Adaptive Sliding Mode Contouring Control Design Based on Reference Adjustment and Uncertainty Compensation for Feed Drive Systems

Industrial feed drive systems, particularly, ball-screw and lead-screw feed drives are among the dominating motion components in production and manufacturing industries. They operate around the clock at high speeds for coping with the rising production demands. Adversely, high-speed motions cause mechanical vibrations, high-energy consumption, and insufficient accuracy. Although there are many control strategies in the literature, such as sliding mode and model predictive controls, further research is necessary for precision enhancement and energy saving. This study focused on design of an adaptive sliding mode contouring control based on reference adjustment and uncertainty compensation for feed drive systems. A combined reference adjustment and uncertainty compensator for precision motion of industrial feed drive systems were designed. For feasibility of the approach, simulation using matlab was conducted, and results are compared with those of an adaptive nonlinear sliding model contouring controller. The addition of uncertainty compensator showed a substantial improvement in performance by reducing the average contour error by 85.71% and the maximum contouring error by 78.64% under low speed compared to the adaptive sliding mode contouring controller with reference adjustment. Under high speed, the addition of uncertainty compensator reduced the average and absolute maximum contour errors by 4.48% and 10.13%, respectively. The experimental verification will be done in future. Keywords:    Machine tools, Feed drive systems, contouring control, Uncertainty dynamics, Sliding mode control

Approaches for improving cutting processes and machine too in re-contouring

Re-contouring in the repair process of aircraft engine blades and vanes is a crucial task. Highest demands are made on the geometrical accuracy as well as on the machined surface of the part. Complexity rises even more due to the unique part characteristic originating from the operation and repair history. This requires well-designed processes and machine tool technologies. In this paper, approaches for coping with these challenges and improving the re-contouring process are described and discussed. This includes an advanced process simulation with its capabilities to accurately depict different material areas and predict process forces. Beyond, experimental investigations on workpiece-tooldeflection are presented. Finally, a machine tool prototype with a novel electromagnetic guiding system is introduced and the benefits of this technology in the field of repair are outlined.DFG/CRC/87

Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System

This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input – output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities

Neural Network Contour Error Predictor in CNC Control Systems

Paper presented as poster presentation at MMAR 2016 conference (Międzyzdroje,Poland, 29 Aug.-1 Sept. 2016)This article presents a method for predicting contour error using artificial neural networks. Contour error is defined as the minimum distance between actual position and reference toolpath and is commonly used to measure machining precision of Computerized Numerically Controlled (CNC) machine tools. Offline trained Nonlinear Autoregressive networks with exogenous inputs (NARX) are used to predict following error in each axis. These values and information about toolpath geometry obtained from the interpolator are then used to compute the contour error. The method used for effective off-line training of the dynamic recurrent NARX neural networks is presented. Tests are performed that verify the contour error prediction accuracy using a biaxial CNC machine in a real-time CNC control system. The presented neural network based contour error predictor was used in a predictive feedrate optimization algorithm with constrained contour error

Rapid Identification of Virtual CNC Drives

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

Digital Twin Feed Drive Identification for Virtual Process Planning

Computer numerical controlled (CNC) machines have become an integral part of the manufacturing industry, allowing companies to increase the accuracy and productivity of their manufacturing lines. The next step to improving and accelerating the development process of a part is to involve virtual prototyping during the design phases. Virtual manufacturing has become an invaluable tool to process planners and engineers in recent years to model the manufacturing environment in a virtual setting to determine the final geometry and tolerances of new parts and processes. For a virtual twin of a CNC machine to be built, the dynamics of the drive and CNC controller must be identified. Traditionally, these identification techniques require several intrusive tests to be run on the machine tool, causing valuable time lost on production machines. In this thesis, three new techniques of developing virtual models of machine tools are discussed. The first model presented is a quasi-static model which is suitable for trajectory tracking error prediction. This technique is used to determine the contributions of the commanded velocity, acceleration, and jerk to the tracking errors of each axis of the machine tool. After determining these contributions, process planners can modify the axis feedrates in a virtual environment during trajectory optimization to find the best parameters for the shortest cycle time. This method was validated using a laser drilling machine tool from Pratt and Whitney Canada (P&WC) and was able to predict the root mean square (RMS) of the tracking error within 2.62 to 11.91 µm. A simple graphical user interface (GUI) was developed so that process planners and engineers can import data collected from the FANUC and Siemens CNC controllers to identify quasi-static models. The second model presented is a single input – single output (SISO) rigid body rapid identification model. In previous literature, a rapid identification method was proposed where a short G-code was run on machine tools, the input and output signals were collected from the controller and the dynamics were reverse engineered from the gathered data. However there were some shortfalls with this older method, the new proposed rapid identification model addresses these by improving parameter convergence and using commanded signal derivatives for identification. Tests were conducted on a five-axis machine tool located at the University of Waterloo (UW) to verify and compare the new rapid identification model to the previous model. It was determined that the model is able to predict the RMS of the tracking errors with 50-76% improvement and maximum contour error discrepancy with 22-35% improvement. Another GUI was developed for the SISO rigid body rapid identification model that allows users to import data collected from different machine tools and identify a model. The third model that is discussed in this thesis is a multi input – multi output (MIMO) model. This model builds upon the SISO rigid body model and is able to capture vibratory and elastic dynamics. Relations between inputs, such as reference and disturbance signals, can be related to a variety of measurable outputs. The model is used to predict the relationship between the inputs of commanded position and disturbance to the outputs of tracking error and velocity of the x- and y- axes of a P&WC five axis milling machine tool. Three different models were identified using this algorithm, two 1-axis 3rd order decoupled models and two 2-axis 6th order coupled model are compared in this thesis. The two 6 th order models have different search spaces, the first has a search space defined from the 3rd order decoupled identified parameters while the second has a more general search space. Overall, the 6th order model with a larger search space was able to predict the RMS and maximum tracking error more closely, with a maximum improvement of 19% for both metrics. However it should be noted that 6th order model with a smaller search space was still able to predict the RMS and maximum tracking error similarly to the 6th order model with the larger search space. The smaller search space configuration can save on computational time which can be advantageous in real world applications. In order to verify that the MIMO rapid identification technique would be able to identify a vibration mode, an experimental setup was designed and machined. A flexure mount with known vibration modes was designed, built and tested to validate Solidworks frequency simulation results. It was concluded that the simulation results were able to estimate the frequencies of the flexure with 95-98% accuracy and with a maximum absolute difference of 2.87 Hz. The flexure was mounted onto the five-axis machine tool at UW to introduce vibratory dynamics. Since there is a flexible mode being introduced at the tool-workpiece interface, the motor encoders would not be able to capture these dynamics, therefore a two-dimensional grid encoder (KGM) and two 3-axis accelerometers (one located on the tool head and the other on the workpiece table) were also placed on the machine tool to record the true tool-workpiece response. The data collected from the accelerometers were corrected for possible roll, pitch and yaw misalignments before synchronizing the accelerometer and KGM data to the motor encoder data. This data was then used to build MIMO rapid identification models with the commanded position (recorded from the motor encoders) and normalized Coulomb disturbance as the inputs to the system and the true tool-workpiece position or acceleration and machine tool feed drive velocity as the outputs to the model. The model estimated from the position measurements from the KGM yielded better results 19-1496% improvement in RMS tracking error prediction over the acceleration based models. The contouring error estimated using the KGM position model also has an improvement of 233-370% over the acceleration models. Using the transfer functions estimated from the accelerometer data, there was a 16-33% improvement in the RMS tracking error prediction and an 11-51% improvement in the maximum tracking error prediction over the KGM acceleration based model. The RMS contour prediction error also improved 4-5% and the maximum contour error prediction improved by 1-6% between the two models. Further development into the MIMO LTI algorithm is currently being done in the laboratory, including research into more complex friction models. It is also recommended to machine an actual part on the five axis machine tool and to measure the contouring error of the part on the coordinate measuring machine to verify the predictions presented in this thesis

From computer-aided to intelligent machining: Recent advances in computer numerical control machining research

The aim of this paper is to provide an introduction and overview of recent advances in the key technologies and the supporting computerized systems, and to indicate the trend of research and development in the area of computational numerical control machining. Three main themes of recent research in CNC machining are simulation, optimization and automation, which form the key aspects of intelligent manufacturing in the digital and knowledge based manufacturing era. As the information and knowledge carrier, feature is the efficacious way to achieve intelligent manufacturing. From the regular shaped feature to freeform surface feature, the feature technology has been used in manufacturing of complex parts, such as aircraft structural parts. The authors’ latest research in intelligent machining is presented through a new concept of multi-perspective dynamic feature (MpDF), for future discussion and communication with readers of this special issue. The MpDF concept has been implemented and tested in real examples from the aerospace industry, and has the potential to make promising impact on the future research in the new paradigm of intelligent machining. The authors of this paper are the guest editors of this special issue on computational numerical control machining. The guest editors have extensive and complementary experiences in both academia and industry, gained in China, USA and UK

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

Adaptive cutting force control on a milling machine with hybrid axis configuration

In the re-contouring process of aircraft engine components, the unknown geometry and inhomogeneous material properties of the workpiece are major challenges. For this reason a new repair process chain is supposed which consists of noncontact geometry identification, process simulation and NC-path planning, followed by a force controlled milling process. A new milling machine prototype is employed to ensure an effective force control loop. By use of a magnetic guided spindle slide, higher dynamics and precise tracking are enabled. Since variation of the process forces result in variable control plant characteristics, an indirect adaptive controller has been designed. Consequently, models of actuator and process are presented and the estimation of the present parameters by a recursive least square algorithm is outlined. Once the parameters are known, the control polynomials are calculated on the basis of a pole placement control approach. First experimental results of a force controlled milling process are put forward.DFG/SFB/87

Feed drive modelling for the simulation of tool path tracking in multi-axis High Speed Machining

International audienceWithin the context of High Speed Machining, it is essential to manage the trajectory generation to achieve both high surface quality and high productivity. As feed drives are one part of the set Machine tool - Numerical Controller, it is necessary to improve their performances to optimize feed drive dynamics during trajectory follow up. Hence, this paper deals with the modelling of the feed drive in the case of multi axis machining. This model can be used for the simulation of axis dynamics and tool-path tracking to tune parameters and optimize new frameworks of command strategies. A procedure of identification based on modern NC capabilities is presented and applied to industrial HSM centres. Efficiency of this modelling is assessed by experimental verifications on various representative trajectories. After implementing a Generalized Predictive Control, reliable simulations are performed thanks to the model. These simulations can then be used to tune parameters of this new framework according to the tool-path geometry