Virtual Model Building for Multi-Axis Machine Tools Using Field Data

Accurate machine dynamic models are the foundation of many advanced machining technologies such as virtual process planning and machine condition monitoring. Viewing recent designs of modern high-performance machine tools, to enhance the machine versatility and productivity, the machine axis configuration is becoming more complex and diversified, and direct drive motors are more commonly used. Due to the above trends, coupled and nonlinear multibody dynamics in machine tools are gaining more attention. Also, vibration due to limited structural rigidity is an important issue that must be considered simultaneously. Hence, this research aims at building high-fidelity machine dynamic models that are capable of predicting the dynamic responses, such as the tracking error and motor current signals, considering a wide range of dynamic effects such as structural flexibility, inter-axis coupling, and posture-dependency. Building machine dynamic models via conventional bottom-up approaches may require extensive investigation on every single component. Such approaches are time-consuming or sometimes infeasible for the machine end-users. Alternatively, as the recent trend of Industry 4.0, utilizing data via Computer Numerical Controls (CNCs) and/or non-intrusive sensors to build the machine model is rather favorable for industrial implementation. Thus, the methods proposed in this thesis are top-down model building approaches, utilizing available data from CNCs and/or other auxiliary sensors. The achieved contributions and results of this thesis are summarized below. As the first contribution, a new modeling and identification technique targeting a closed-loop control system of coupled rigid multi-axis feed drives has been developed. A multi-axis closed-loop control system, including the controller and the electromechanical plant, is described by a multiple-input multiple-output (MIMO) linear time-invariant (LTI) system, coupled with a generalized disturbance input that represents all the nonlinear dynamics. Then, the parameters of the open-loop and closed-loop dynamic models are respectively identified by a strategy that combines linear Least Squares (LS) and constrained global optimization. This strategy strikes a balance between model accuracy and computational efficiency. This proposed method was validated using an industrial 5-axis laser drilling machine and an experimental feed drive, achieving 2.38% and 5.26% root mean square (RMS) prediction error, respectively. Inter-axis coupling effects, i.e., the motion of one axis causing the dynamic responses of another axis, are correctly predicted. Also, the tracking error induced by motor ripple and nonlinear friction is correctly predicted as well. As the second contribution, the above proposed methodology is extended to also consider structural flexibility, which is a crucial behavior of large-sized industrial 5-axis machine tools. More importantly, structural vibration is nonlinear and posture-dependent due to the nature of a multibody system. In this thesis, prominent cases of flexibility-induced vibrations in a linear feed drive are studied and modeled by lumped mass-spring-damper system. Then, a flexible linear drive coupled with a rotary drive is systematically analyzed. It is found that the case with internal structural vibration between the linear and rotary drives requires an additional motion sensor for the proposed model identification method. This particular case is studied with an experimental setup. The thesis presents a method to reconstruct such missing internal structural vibration using the data from the embedded encoders as well as a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU) mounted on the machine table. It is achieved by first synchronizing the data, aligning inertial frames, and calibrating mounting misalignments. Finally, the unknown internal vibration is reconstructed by comparing the rigid and flexible machine kinematic models. Due to the measurement limitation of MEMS IMUs and geometric assembly error, the reconstructed angle is unfortunately inaccurate. Nevertheless, the vibratory angular velocity and acceleration are consistently reconstructed, which is sufficient for the identification with reasonable model simplification. Finally, the reconstructed internal vibration along with the gathered servo data are used to identify the proposed machine dynamic model. Due to the separation of linear and nonlinear dynamics, the vibratory dynamics can be simply considered by adding complex pole pairs into the MIMO LTI system. Experimental validation shows that the identified model is able to predict the dynamic responses of the tracking error and motor force/torque to the input command trajectory and external disturbances, with 2% ~ 6% RMS error. Especially, the vibratory inter-axis coupling effect and posture-dependent effect are accurately depicted. Overall, this thesis presents a dynamic model-building approach for multi-axis feed drive assemblies. The proposed model is general and can be configured according to the kinematic configuration. The model-building approach only requires the data from the servo system or auxiliary motion sensors, e.g., an IMU, which is non-intrusive and in favor of industrial implementation. Future research includes further investigation of the IMU measurement, geometric error identification, validation using more complicated feed drive system, and applications to the planning and monitoring of 5-axis machining process

Chip geometry, cutting force, and elastic deformation prediction for gear hobbing

The machining industry is constantly challenged through increasing demands for productivity and stringent part quality requirements such as dimensional accuracy and surface quality. Physics-based models are becoming more commonly employed in the manufacturing industry for traditional machining processes like turning, milling, and drilling. By utilizing such models, machining process planners can optimize productivity while preserving or improving part quality, through virtual manufacturing of the components ahead of time via realistic simulations. In this context, cutting force prediction models are essential for machining process simulations. For traditional machining operations, where the cutter and workpiece geometries and kinematics are simple, cutting forces can be calculated via analytical equations. However, in complex processes like 5-axis milling, turn-milling and gear machining, the cutter-workpiece engagement is very complex and is best calculated using geometric CAD modelers. This engagement information allows for cutting forces along the cutting edge of the tool to be computed and summed up. Modeling the cutting forces also provides insight into the torque/power requirement, elastic deformation, vibrations, and machining stability (chatter) during the process, which are the primary factors that contribute to dimensional inaccuracies, surface location errors, and poor surface finish. By integrating these models, a comprehensive physics-based approach to machining processes can be developed, allowing for accurate simulation, prediction, and optimization of part quality. The main objective of this thesis is to establish the very first steps of such an integrated simulation environment for the gear hobbing process, by investigating the efficient prediction of cutting forces and elastic deformations. Hobbing is a high-speed and accurate gear cutting process used extensively to produce external gears – which are essential components in power transmission, automotive, aerospace, and automation (e.g., robotics) applications. The hobging process involves feeding a rotating cutting tool (known as a ‘hob’) into a workpiece (referred to as blank gear) that is rotating while the two are meshed together, as would be in worm-gear mechanism. This results in the continuous removal of chips during the process. Unlike conventional machining operations, hobbing has complex tool and workpiece geometries, and complicated kinematics with multi-axis motions. In this thesis, a mathematical model of the hobbing kinematics is developed and validated through collected CNC signals obtained using the Siemens 840D controller of Liebherr LC500 hobbing machine. The cutter-workpiece engagement is calculated using an efficient discrete geometric modeler in tri-dexel format. Using Delaunay triangulation and alpha shape reconstruction, the 2D cross-section of the uncut chip is created from its internal data. This cross-section is then utilized to approximate the local chip geometry along the discretized cutting edge of the tool. Each node along the cutting edge represents a generalized oblique cutting force model with specific rake and inclination angles, and principal directions (i.e., tangential, feed, and radial). At each time step, the incremental forces for the engaged cutting edge nodes are computed and ultimately integrated to obtain the total cutting forces. Using a rotary dynamometer, the proposed cutting force model has been validated through cutting trials on a Liebherr LC500 CNC hobbing machine. The tests involved cutting of several spur and helical external gears with varying process parameters in single and two-pass processes. The model reasonably captures the overall behavior of the measured forces, min/max force envelopes and cutting strokes with the RMS error being 7-21% for roughing passes and 24-36% for finishing passes throughout the tests, which is reasonable for machining process planning. In the finishing cut, due to the forces being smaller, the signal-to-noise ratio and apparent prediction accuracy are worse. The elastic deformation is modeled based on the static stiffness of the tooling and workpiece assemblies. The stiffness is approximated from experimentally-measured mechanical frequency response functions (FRFs). The expected elastic deformations are computed by dividing the cutting forces by the static stiffness values. The calculated deflections are then used to superpose the tool’s nominal position in the time-domain simulation of the gear machining operation, thereby gears to be ‘virtually-machined’ with errors originating both from the kinematics of the hobbing feeding process, as well as the mechanical elastic deformations. The virtually-produced gears are then measured according to the ANSI/AGMA standard for gear inspection, using the integrated gear cutting simulation and metrology software developed at the University of Waterloo, and the prediction results are compared with the quality inspection measurements taken from physically machined gears, using a GLEASON 300GMS Lead & Involute Checker. The lead deviation predictions showed good correlation, while profile deviations require further research. Overall, this thesis has achieved a detailed physics-based model for hobbing, which focuses on the kinematics, chip geometry, cutting forces, and elastic deformation. Future research will explore error sources in the cutting force model prediction, enhancing the elastic deformation model, and developing models for vibrations and chatter

International Workshop on MicroFactories (IWMF 2012): 17th-20th June 2012 Tampere Hall Tampere, Finland

This Workshop provides a forum for researchers and practitioners in industry working on the diverse issues of micro and desktop factories, as well as technologies and processes applicable for micro and desktop factories. Micro and desktop factories decrease the need of factory floor space, and reduce energy consumption and improve material and resource utilization thus strongly supporting the new sustainable manufacturing paradigm. They can be seen also as a proper solution to point-of-need manufacturing of customized and personalized products near the point of need

Analysis and Strategies for Five-Axis Near-Dry EDM Milling.

Strategies for precision five-axis near-dry electric discharge machining (EDM) milling are investigated. By understanding the material removal process behind near-dry EDM milling, its performance can further be improved using a machine with five degrees of freedom. Three major research areas are investigated: (1) effect of electrode orientation in five-axis milling, (2) the trajectory planning for five-axis near-dry EDM milling, and (3) a new gap control strategy for five-axis near-dry EDM process. Computational fluid dynamics (CFD) model is developed to predict the dielectric fluid flow rate for various electrode inclinations and qualitatively compared with the experimentally measured material removal rate. The study shows that the material removal rate is linearly proportional to the mass flow rate of air and kerosene mixture, the tool electrode wear ratio is inversely related to the mass flow rate of the air and kerosene mixture, and the average surface roughness is not correlated with the flow rate of the mixture. Using the results from the electrode orientation investigation, a tool path planning strategy that maximizes the material removal rate in roughing process is developed. The strategy includes methods to engage into the workpiece, machining of workpiece edge, minimum lead angle for curved surface, and minimum and maximum path interval. Experimental verifications of the proposed path planning strategy yielded higher material removal rate compared with that of standard path planning. A new gap control strategy for five-axis near-dry EDM is proposed and experimentally investigated. The new gap controller retracts the electrode in the direction of electrode orientation. The performance of the new gap controller in term of material removal rate, tool electrode wear ratio and surface roughness is compared with that of a conventional controller. The experimental verification yielded 30% increase in material removal rate while not affecting the tool electrode wear ratio and surface roughness.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64717/1/fujiki_1.pd

Development of compliant mechanisms for real-time machine tool accuracy enhancement using dual-servo principle

Ph.DDOCTOR OF PHILOSOPH

Rapid prototyping using a precision robotic manipulator

A rapid prototyping system using a precision robotic manipulator has been developed. The system is comprised of a latest personal computer (Pentium II, 300 MHz, 128 MB RAM and 5 GB hard disk capacity), interfacing system (PS-23 indexer, KS-drives and servomotors), a four degrees of freedom precision manipulator and a ball nosed end milling equipment. The hardware is integrated with the AutoSurf (CAD software), which is used in designing engineering models, section cut the surface models and changing graphic file into DXF files (neutral format files). The AutoLISP (AutoSurf programming language) has been used to simulate the additive prototyping process. The hardware is also linked with the self-developed CAM programs for data processing and motion control. With the above hardware and software configuration, subtractive prototyping models have been produced successfully. Simple additive prototyping process was also simulated graphically in AutoSurf environment. The CAM programs were also tested to be fine with the additive prototyping models’ data files. Generally, the rapid prototyping system using the precision robotic manipulator has the advantage of being cheaper, effective, time and space saving, with dual purposes (subtractive and additive processes) and it is an all in one system

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

Kinematics and Robot Design II (KaRD2019) and III (KaRD2020)

This volume collects papers published in two Special Issues “Kinematics and Robot Design II, KaRD2019” (https://www.mdpi.com/journal/robotics/special_issues/KRD2019) and “Kinematics and Robot Design III, KaRD2020” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2020), which are the second and third issues of the KaRD Special Issue series hosted by the open access journal robotics.The KaRD series is an open environment where researchers present their works and discuss all topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. It aims at being an established reference for researchers in the field as other serial international conferences/publications are. Even though the KaRD series publishes one Special Issue per year, all the received papers are peer-reviewed as soon as they are submitted and, if accepted, they are immediately published in MDPI Robotics. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”.KaRD2019 together with KaRD2020 received 22 papers and, after the peer-review process, accepted only 17 papers. The accepted papers cover problems related to theoretical/computational kinematics, to biomedical engineering and to other design/applicative aspects

Intelligent monitoring and control system for a friction stir welding process

A Friction Stir Welding machine is proposed and built to allow future research into the process and to provide a framework from which the application of intelligent manufacturing to industrial processes can be investigated. Initially a literature survey was conducted upon which the design of the machine could be based. The conversion of a conventional milling machine into a Friction Stir Welding machine by applying modern monitoring and control systems is then presented. Complete digital control was used to drive actuators and monitor sensors. A wireless chuck mounted monitoring system was implemented, enabling forces, torques, temperature and speed of the tool to be obtained directly from the process. Software based on a hierarchical Open Systems Architectural design, incorporating modularity, interoperability, portability and extensibility is implemented. This experimental setup is used to analyze the Friction Stir Welding process by performing data analysis using statistical methods. Three independent variables (weld speed, spindle speed and plunge depth) were varied and the independent variables (forces, torques, power, temperature, speed, etc) recorded using the implemented software. The statistical analysis includes the analysis of variants, regression analysis and the creation of surface plots. Using these results, certain linguistic rules for process control are proposed. An intelligent controller is designed and discussed, using the derived rules to improve and optimize certain aspects of the process encountered during the experimental phase of the research