Measurement of the structural behaviour of a 3D airless wheel prototype by means of optical non-contact techniques

Additive Manufacturing (AM) is becoming a widely employed technique also in mass production. In this field, compliances with geometry and mechanical performance standards represent a crucial constrain. Since 3D printed products exhibit a mechanical behaviour that is difficult to predict and investigate due to the complex shape and the inaccuracy in reproducing nominal sizes, optical non-contact techniques are an appropriate candidate to solve these issues. In this paper, 2D digital image correlation and thermoelastic stress analysis are combined to map the stress and the strain performance of an airless wheel prototype. The innovative airless wheel samples are 3D-printed by fused deposition modelling and stereolithography in poly-lactic acid and photopolymer resin, respectively. The static mechanical behaviour for different wheel-ground contact configurations is analysed using the aforementioned non-contact techniques. Moreover, the wheel-ground contact pressure is mapped, and a parametric finite element model is developed. The results presented in the paper demonstrate that several factors have great influence on 3D printed airless wheels: a) the type of material used for manufacturing the specimen, b) the correct transfer of the force line (i.e., the loading system), c) the geometric complexity of the lattice structure of the airless wheel. The work confirms the effectiveness of the proposed non-contact measurement procedures for characterizing complex shaped prototypes manufactured using AM

Are inductive current transformers performance really affected by actual distorted network conditions? An experimental case study

The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in off-nominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or low-power instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two off-the-shelf current transformers. Then, their accuracy performances have been evaluated by means of the well-known composite error index and an approximated version of it. The obtained results show that under realistic non-rated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes

Li-ion battery modeling and state of charge estimation method including the hysteresis effect

In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. The proposed characterization procedure and the model effectiveness for SoC estimation are experimentally verified using a real grid-connected storage system. A mixed algorithm is adopted for SoC estimation, which takes into account both the traditional Coulomb counting method and the developed model. The experimental comparison with the traditional approach and the obtained results show the feasibility of the proposed approach for accurate SoC estimation, even in the presence of low-accuracy measurement transducers

3D Multispectral Imaging for Cultural Heritage Preservation: The Case Study of a Wooden Sculpture of the Museo Egizio di Torino

Digitalization techniques, such as photogrammetry (PG), are attracting the interest of experts in the cultural heritage field, as they enable the creation of three-dimensional virtual replicas of historical artifacts with 2D digital images. Indeed, PG allows for acquiring data regarding the overall appearance of an artifact, its geometry, and its texture. Furthermore, among several image-based techniques exploited for the conservation of works of art, multispectral imaging (MSI) finds great application in the study of the materials of historical items, taking advantage of the different responses of materials when exposed to specific wavelengths. Despite their great usefulness, PG and MSI are often used as separate tools. Integrating radiometric and geometrical data can notably expand the information carried by a 3D model. Therefore, this paper presents a novel research methodology that enables the acquisition of multispectral 3D models, combining the outcomes of PG and MSI (Visible (VIS), Ultraviolet-induced Visible Luminescence (UVL), Ultraviolet-Reflected (UVR), and Ultraviolet-Reflected False Color (UVR-FC) imaging) in a single coordinate system, using an affordable tunable set-up and open-source software. The approach has been employed for the study of two wooden artifacts from the Museo Egizio di Torino to investigate the materials present on the surface and provide information that could support the design of suitable conservation treatments

Enhanced Nonlinear System Identification by Interpolating Low-Rank Tensors

Function approximation from input and output data is one of the most investigated problems in signal processing. This problem has been tackled with various signal processing and machine learning methods. Although tensors have a rich history upon numerous disciplines, tensor-based estimation has recently become of particular interest in system identification. In this paper we focus on the problem of adaptive nonlinear system identification solved with interpolated tensor methods. We introduce three novel approaches where we combine the existing tensor-based estimation techniques with multidimensional linear interpolation. To keep the reduced complexity, we stick to the concept where the algorithms employ a Wiener or Hammerstein structure and the tensors are combined with the well-known LMS algorithm. The update of the tensor is based on a stochastic gradient decent concept. Moreover, an appropriate step size normalization for the update of the tensors and the LMS supports the convergence. Finally, in several experiments we show that the proposed algorithms almost always clearly outperform the state-of-the-art methods with lower or comparable complexity.Comment: 12 pages, 4 figures, 3 table

Estudi del consum energètic de convertidors analògic-digital integrats en microcontroladors

Microcontrollers are programmable integrated circuits designed to control any particular application. Nowadays are present in many of the electronic devices that surround us: at work, at home and in our life in general. They can be found by controlling the operation of mobiles, computers, tablets, TVs, smart clocks, etc. These are also the main control devices in low-energy sensing nodes applied to smart cities and intelligent buildings. The microcontrollers have integrated hardware modules (called peripherals) in the same chipandthatcanworksimultaneouslywiththeexecutionofinstructionsmadebytheCPU. One of these peripherals is the analog-digital converter (ADC) that has the function of converting an analog input voltage to digital data. This bachelor thesis is intended to study the energy consumption of these integrated ADCs. To do this study, two integrated converters will be presented in different commercial microcontrollers and different experiments will be carried out to measure their consumption. The power consumption of the two ADCs will be studied separately and later a comparison will be made. Finally, conclusions will be drawn on the most appropriate mode of operation (for example, in terms of working frequency of this peripheral) for a lowconsumption ADC conversion that allows, for example, increasing the life time of sensor nodes.Els microcontroladors son circuits integrats programables destinats a governar una apli- ´ cacio determinada. Aquests estan presents en molts dels dispositius electr ´ onics que ens ènvolten: a la feina, a casa nostra i en la nostra vida en general. Es poden trobar controlant el funcionament de mobils, ordinadors, tablets, TV, rellotges intel ` ·ligents, etc. Tambe s ´ onéls dispositius principals de control en nodes sensors de baix consum aplicats a ciutats i edificis intel·ligents. Els microcontroladors disposen de recursos hardware (anomenats periferics) integrats en èl mateix xip i que poden funcionar simultaniament amb l’execuci ` o d’instruccions feta per ´ la CPU. Un d’aquests periferics ` es el convertidor anal ´ ogic-digital (ADC) que t ` e la funci ´ o´ de convertir una tensio d’entrada anal ´ ogica en una dada digital. ` Aquest treball de final de grau esta destinat a estudiar el consum energ ` etic d’aquests ` ADC integrats. Per fer aquest estudi, es presentaran dos convertidors integrats en diferents microcontroladors comercials i es realitzaran diferents experiments per mesurar el seu consum. S’estudiara el consum d’energia dels dos convertidors ADC per separat i ` posteriorment es fara una comparativa. Finalment s’extrauran conclusions sobre el mode ` de funcionament mes adequat (per exemple, en quant a la freq ´ u¨encia de treball) per ob- ` tenir una conversio ADC de baix consum que permeti, per exemple allargar el temps de ´ vida de nodes sensor

Fusion Of Multiple Inertial Measurements Units And Its Application In Reduced Cost, Size, Weight, And Power Synthetic Aperture Radars

Position navigation and timing (PNT) is the concept of determining where an object is on the Earth (position), the destination of the object (navigation), and when the object is in these positions (timing). In autonomous applications, these three attributes are crucial to determining the control inputs required to control and move the platform through an area. Traditionally, the position information is gathered using mainly a global positioning system (GPS) which can provide positioning sufficient for most PNT applications. However, GPS navigational solutions are limited by slower update rates, limited accuracy, and can be unreliable. GPS solutions update slower due to the signal having to travel a great distance from the satellite to the receiver. Additionally, the accuracy of the GPS solution relies on the environment of the receiver and the effects caused by additional reflections that introduce ambiguity into the positional solution. As result, the positional solution can become unstable or unreliable if the ambiguities are significant and greatly impact the accuracy of the positional solution. A common solution to addressing the shortcomings of the GPS solution is to introduce an additional sensor focused on measuring the physical state of the platform. The sensors popularly used are inertial measurement units (IMU) and can help provide faster positional accuracy as the transmission time is eliminated. Furthermore, the IMU is directly measuring physical forces that contribute to the position of the platform, therefore, the ambiguities caused by additional signal reflections are also eliminated. Although the introduction of the IMU helps mitigate some of the shortcomings of GPS, the sensors introduce a slightly different set of challenges. Since the IMUs directly measure the physical forces experienced by the platform, the position is estimated using these measurements. The estimates of position utilize the previously known position and estimate the changes to the position based on the accelerations measured by the IMUs. As the IMUs intrinsically have sensor noise and errors in their measurements, the noise errors directly impact the accuracy of the position estimated. These inaccuracies are further compounded as the erroneous position estimate is now used as the basis for future position calculations. Inertial navigation systems (INS) have been developed to pair the IMUs with the GPS to overcome the challenges brought by each sensor independently. The data provided from each sensor is processed using a technique known as data fusion where the statistical likelihood of each positional solution is evaluated and used to estimate the most likely position solution given the observations from each sensor. Data fusion allows for the navigation solution to provide a positional solution at the sampling rate of the fastest sensor while also limiting the compounding errors intrinsic to using IMUs. Synthetic aperture radar (SAR) is an application that utilizes a moving radar to synthetically generate a larger aperture to create images of a target scene. The larger aperture allows for a finer spatial resolution resulting in higher quality SAR images. For synthetic aperture radar applications, the PNT solution is fundamental to producing a quality image as the range to a target is only reported by the radar. To form an image, the range to each target must be aligned over the coherent processing interval (CPI). In doing so, the energy reflected from the target as the radar is moving can be combined coherently and resolved to a pixel in the image product. In practice, the position of the radar is measured using a navigational solution utilizing a GPS and IMU. Inaccuracies in these solutions directly contribute to the image quality in a SAR system because the measured range from the radar will not agree with the calculated range to the location represented by the pixel. As a result, the final image becomes unfocused and the target will be blurred across multiple pixels. For INS systems, increasing the accuracy of the final position estimate is dependent on the accuracy of the sensors in the system. An easy way to increase the accuracy of the INS solution is to upgrade to a higher grade IMU. As a result, the errors compounded by the IMU estimations are minimized because the intrinsic noise perturbations are smaller. The trade-off is the IMU sensors increase in cost, size, weight, and power (C-SWAP) as the quality of the sensor increases. The increase in C-SWAP is a challenge of utilizing higher grade IMUs in INS navigational solutions for SAR applications. This problem is amplified when developing miniaturized SAR systems. In this dissertation, a method of leveraging the benefits of data fusion to combine multiple IMUs to produce higher accuracy INS solutions is presented. Specifically, the C-SWAP can be reduced when utilizing lower-quality IMUs. The use of lower quality IMUs presents an additional challenge of providing positional solutions at the rates required for SAR. A method of interpolating the position provided by the fusion algorithm while maintaining positional accuracy is also presented in this dissertation. The methods presented in this dissertation are successful in providing accurate positional solutions from lower C-SWAP INS. The presented methods are verified in simulations of motion paths and the results of the fusion algorithms are evaluated for accuracy. The presented methods are instrumented in both ground and flight tests and the results are compared to a 3rd party accurate position solution for an accuracy metric. Lastly, the algorithms are implemented in a miniaturized SAR system and both ground and airborne SAR tests are conducted to evaluate the effectiveness of the algorithms. In general, the designed algorithms are capable of producing positional accuracy at the rate required to focus SAR images in a miniaturized SAR system

Optimization and application of a flexible dual arm robot based automation system for sample preparation and measurement

This dissertation describes the optimization of the implementation of the Yaskawa SDA10F dual-arm robot to carry out routine sample preparation tasks in a life science laboratory such that standard lab equipment can be used and the robot can replace humans in sample preparation process. The existing robot control software is changed to carry out various tasks consecutively without interruption. Robot environment and motions were optimized allowing system expansion, multiple batches of samples are made at a time, increasing throughput. The system was validated with the help of two applications