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

Size-dependent endocytosis of gold nanoparticles studied by three-dimensional mapping of plasmonic scattering images

<p>Abstract</p> <p>Background</p> <p>Understanding the endocytosis process of gold nanoparticles (AuNPs) is important for the drug delivery and photodynamic therapy applications. The endocytosis in living cells is usually studied by fluorescent microscopy. The fluorescent labeling suffers from photobleaching. Besides, quantitative estimation of the cellular uptake is not easy. In this paper, the size-dependent endocytosis of AuNPs was investigated by using plasmonic scattering images without any labeling.</p> <p>Results</p> <p>The scattering images of AuNPs and the vesicles were mapped by using an optical sectioning microscopy with dark-field illumination. AuNPs have large optical scatterings at 550-600 nm wavelengths due to localized surface plasmon resonances. Using an enhanced contrast between yellow and blue CCD images, AuNPs can be well distinguished from cellular organelles. The tracking of AuNPs coated with aptamers for surface mucin glycoprotein shows that AuNPs attached to extracellular matrix and moved towards center of the cell. Most 75-nm-AuNPs moved to the top of cells, while many 45-nm-AuNPs entered cells through endocytosis and accumulated in endocytic vesicles. The amounts of cellular uptake decreased with the increase of particle size.</p> <p>Conclusions</p> <p>We quantitatively studied the endocytosis of AuNPs with different sizes in various cancer cells. The plasmonic scattering images confirm the size-dependent endocytosis of AuNPs. The 45-nm-AuNP is better for drug delivery due to its higher uptake rate. On the other hand, large AuNPs are immobilized on the cell membrane. They can be used to reconstruct the cell morphology.</p

N-(6-Methyl-2-pyrid­yl)formamide

The mol­ecule of the title compound, C7H8N2O, is essentially planar with a maximum deviation of 0.0439 (1) Å from the best plane. In the crystal, N—H⋯O hydrogen bonds between self-complementary amide groups join mol­ecules into centrosymmetric dimers

Location-based Data Analysis of Visitor Structure for Recreational Area Management

This work presents a location-based data analysis framework for profiling visitors structures. In terms of recreational area management, understanding visitors’ structure and popularity is important. Traditionally, visitors monitoring with automatic counting devices has drawbacks of inaccurate visitors counting. In this work, compared to automatic counting devices, we use Wi-Fi tracking as the main method to count visitors, which provides a fairly precise picture of visitor structures. Moreover, we deliver rich analytic functions in this framework and we present the functionality with visitor data collected from Guanyinshan Visitor Center. This framework not only standardizes visitor counting process but also facilitates a profound analysis of visitor structures. Key Words: Guanyinshan Visitor Center, Wi-Fi trackin

Ceftriaxone attenuates hypoxic-ischemic brain injury in neonatal rats

<p>Abstract</p> <p>Background</p> <p>Perinatal brain injury is the leading cause of subsequent neurological disability in both term and preterm baby. Glutamate excitotoxicity is one of the major factors involved in perinatal hypoxic-ischemic encephalopathy (HIE). Glutamate transporter GLT1, expressed mainly in mature astrocytes, is the major glutamate transporter in the brain. HIE induced excessive glutamate release which is not reuptaked by immature astrocytes may induce neuronal damage. Compounds, such as ceftriaxone, that enhance the expression of GLT1 may exert neuroprotective effect in HIE.</p> <p>Methods</p> <p>We used a neonatal rat model of HIE by unilateral ligation of carotid artery and subsequent exposure to 8% oxygen for 2 hrs on postnatal day 7 (P7) rats. Neonatal rats were administered three dosages of an antibiotic, ceftriaxone, 48 hrs prior to experimental HIE. Neurobehavioral tests of treated rats were assessed. Brain sections from P14 rats were examined with Nissl and immunohistochemical stain, and TUNEL assay. GLT1 protein expression was evaluated by Western blot and immunohistochemistry.</p> <p>Results</p> <p>Pre-treatment with 200 mg/kg ceftriaxone significantly reduced the brain injury scores and apoptotic cells in the hippocampus, restored myelination in the external capsule of P14 rats, and improved the hypoxia-ischemia induced learning and memory deficit of P23-24 rats. GLT1 expression was observed in the cortical neurons of ceftriaxone treated rats.</p> <p>Conclusion</p> <p>These results suggest that pre-treatment of infants at risk for HIE with ceftriaxone may reduce subsequent brain injury.</p

Temporal and Spatial Variations in spatial variations in symbiont communities of catch bowl coral Isopora palifera (Scleractinia: Acroporidae) on reefs in Kenting National Park, Taiwan

Acclimatization through Symbiodinium shuffling is one of potential mechanisms in reef-building corals to survive environmental stress. In our previous study, the catch bowl coral Isopora palifera in Tantzei Bay (TZB), Nanwan, Kenting National Park (KNP), southern Taiwan was demonstrated to shuffle thermal-tolerant Symbiodinium D1a and thermal-sensitive Symbiodinium C3 in response to seasonal variations in sea surface temperatures (SSTs) in 2000 and 2001. In this study, we reexamined the temporal dynamics of the Symbiodinium community of I. palifera in TZB in 2006-2009. In addition, spatial variations in Symbiodinium communities in I. palifera were also examined at 6 other sites of Nanwan, KNP in 2009, including a site located at a nuclear power plant outlet (NPP-OL) in southern Taiwan with a yearly mean SST 0.6-1.5 degrees C higher compared to the other sites. Phylotyping and DNA sequence analyses of Symbiodinium ribosomal 28S and ITS2 markers showed that I. palifera colonies at TZB continued to show seasonal shuffling, but shifted to thermal-sensitive type C3 dominant in 2006-2009. This differed from the symbiont community originally dominated by the thermal-tolerant Symbiodinium D1a in 2000 and 2001 after the 1998 mass-bleaching event. Significant differences in spatial variations of the symbiont community in Nanwan were detected with I. palifera colonies at the NPP-OL dominated by Symbiodinium D1a. Our study results suggest that I. palifera can acclimatize to SST anomalies by shuffling to thermal-tolerant Symbiodinium D1a and can revert to thermal-sensitive C3 when the stress disappears, but will maintain the thermally tolerant Symbiodinium D1a as the dominant symbiont if the heat stress continues

2-{5-[N-(2-Pyridyl)carbamo­yl]pentan­amido}pyridinium hexa­fluoro­phosphate

In the crystal structure of the title compound, C16H19N4O2 +·PF6 −, the cations and anions are situated on centres of inversion. Thus, the N—H H atom is disordered over both N atoms due to symmetry. In the crystal, mol­ecules are connected via N—H⋯F and N—H⋯O hydrogen bonds. The cation adopts the ⋯AAA⋯ trans conformation in the solid state

Experimental verification of a wireless sensing and control system for structural control using MR dampers

The performance aspects of a wireless ‘active’ sensor, including the reliability of the wireless communication channel for real-time data delivery and its application to feedback structural control, are explored in this study. First, the control of magnetorheological (MR) dampers using wireless sensors is examined. Second, the application of the MR-damper to actively control a half-scale three-storey steel building excited at its base by shaking table is studied using a wireless control system assembled from wireless active sensors. With an MR damper installed on each floor (three dampers total), structural responses during seismic excitation are measured by the system's wireless active sensors and wirelessly communicated to each other; upon receipt of response data, the wireless sensor interfaced to each MR damper calculates a desired control action using an LQG controller implemented in the wireless sensor's computational core. In this system, the wireless active sensor is responsible for the reception of response data, determination of optimal control forces, and the issuing of command signals to the MR damper. Various control solutions are formulated in this study and embedded in the wireless control system including centralized and decentralized control algorithms. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/56121/1/682_ftp.pd

Interpretable estimation of the risk of heart failure hospitalization from a 30-second electrocardiogram

Survival modeling in healthcare relies on explainable statistical models; yet, their underlying assumptions are often simplistic and, thus, unrealistic. Machine learning models can estimate more complex relationships and lead to more accurate predictions, but are non-interpretable. This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations. We train an eXtreme Gradient Boosting accelerated failure time model and exploit SHapley Additive exPlanations values to explain the effect of each feature on predictions. Our model achieved a concordance index of 0.828 and an area under the curve of 0.853 at one year and 0.858 at two years on a held-out test set of 6,573 patients. These results show that a rapid test based on an electrocardiogram could be crucial in targeting and treating high-risk individuals.Comment: 4 pages, 4 figure