILC on a virtual model, which is continuously updated via real-time production data using the identification methods developed in this work. This would minimize the amount of trial and error correction needed on the actual machine. In the course of this thesis research, after validating the effectiveness of ILC in simulations, to reliably and safely migrate the virtual modeling and trajectory correction results into industry (such as on a gear grinding machine tool), the author initiated and led the design and fabrication of an industry-scale testing platform, comprising a Siemens 840D SolutionLine CNC with a multi-axis feed drive setup. Majority of this implementation has been completed, and in near future work, the dynamic accuracy and productivity improvements facilitated with ‘virtually’ tuned ILC are expected to be demonstrated experimentally and tested in industry

Dynamics and Control of Flexure-based Large Range Nanopositioning Systems.

The objective of this thesis is to demonstrate desktop-size and cost-effective flexure-based multi-axis nanopositioning capability over a motion range of several millimeters per axis. Increasing the motion range will overcome one of the main drawbacks of existing nanopositioning systems, thereby significantly improving the coverage area in nanometrology and nanomanufacturing applications. A single-axis nanopositioning system, comprising a symmetric double parallelogram flexure bearing and a traditional-architecture moving magnet actuator, is designed, fabricated, and tested. A figure of merit for the actuator is derived and shown to directly impact the system-level trade-offs in terms of range, resolution, bandwidth, and temperature rise. While linear feedback controllers provide good positioning performance for point-to-point commands, the tracking error for dynamic commands prove to be inadequate due to the nonlinearities in the actuator and its driver. To overcome this, an iterative learning controller is implemented in conjunction with linear feedback to reduce the periodic component of the tracking error by more than two orders of magnitude. Experimental results demonstrate 10 nm RMS tracking error over 8 mm motion range in response to a 2 Hz bandlimited triangular command. For the XY nanopositioning system, a lumped-parameter model of an existing XY flexure bearing is developed in order to understand the unexplained variation observed in the transfer function zeros over the operating range of motion. It is shown that the kinematic coupling, due to geometric nonlinearities in the beam mechanics, and small dimensional asymmetry, due to manufacturing tolerances, may conspire to produce complex-conjugate nonminimum phase zeros at certain operating points in the system's workspace. This phenomenon significantly restricts the overall performance of the feedback control system. After intentional use of large asymmetry is employed to overcome this problem, independent feedback and iterative learning controllers are implemented along each axis. Experimental results demonstrate 20 nm RMS radial tracking error while traversing a 2 mm diameter circle at 2 Hz.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107086/1/parmar_1.pd

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery

A robust and fast automatic moving object detection and tracking system is essential to characterize target object and extract spatial and temporal information for different functionalities including video surveillance systems, urban traffic monitoring and navigation, robotic. In this dissertation, I present a collaborative Spatial Pyramid Context-aware moving object detection and Tracking system. The proposed visual tracker is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called dynamically to assist master tracker. SPCT utilizes image spatial context at different level to make the video tracking system resistant to occlusion, background noise and improve target localization accuracy and robustness. We chose a pre-selected seven-channel complementary features including RGB color, intensity and spatial pyramid of HoG to encode object color, shape and spatial layout information. We exploit integral histogram as building block to meet the demands of real-time performance. A novel fast algorithm is presented to accurately evaluate spatially weighted local histograms in constant time complexity using an extension of the integral histogram method. Different techniques are explored to efficiently compute integral histogram on GPU architecture and applied for fast spatio-temporal median computations and 3D face reconstruction texturing. We proposed a multi-component framework based on semantic fusion of motion information with projected building footprint map to significantly reduce the false alarm rate in urban scenes with many tall structures. The experiments on extensive VOTC2016 benchmark dataset and aerial video confirm that combining complementary tracking cues in an intelligent fusion framework enables persistent tracking for Full Motion Video and Wide Aerial Motion Imagery.Comment: PhD Dissertation (162 pages