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

Development of a sensor for microvibrations measurement in the AlbaSat CubeSat mission

openMicrovibrations on spacecraft represent an issue for payloads requiring high pointing accuracy and/or stability over time, and they might represent a particular concern for CubeSats and small satellites that, usually, are not equipped with very-high performance attitude control systems. Hence, collecting reliable measures of the vibration spectra during the operations of a CubeSat represents a significant research activity. This thesis presents the development of a sensor, configured as a payload within the AlbaSat mission, capable of accurately measuring the microvibrations in space, with particular focus on those produced by the Momentum Exchange Devices (MED), i.e., Reaction or Momentum Wheels, that represent one of the most important microvibrations sources. The thesis takes place in the framework of the AlbaSat mission. AlbaSat is a 2U CubeSat developed by a student team of the University of Padova under the “Fly Your Satellite! – Design Booster” programme promoted by the European Space Agency (ESA). The mission has four different objectives: (1) to collect measurements of the space debris environment in-situ, (2) to measure the microvibrations on board the CubeSat, (3) to precisely determine the position of the satellite through laser ranging and (4) to investigate alternative systems for possible Satellite Quantum Communication applications on nanosatellites. The requirements for the correct sizing of the sensor and the chosen physical and functional architecture are defined and presented in the thesis. A meticulous schedule for functional tests is finally outlined, aimed at verifying the correct functionality of the microvibration sensor. These tests serve as a starting point for the future development of the payload.Microvibrations on spacecraft represent an issue for payloads requiring high pointing accuracy and/or stability over time, and they might represent a particular concern for CubeSats and small satellites that, usually, are not equipped with very-high performance attitude control systems. Hence, collecting reliable measures of the vibration spectra during the operations of a CubeSat represents a significant research activity. This thesis presents the development of a sensor, configured as a payload within the AlbaSat mission, capable of accurately measuring the microvibrations in space, with particular focus on those produced by the Momentum Exchange Devices (MED), i.e., Reaction or Momentum Wheels, that represent one of the most important microvibrations sources. The thesis takes place in the framework of the AlbaSat mission. AlbaSat is a 2U CubeSat developed by a student team of the University of Padova under the “Fly Your Satellite! – Design Booster” programme promoted by the European Space Agency (ESA). The mission has four different objectives: (1) to collect measurements of the space debris environment in-situ, (2) to measure the microvibrations on board the CubeSat, (3) to precisely determine the position of the satellite through laser ranging and (4) to investigate alternative systems for possible Satellite Quantum Communication applications on nanosatellites. The requirements for the correct sizing of the sensor and the chosen physical and functional architecture are defined and presented in the thesis. A meticulous schedule for functional tests is finally outlined, aimed at verifying the correct functionality of the microvibration sensor. These tests serve as a starting point for the future development of the payload

Design and optimisation of constrained electromagnetic energy harvesting devices

This thesis investigates the design and optimisation of constrained electromagnetic energy harvesters. It provides optimal design guidelines for constrained electromagnetic energy harvesters under harmonic and random vibrations. To find the characteristics of the vibration source, for instance vertical motion of a boat, the spectrum of the excitation amplitude should be obtained. Two Kalman filter based methods are proposed to overcome the difficulties of calculating displacement from measured acceleration. Analytical models describing the dynamics of linear and rotational electromagnetic energy harvesters are developed. These models are used to formulate a set of design rules for constrained linear and rotational energy harvesters subjected to a given sinusoidal excitation. For the sake of comparison and based on the electromechanical coupling coefficient of the systems, the maximum output power and the corresponding efficiency of linear and rotational harvesters are derived in a unified form. It is shown that under certain condition, rotational systems have greater capabilities in transferring energy to the load resistance and hence obtaining higher efficiency than linear systems. Also, the performance of a designed rotational harvester in response to broadband and band-limited random vibrations is evaluated and an optimum design process is presented for maximizing the output power under these conditions. It is furthermore shown that the profile of the spectral density of the measured acceleration signal of a typical boat can be approximated by a Cauchy distribution which is used to calculate the extracted power extracted by the proposed energy harvester in real conditions. In order to increase the operational bandwidth of rotational energy harvesters, subjected to time-varying frequency vibrations, a variable moment of inertia mechanism is proposed to adaptively tune the resonance frequency of harvester to match the excitation frequency. Also, the effects of combining the variable moment of inertia mechanism and adjusting the load resistance to increase the operational bandwidth of the system for constrained and unconstrained applications are studied. Finally, a ball screw based prototype is manufactured and the experimental results of its testing are presented which confirm the validity of the design and the derived dynamic equations of the system

Measuring skeletal kinematics with accelerometers on the skin surface

The most common motion analysis method uses cameras to track the position of markers on bodily surfaces over time. Although each species has a common skeletal frame to reference recorded motions, the soft tissue covering each is not rigid. Markers, therefore, experience motion relative to the bone and do not accurately portray underlying bone activity. This limits clinical use of motion studies and the understanding of joint motion. Use of MEMS accelerometers for removing soft tissue artifact, motion relative to the bone, from surface measurements and determining the position of the underlying bone was investigated. An animal limb was modeled experimentally as a double pendulum with soft tissue as sprung masses with motions perpendicular to the pendulums. Horizontal motion was cycled at the top joint with a 25 cm stroke. Position data obtained from the mass with a Codamotion™ system and integrated accelerometer data were combined in a Kalman filter to determine global position. Acceleration data in the sensor coordinate system determined tissue artifact and were compared to measurements using CODA markers on the mass and pendulum. Removing artifact from mass position estimated pendulum position over time. In determining mass position, integrated accelerometer data experienced drift, deviating from reasonable values and were determined impractical for Kalman filter input. This led to using only the CODA-determined position as the true position. Accelerometer artifacts resulted in mean differences with the CODA markers of less than 1 mm over 3 cm displacements excluding a mass with mechanical difficulties. The largest mean difference across four tests was 0.66 mm, which is 96.17 percent accurate. Mean differences between base positions collected from accelerometers and CODA markers were found for the global x and y directions. Maximum deviations were 1.64 mm and 4.45 mm, respectively, which are 99.56 and 99.63 percent accurate. Results show the effectiveness of this procedure in calculating the location of the bases of sprung masses in two dimensions. The basis of this research contributes to the determination of bone position over time that will increase the potential of understanding fundamental, rigid body and joint motions in a clinical setting using noninvasive methods

Design and implementation of a sensor testing system with use of a cable drone

Abstract. This thesis aims to develop a testing method for various sensors by modifying a commercial cable cam system to drive with an automated process at constant speed. The goal is to find a way to lift the cables in the air securely without a need for humans to climb on ladders and place them afterwards. This is achieved with a hinged truss tower structure that keeps the cables stabile while the tower is lifted. Another goal was to achieve automated movement of the cable drone. This is done by connecting a tracking camera to a computer that is used to control the cable drone’s motor controller. This will have the drone behave in a certain way depending on the tracking camera’s position data. Third goal is to build a portable sensor system which collects and saves the data from the tested sensors. This goal is achieved with an aluminium profile frame which is equipped with all the necessary equipment, such as a powerful computer. Research included studying different sensors’ performance evaluation criteria and effect of the wind on magnitude of the force in this application. Research was done by studying written sources and consulting a cable camera company called Motion Compound GbR. Results of this master’s thesis are used to evaluate if the idea of using a cable cam is applicable for this kind of sensor testing system. As the conclusion the cable drone with automated driving is evaluated to be a practical method which can still be further developed to meet the requirements even better. Antureiden testausjärjestelmän suunnittelu ja toteuttaminen käyttäen vaijeridronea. Tiivistelmä. Tämän diplomityön tavoitteena on muokata kaupallisesta vaijerikamerajärjestelmästä vakionopeudella liikkuva testausmenetelmä eri antureille. Yhtenä työn tavoitteena on löytää tapa nostaa käytettävät vaijerit ylös turvallisesti siten, ettei niitä tarvitse asentaa jälkikäteen korkealla. Tämä toteutetaan saranoidulla, trusseista rakennetulla tornilla. Tornin huipulle asennetaan laakeroidut akselit sekä suoja, jotka yhdessä pitävät vaijerit paikoillaan myös tornin noston ajan. Toinen tavoite on saavuttaa vaijerilennokin automatisoitu liike. Tämä tapahtuu kytkemällä seurantakamera tietokoneeseen, jota käytetään ohjaamaan myös vaijeridronen moottoriohjainta. Näin vaijeridrone saadaan käyttäytymään halutulla tavalla riippuen seurantakameran sijaintitiedoista. Kolmas tavoite on rakentaa kannettava anturijärjestelmä, jolla kerätään ja tallennetaan testatuilla antureilla kerätty data. Tämä tavoite saavutetaan alumiiniprofiilirungolla, joka varustetaan tarvittavilla laitteilla, kuten esimerkiksi tehokkaalla tietokoneella. Tutkimukseen kuului myös antureiden suorituskyvyn arviointikriteereihin tutustuminen sekä työssä käytettävästä järjestelmästä koituvan voiman suuruuden laskeminen. Tutkimus tehtiin perehtymällä kirjallisuuteen ja konsultoimalla vaijerikamera-alalla toimivaa Motion Compound GbR -yritystä. Tämän diplomityön tuloksia voidaan hyödyntää arvioitaessa, onko vaijerikamerajärjestelmä sovellettavissa mainitun anturien testausjärjestelmän rakentamisessa. Lopputuloksena automatisoidulla ajolla varustetun vaijeridronen arvioidaan olevan tähän tarkoitukseen toimiva menetelmä, jota voidaan edelleen kehittää vastaamaan vaatimuksia vielä paremmin

DEVELOPMENT OF A NOVEL Z-AXIS PRECISION POSITIONING STAGE WITH MILLIMETER TRAVEL RANGE BASED ON A LINEAR PIEZOELECTRIC MOTOR

Piezoelectric-based positioners are incorporated into stereotaxic devices for microsurgery, scanning tunneling microscopes for the manipulation of atomic and molecular-scale structures, nanomanipulator systems for cell microinjection and machine tools for semiconductor-based manufacturing. Although several precision positioning systems have been developed for planar motion, most are not suitable to provide long travel range with large load capacity in vertical axis because of their weights, size, design and embedded actuators. This thesis develops a novel positioner which is being developed specifically for vertical axis motion based on a piezoworm arrangement in flexure frames. An improved estimation of the stiffness for Normally Clamped (NC) clamp is presented. Analytical calculations and finite element analysis are used to optimize the design of the lifting platform as well as the piezoworm actuator to provide maximum thrust force while maintaining a compact size. To make a stage frame more compact, the actuator is integrated into the stage body. The complementary clamps and the amplified piezoelectric actuators based extenders are designed such that no power is needed to maintain a fixed vertical position, holding the payload against the force of gravity. The design is extended to a piezoworm stage prototype and validated through several tests. Experiments on the prototype stage show that it is capable of a speed of 5.4 mm/s, a force capacity of 8 N and can travel over 16 mm

Design of an intelligent embedded system for condition monitoring of an industrial robot

PhD ThesisIndustrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure, and an unforeseen robot stoppage, due to different reasons, has the potential to cause an interruption in the entire production line, resulting in economic and production losses. Condition monitoring (CM) is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reduce maintenance costs. So, the main focus of this research is to design and develop an online, intelligent CM system based on wireless embedded technology to detect and diagnose the most common faults in the transmission systems (gears and bearings) of the industrial robot joints using vibration signal analysis. To this end an old, but operational, PUMA 560 robot was utilized to synthesize a number of different transmission faults in one of the joints (3 - elbow), such as backlash between the gear pair, gear tooth and bearing faults. A two-stage condition monitoring algorithm is proposed for robot health assessment, incorporating fault detection and fault diagnosis. Signal processing techniques play a significant role in building any condition monitoring system, in order to determine fault-symptom relationships, and detect abnormalities in robot health. Fault detection stage is based on time-domain signal analysis and a statistical control chart (SCC) technique. For accurate fault diagnosis in the second stage, a novel implementation of a time-frequency signal analysis technique based on the discrete wavelet transform (DWT) is adopted. In this technique, vibration signals are decomposed into eight levels of wavelet coefficients and statistical features, such as standard deviation, kurtosis and skewness, are obtained at each level and analysed to extract the most salient feature related to faults; the artificial neural network (ANN) is then used for fault classification. A data acquisition system based on National Instruments (NI) software and hardware was initially developed for preliminary robot vibration analysis and feature extraction. The transmission faults induced in the robot can change the captured vibration spectra, and the robot’s natural frequencies were established using experimental modal analysis, and also the fundamental fault frequencies for the gear transmission and bearings were obtained and utilized for preliminary robot condition monitoring. In addition to simulation of different levels of backlash fault, gear tooth and bearing faults which have not been previously investigated in industrial robots, with several levels of ii severity, were successfully simulated and detected in the robot’s joint transmission. The vibration features extracted, which are related to the robot healthy state and different fault types, using the data acquisition system were subsequently used in building the SCC and ANN, which were trained using part of the measured data set that represents the robot operating range. Another set of data, not used within the training stage, was then utilized for validation. The results indicate the successful detection and diagnosis of faults using the key extracted parameters. A wireless embedded system based on the ZigBee communication protocol was designed for the application of the proposed CM algorithm in real-time, using an Arduino DUE as the core of the wireless sensor unit attached on the robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK board) was used as the base station of the wireless system on which the robot’s fault diagnosis algorithm is run. To implement the two stages of the proposed CM algorithm on the designed embedded system, software based on the C programming language has been developed. To demonstrate the reliability of the designed wireless CM system, experimental validations were performed, and high reliability was shown in the detection and diagnosis of several seeded faults in the robot. Optimistically, the established wireless embedded system could be envisaged for fault detection and diagnostics on any type of rotating machine, with the monitoring system realized using vibration signal analysis. Furthermore, with some modifications to the system’s hardware and software, different CM techniques such as acoustic emission (AE) analysis or motor current signature analysis (MCSA), can be applied.Iraqi government, represented by the Ministry of Higher Education and Scientific Research, the Iraqi Cultural Attaché in London, and the University of Technology in Baghda