Neural network contour error prediction of a bi-axial linear motor positioning system

In the article a method of predicting contour error using artificial neural network for a bi-axial positioning system is presented. The machine consists of two linear stages with permanent magnet linear motors controlled by servo drives. The drives are controlled from a PC with real-time operating system via EtherCAT fieldbus. A randomly generated Non-Uniform Rational B-Spline (NURBS) trajectory is used to train offline a NARX-type artificial neural network for each axis. These networks allow prediction of following errors and contour errors of the motion trajectory. Experimental results are presented that validate the viability of the neural network based contour error prediction. The presented contour error predictor will be used in predictive control and velocity optimization algorithms of linear motor based CNC machines

Controlling Contour Errors in CNC Machines

Ph.DDOCTOR OF PHILOSOPH

An investigation into the effects of thermal errors of a machine tool on the dimensional accuracy of parts

The reduction of machining errors has become increasingly important in modern manufacturing in order to obtain the required quality of parts. Geometric error makes up the basic part of the inaccuracy of the machine tool at the cold stage; however, as the machine running time increases, thermally-induced errors start to play a major role in machined workpiece accuracy. Dimensional accuracy of machined parts could be affected by several factors, such as the machine tool’s condition, the workpiece material, machining procedures and the operator’s skill. Of these, the machine condition plays an important role in determining the machine’s performance and its effects on the final dimensions of machined parts. The machine’s condition can be evaluated by its errors which include the machine’s built-in geometric and kinematic error, thermal error, cutting force-induced error and other errors.This research represents a detailed study of the effects of thermal errors of a machine tool on the dimensional accuracy of the parts produced on it. A new model has been developed for the prediction of thermally-induced errors of a three-axis machine tool. By applying the proposed model to real machining examples, the dimensional accuracy of machined parts was improved. The research work presented in this thesis has the following four unique characteristics:• Investigated the thermal effects on the dimensional accuracy of machined parts by machining several components at different thermal conditions of a machine tool to establish a direct relationship between the dimensional accuracy of machined parts and the machine tool’s thermal status.• Developed a new model for calculating thermally-induced volumetric error where the three axial positioning errors were modelled as functions of ball screw nut temperature and travel distance. The influences of the other 18 error components were ignored due to their insignificant influence.• Employed a Laser Doppler Displacement Meter (LDDM) with three thermocouples, instead of the expensive laser interferometer and the large number of thermocouples required by the traditional model, to assess the thermally-induced volumetric errors of a three-axis CNC machining centre. The thermally-induced volumetric error predictions were in good agreement with the measured results.• Applied the newly developed thermally-induced volumetric error compensation model for drilling operations to improve the positioning accuracy of drilled holes. The results show that positioning accuracy of the drilled holes was improved significantly after compensation. The absolute reduction of the positioning errors of drilled holes was an average 30.44 μm at the thermal stable stage, while the average relative reduction ratio of these errors was 77%.Therefore, the proposed thermally-induced volumetric error compensation model can bean effective tool for enhancing the machining accuracy of existing machine tools used in the industry

SpinX: Time-resolved 3D Analysis of Spindle Dynamics using Deep Learning Techniques and Mathematical Modelling.

PhD ThesesLive-cell movies generate terabytes of data. However, manual analysis of this data is prone to error and can easily exhaust days of research time, thus limiting the insights that can be gleaned from cutting edge microscopes. Automated analysis has been hard because of discontinuities between the distinct frames of 3D live-cell movies. We present SpinX, a comprehensive and extensible computational framework which bridges the gaps between discontinuous frames in time lapse movies by utilising state-of-the-art Deep Learning technologies and modelling for 3D reconstruction of highly mobile subcellular structures. Using SpinX, we are now in a position to precisely track and analyse the movements of multiple subcellular structures within minutes, including the cell cortex, chromosomes and the mitotic spindle. We demonstrate the utility of SpinX by employing it to define the precise roles of spindle movement regulators that ultimately determine the plane of cell division. We illustrate the extensibility of SpinX by showing how it can also be used to infer the regulation of complex cortex-microtubule interactions. Our analyses reveal previously unrecognised roles for the evolutionarily conserved Dynein motor and MARK2/Par1 polarity kinase in regulating the 3D movements of the mitotic spindle. Thus, SpinX provides an exciting opportunity to study spindle dynamics in relation to the cell cortex using hundreds of time-resolved 3D movies in a novel way

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

1992 NASA/ASEE Summer Faculty Fellowship Program

For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers

Manufacturing Metrology

Metrology is the science of measurement, which can be divided into three overlapping activities: (1) the definition of units of measurement, (2) the realization of units of measurement, and (3) the traceability of measurement units. Manufacturing metrology originally implicates the measurement of components and inputs for a manufacturing process to assure they are within specification requirements. It can also be extended to indicate the performance measurement of manufacturing equipment. This Special Issue covers papers revealing novel measurement methodologies and instrumentations for manufacturing metrology from the conventional industry to the frontier of the advanced hi-tech industry. Twenty-five papers are included in this Special Issue. These published papers can be categorized into four main groups, as follows: Length measurement: covering new designs, from micro/nanogap measurement with laser triangulation sensors and laser interferometers to very-long-distance, newly developed mode-locked femtosecond lasers. Surface profile and form measurements: covering technologies with new confocal sensors and imagine sensors: in situ and on-machine measurements. Angle measurements: these include a new 2D precision level design, a review of angle measurement with mode-locked femtosecond lasers, and multi-axis machine tool squareness measurement. Other laboratory systems: these include a water cooling temperature control system and a computer-aided inspection framework for CMM performance evaluation

A Subject-Specific Multiscale Model of Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is a neuromodulation technique used to treat a variety of neurological disorders. While many types of neuromodulation therapy are invasive, TMS is an attractive alternative because it is noninvasive and has a very strong safety record. However, clinical use of TMS has preceded a thorough scientific understanding: its mechanisms of action remain elusive, and the spatial extent of modulation is not well understood. We created a subject-specific, multiscale computational model to gain insights into the physiological response during motor cortex TMS. Specifically, we developed an approach that integrates three main components: 1) a high-resolution anatomical MR image of the whole head with diffusion weighted MRI data; 2) a subject-specific, electromagnetic, non-homogeneous, anisotropic, finite element model of the whole head with a novel time-dependent solver; 3) a population of multicompartmental pyramidal cell neuron models. We validated the model predictions by comparing them to motor evoked potentials (MEPs) immediately following single-pulse TMS of the human motor cortex. This modeling approach contains several novel components, which in turn allowed us to gain greater insights into the interactions of TMS with the brain. Using this approach we found that electric field magnitudes within gray matter and white matter vary substantially with coil orientation. Our results suggest that 1) without a time-dependent, subject-specific, non-homogeneous, anisotropic model, loci of stimulation cannot be accurately predicted; 2) loci of stimulation depend upon biophysical properties and morphologies of pyramidal cells in both gray and white matter relative to the induced electric field. These results indicate that the extent of neuromodulation is more widespread than originally thought. Through medical imaging and computational modeling, we provide insights into the effects of TMS at a multiscale level, which would be unachievable by either method alone. Finally, our approach is amenable to clinical implementation. As a result, it could provide the means by which TMS parameters can be prescribed for treatment and a foundation for improving coil design

White Matter Hyperintensity and Multi-region Brain MRI Segmentation Using Convolutional Neural Network

Accurate segmentation of WMH (white matter hyperintensity) from the magnetic resonance image is a prerequisite for many precise medical procedures, especially for the diagnosis of vascular dementia. Brain segmentation has important research significance and clinical application prospects especially for early detection of Alzheimer’s disease. In order to effectively perform accurate segmentation according to the MRI characteristics of different regions of the brain, this thesis proposed an optimized 3D u-net and used WHM segmentation as a pre-experiment to select the good hyperparameters (i.e. network depth, image fusion method, and the implementation of loss function) to construct an image feature learning network with both long and short skip connections. Soft voting is used as the postprocessing procedure. Our model is evaluated by a 10-fold cross-validation and achieved a dice score of 0.78 for binary segmentation (WMH segmentation) and accuracy of 0.96 for multi-class segmentation (139 regions brain segmentation), outperforming other methods

Integrated Neuromusculoskeletal Modeling within a Finite Element Framework to Investigate Mechanisms and Treatment of Neurodegenerative Conditions

Neurodegenerative and neurodevelopmental disorders are a group of conditions that stem from irregularities in the nervous system that lead to complications in function and movement. The goal of this work is to develop computational tools that: (1) measure the accuracy of surgical interventions in neurodegenerative and neurodevelopmental conditions, and (2) integrate neural and musculoskeletal frameworks to provide a platform to better investigate neurodegenerative and neurodevelopmental disorders. Parkinson’s disease (PD) is a neurodegenerative condition projected to affect over 1.2 million people by 2030 in the US. It is caused by atypical firing patterns in the basal ganglia region of the brain that leads to primary motor symptoms of tremor, slowness of movement, and rigidity. A potential treatment for PD is deep brain stimulation (DBS). DBS involves implanting electrodes into central brain structures to regulate the pathological signaling. Electrode placement accuracy is a key metric that helps to determine patient outcomes postoperatively. An automated measurement system was developed to quantify electrode placement accuracy in robot-assisted asleep DBS procedures (Chapter 2). This measurement system allows for precise metrics without human bias in large cohorts of patients. This measurement system was later modified to measure screw placement accuracy in spinal fusion procedures for the treatment of degenerative musculoskeletal conditions (Chapter 3). DBS is an effective treatment for PD, but it is not a cure for the cause of the disease itself. To cure neurodegenerative and neurodevelopmental diseases, the underlying disease mechanisms must be better understood. A major limitation in studying neural conditions is the infeasibility of performing in vivo experiments, particularly in humans due to ethical considerations. Computational modeling, specifically fully predictive neuromusculoskeletal (NMS) models, can help to accumulate additional knowledge about neural pathways that cannot be determined experimentally. NMS models typically include complexity in either the neuromuscular or musculoskeletal system, but not both, making it difficult or infeasible to investigate the relationship between neural signaling and musculoskeletal function. To overcome this, a fully predictive NMS model was developed by integrating NEURON software within Abaqus, a finite element (FE) environment (Chapter 4). The neural model consisted of a pool of motor neurons innervating the soleus muscle in a FE human ankle model. Software integration was verified against previously published data, and the neuronal network was verified for motor unit recruitment and rate coding, which are the two principles required for in vivo muscle generation. To demonstrate the applicability of the model to study neurodegenerative and neurodevelopmental diseases, a fully predictive mouse hindlimb NMS model was developed using the integrated framework to investigate Rett syndrome (RS) (Chapter 5). RS is a neurodevelopmental disorder caused by a mutation of the Mecp2 gene with hallmark motor symptoms of a loss of purposeful hand movement, changes in muscle tone, and a loss of speech. Recent experimental analysis has found that the axon initial segment (AIS) in mice that model RS has torsional morphology compared to wildtype littermate controls. The effects these neural morphological changes have on joint motion will be studied using the mouse NMS model. This work encompasses a range of research that uses computational models to study the underlying mechanisms and design targeted treatment options for neurodegenerative and neurodevelopmental disorders. The outcomes of this work have quantified the accuracy at which surgical interventions for these conditions can be performed and have resulted in a neuromusculoskeletal model that can be applied to understand how neural morphology, and associated changes due to these disorders, affects musculoskeletal function