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Viscoelastic materials play a key role in mechanical designs due to its numerous application. Even though numerical models already exist and are included in commercial codes, the precision ...

This work deals with the characterization of a hyperelastic material and the subsequent validation in different stressed states. The well-known three-parameter Mooney–Rivlin model is chosen for the sake of simplicity. In order to obtain the mechanical properties of this material, a specimen is tested using tensile forces. Once the tests are performed, the material constants are determined using a genetic algorithm to fit the experimental curve. An accurate fitness function is defined and the procedure to obtain the generations is described. Finally, with the aim to model and validate the results in a more complex setting, physical tests are performed in combination with numerical studies and FEM simulations.

In this paper, the reliability of a finite element (FE) model including carbon-fibre reinforced plastics (CFRPs) is evaluated for a case of a complex thin-wall honeycomb structure designed for a scientific instrument, such as a calorimeter. Mechanical calculations were performed using FE models including CFRPs, which required a specific definition to describe the micro-mechanical behaviour of the orthotropic materials coupled to homogeneous ones. There are well-known commercial software packages used as powerful tools for analyzing structures, however, for complex (many-parts) structures, the models become largely time consuming for both definition and calculation, which limits the appropriate feedback for the structure&rsquo, s design. This study introduces a method to reduce a highly nonlinear model, including CFRPs, into a robust, simplified and realistic FE model capable of describing the deformations of the structure with known uncertainties. Therefore, to calculate the deviations of our model, displacement measurements in a reduced mechanical setup were performed, and then a variety of FE models were studied with the objective to find the simplest model with reliable results. The approach developed in this work leads to concluding that the deformations evaluated, including the uncertainties, were below the actual production tolerances, which makes the proposed model a successful tool for the designing process. Ultimately, this study serves as a future reference for complex projects requiring intensive mechanical evaluations for designing decisions.

This work is a first approach to the management applied to the Periodical Technical Inspection (PTI) stations in the context of Industry 4.0. Since 1965, PTIs are mandatory in Spain, initially only for public transport and freight trucks, but since the 1980s they are compulsory for all types of vehicles, in order to control vehicles technical performance to avoid traffic accidents. Number of PTI stations and inspection lines have been increased from 1981 to 2017, from about 30 to more than 460 operative stations and from about 70 to more than 1200 inspection lines. This fact makes necessary to implement a new management model in the context of the Industry 4.0 that will lead to the PTI 4.0. The use of cyber physical systems, and the application of smart factory concepts in the inspection processes will allow to incorporate new benefits to the management of PTI stations, in addition to improving their effectiveness and efficiency. Regarding 2016 data, more than 19.5 million of vehicles were inspected in Spain and almost 24 million of defects were detected. Also, the total number of vehicles has increased about a 3.6%, but inspections have been increasing more than 5%. Taking into account the number of items to verify, the use of Industry 4.0 theologies, such as Big Data, is highly recommended. In addition, since 2013, Spanish DGT has launched Telematics’ Information Exchange with the objective to get telematics data recording of the PTI results. This make necessary to contemplate concepts based on Industry 4.0 for their management.

The aim of this article is to announce the results for the antenna deployment mechanism on the Cubesat [1] , [4] Xatcobeo picosatellite which launched on 13th February, 2012 from the Kourou space port aboard the new VEGA rocket. It was also launched as part of Humsat-D on 21st November, 2013 aboard Dnepr. The main feature of the device is its extremely light weight, achieved by using polymeric materials and additive manufacturing. Analysis was not only made of detailed characteristics but also of the advantages of using this combination, its validity after almost two years of perfect operation in orbit on Xatcobeo, and its latest operational success on Humsat-D. The results show that it deployed as expected in orbit and that it continues to operate correctly on both missions, not only in terms of the deployment mechanism but also the materials used. The analysis focuses on the mechanism׳s operational reliability and long useful lifetime.

The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO4) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.

The ornamental rock sector in Galicia (NW Spain), in particular the granite extraction and processing sector, has undergone tremendous development in recent years as a direct consequence of the use of non-aggressive rock mass extraction methods and the progressive modernization and automation of the different processes for processing and finishing the slabs, as well as the improvement of in-plant logistics. Nevertheless, it has been ascertained that cutting parameters entered as setpoint values in the diamond circular saws, used for the subdivision of slabs of the different commercial granite varieties, do not fit clear technical criteria for the optimization of cutting efficiency, but are selected and pre-set based on the experience of the circular saw operator. In order to evaluate the validity of thus selected cutting parameters and to provide the sector with a more objective tool, the cutting performance of seven granite varieties from Galicia which have high sales was analysed. Thus, each available slab was subdivided using four different cutting settings resulting from modifying two of the parameters which most affect cutting efficiency: cutting speed (m/min) and cutting depth (mm). These parameters, in turn, have an influence on power consumption (kWh), diamond saw blade wear (mm) and average production (mm2/min). Thereby, an exhaustive data acquisition process was carried out in order to create a complete database with the representative information of the cutting of each of the seven tested varieties and determine the optimum configuration for cutting each material, based on the results of a graphical analysis of the mean of data and the analysis of the variance (ANOVA).

In this paper, we numerically study a dynamic viscoelastic problem. The variational formulation is written as a linear parabolic variational equation for the velocity field. An existence and uniqueness result is recalled. Then, fully discrete approximations are introduced using the implicit Euler scheme and the finite element method, for which some a priori error estimates are derived, leading to the linear convergence of the algorithm under suitable additional regularity conditions. Finally, some one- and three-dimensional numerical simulations are presented to show the accuracy of the algorithm and the behaviour of the solution, including a comparison with an experimental study.

It is not uncommon to use profiles to act as energy absorption parts in vehicle safety systems. This work analyses an impact attenuator based on a simple design and discusses the use of a thermoplastic material. We present the design of the impact attenuator and a mechanical test for the prototype. We develop a simulation model using the finite element method and explicit dynamics, and we evaluate the most appropriate mesh size and integration for describing the test results. Finally, we consider the performance of different materials, metallic ones (steel AISI 4310, Aluminium 5083-O) and a thermoplastic foam (IMPAXX500™). This reflects the car industry’s interest in using new materials to make high-performance, low-mass energy absorbers. We show the strength of the models when it comes to providing reliable results for large deformations and strong non-linearities, and how they are highly correlated with respect to the test results both in value and behaviour.

This paper studies the stress state of a threaded fastening by using Finite Element (FE) models, applied to surgical screws in cortical bone. There is a general interest in studying the stress states induced in the different elements of a joint caused by the thread contact. Analytical models were an initial approach, and later FE models allowed detailed studies of the complex phenomena related to these joints. Different studies have evaluated standard threaded joints in machinery and structures, being the thread symmetric. However, surgical screws employ asymmetric thread geometry, selected to improve the stress level generated in the bone. Despite the interest and widespread use, there is scarce documentation on the actual effect of this thread type. In this work, we discuss the results provided by FE models with detailed descriptions of the contacts comparing differences caused by the materials of the joint, the thread geometry and the thread’s three-dimensional helical effects. The complex contacts at the threaded surfaces cause intense demand on computational resources that often limits the studies including these joints. We analyze the results provided by one commercial software package to simplify the threaded joints. The comparison with detailed FE models allows a definition of the level of uncertainty and possible limitations of this type of simplifications, and helps in making suitable choices for complex applications.

Shape optimization is a widely used technique in the design phase of a product. Current ongoing improvement policies require a product to fulfill a series of conditions from the perspective of mechanical resistance, fatigue, natural frequency, impact resistance, etc. All these conditions are translated into equality or inequality restrictions which must be satisfied during the optimization process that is necessary in order to determine the optimal shape. This article describes a new method for shape optimization that considers any regular shape as a possible shape, thereby improving on traditional methods limited to straight profiles or profiles established a priori. Our focus is based on using functional techniques and this approach is, based on representing the shape of the object by means of functions belonging to a finite-dimension functional space. In order to resolve this problem, the article proposes an optimization method that uses machine learning techniques for functional data in order to represent the perimeter of the set of feasible functions and to speed up the process of evaluating the restrictions in each iteration of the algorithm. The results demonstrate that the functional approach produces better results in the shape optimization process and that speeding up the algorithm using machine learning techniques ensures that this approach does not negatively affect design process response times.

10 págs.; 22 figs.; 3 tabs., Advancement of the CALIFA calorimeter project has reached a new milestone with the construction of the first modules of the CALIFA Demonstrator, ultimately to be integrated into the final calorimeter. Aspects and methods of detector optimisation will be discussed, along with characterisation using proton beams of 70<, This work has been supported by the following projects MINECO (FPA2012-39404-C02-01, FPA2012-39404-C02-02, FPA2013-47831- C2-1, FPA2013-47831-C2-1, FPA2013-47831-C2-1), PRI-PIMNUP- 2011-1357, Xunta de Galicia (GRC2013-11), NUPET Eranet (GANAS), ENSAR (VIIPM), HIC for FAIR, BMBF(05P12WOFNF, 05P12WOFNUE, 05P12RDFN8, 05P12RDFN8), DFG (EXC153) and VR (2012-4550, 2013-2109, 2013-4178, 2014-6343).

Current knowledge of the behavior of heavy quadricycles under impact is still very poor. One of the most significant causes is the lack of energy absorption in the vehicle frame or its steel chassis structure. For this reason, special steels (with yield stresses equal to or greater than 350 MPa) are commonly used in the automotive industry due to their great strain hardening properties along the plastic zone, which allows good energy absorption under impact. This paper presents a proposal for a steel quadricycle energy absorption system which meets the percentages of energy absorption for conventional vehicles systems. This proposal is validated by explicit dynamics simulation, which will define the whole problem mathematically and verify behavior under impact at speeds of 40 km/h and 56 km/h using the finite element method (FEM). One of the main consequences of this study is that this FEM–based methodology can tackle high nonlinear problems like this one with success, avoiding the need to carry out experimental tests, with consequent economical savings since experimental tests are very expensive. Finally, the conclusions from this innovative research work are given.

Current anchorage systems for poles, lighting columns, etc., have an inherent strength that causes serious injuries, and even death, in cases of vehicle impact. This article explores the deficiency of traditional anchorage systems and explains the development of a new anchorage system over the different phases, such as design conditions, FEM analysis, dynamic testing and actual crash tests. The behaviour of the traditional and new anchorage systems are compared, including the use of a data acquisition system and a high-speed camera. The aim of this study was to develop a new non-energy-absorbing anchorage system, adaptable to lighting poles, signposts, etc., and to evaluate the decrease in primary injuries with the employment of this system.

The main aim of this research work is to build a new practical hybrid regression model to predict the milling tool wear in a regular cut as well as entry cut and exit cut of a milling tool. The model was based on Particle Swarm Optimization (PSO) in combination with support vector machines (SVMs). This optimization mechanism involved kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, a PSO–SVM–based model, which is based on the statistical learning theory, was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. To accomplish the objective of this study, the experimental dataset represents experiments from runs on a milling machine under various operating conditions. In this way, data sampled by three different types of sensors (acoustic emission sensor, vibration sensor and current sensor) were acquired at several positions. A second aim is to determine the factors with the greatest bearing on the milling tool flank wear with a view to proposing milling machine’s improvements. Firstly, this hybrid PSO–SVM–based regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the flank wear (output variable) and input variables (time, depth of cut, feed, etc.). Indeed, regression with optimal hyperparameters was performed and a determination coefficient of 0.95 was obtained. The agreement of this model with experimental data confirmed its good performance. Secondly, the main advantages of this PSO–SVM–based model are its capacity to produce a simple, easy–to–interpret model, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, the main conclusions of this study are exposed.

In this paper we describe the DEMONSTRATOR structures and active units (PETALs) developed for the detector CALIFA of the experiment R3 B - FAIR. The design is based in the CALIFA BARREL mechanical solutions, but adapted to the characteristics of the PETALs, namely in what concerns the load distribution during setup and service. The R&D program defined the materials and procedures for both producing the pieces of carbon fiber (CF) composites as well as the mounting of the bundles to make an alveolar structure. The procedures also include a quality control program to ensure the dimensional properties of the CF assemblies. We are also developing the use of tomographic imaging analysis for this quality program, that will be of mayor interest in the construction of the future CALIFA CF-structure.

In this work we present the mechanical concept proposed for one of the sec- tions of the detector CALIFA of the R 3 B experiment for FAIR. The use of an alveolar structure made of carbon-fiber composites allows for a light and robust solution to hold the active elements with an extreme mass ratio below 0.7%. The active core is supported by structural elements designed to make a fully operational assembly, taking care of dif- ferent configurations and functionality. All the design has been developed using intensive calculation based in finite elements models and physical simulations.

The R3B experiment (Reactions with Relativistic Radioactive Beams) at FAIR (Facility for Antiproton and Ion Research) is a versatile setup dedicated to the study of reactions induced by high-energy radioactive beams. It will provide kinematically complete measurements with high efficiency, acceptance and resolution, making possible a broad physics program with rare-isotopes. CALIFA (CALorimeter for In-Flight detection of gamma-rays and high energy charged pArticles), is a complex detector based on scintillation crystals, that will surround the target of the R3B experiment. CALIFA will act as a total absorption gamma-calorimeter and spectrometer, as well as identifier of charged particles from target residues. This versatility is its most challenging requirement, demanding a huge dynamic range, to cover from low energy gamma-rays up to 300 MeV protons. This fact, along with the high-energy of the beams determine the conceptual design of the detector, presented in this paper, together with the technical solutions proposed for its construction. © 2014 Elsevier Inc., This work was supported by: FPA2009-14604-C02-01, FPA2009-14604-C02-02, FPA2009-07387, HIC for FAIR, BMBF(06DA9040I, 05P12RDFN8, 06MT9156, 05P12WOFNF), DFG EXC153, GSI-TU Darmstadt cooperationn and EraNet NupNet GANAS.

The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006–2008) and they are used to create a highly nonlinear model of the PM10 in t...

Well established in the field of scintillator detection, Caesium Iodide remains at the forefront of scintillators for use in modern calorimeters. Recent developments in photosensor technology have lead to the production of Large Area Avalanche Photo Diodes (LAAPDs), a huge advancement on traditional photosensors in terms of high internal gain, dynamic range, magnetic field insensitivity, high quantum efficiency and fast recovery time. The R 3B physics programme has a number of requirements for its calorimeter, one of the most challenging being the dual functionality as both a calorimeter and a spectrometer. This involves the simultaneous detection of ∼300MeV protons and gamma rays ranging from 0.1 to 20 MeV. This scintillator - photosensor coupling provides an excellent solution in this capacity, in part due to the near perfect match of the LAAPD quantum efficiency peak to the light output wavelength of CsI(Tl). Modern detector development is guided by use of Monte Carlo simulations to predict detector performance, nonetheless it is essential to benchmark these simulations against real data taken with prototype detector arrays. Here follows an account of the performance of two such prototypes representing different polar regions of the Barrel section of the forthcoming CALIFA calorimeter. Measurements were taken for gamma-ray energies up to 15.1 MeV (Maier-Leibnitz Laboratory, Garching, Germany) and for direct irradiation with a 180 MeV proton beam (The Svedberg Laboratoriet, Uppsala, Sweden). Results are discussed in light of complementary GEANT4 simulations. © 2013 Elsevier B.V., This work has been supported by FPA 2009, GANAS, ENSAR, HIC for FAIR, Mineco (FPA2009-14604-C02-01, FPA2009-07387), BMBF (06DA9040I, 05P12RDFN8, 06MT9156, 05P12WOFNF, 05P12WONUE), the Spanish Ministereo de Ciencia e Innovación (FP2005-00732) and the Xunta de Galicia (project number PGIDIT07PXIB206124PR).

Quality control is a necessary component of the slate slab manufacturing process so as to evaluate defects as defined by the current standard for slate. Quality control has traditionally been performed manually by an expert in the field, with the consequent human subjectivity. We studied the feasibility of using a 3D laser scanner to measure slate slabs and analyze possible defects that would lead to the rejection of slabs for particular industrial processes. The application requires slate characterization to be performed in real time and thereby requires a short computation time. We describe an optimized calibration method based on Tsai's approach that reduces calculation complexity and cost in this key 3D laser scanning stage. Configured and implemented for slate slab characterization, the system produces the required information in real time during the production process.

Resistive-plate-chambers (RPCs) were proposed to be used to build a time-of-flight detector for relativist heavy ions of the R3B-FAIR experiment, as well as other applications. State-of-the-art reaction codes allow for evaluating the requirements of the detector. The specific needs that working with heavy ions impose about material thicknesses are solved with new design concepts. We built prototypes and investigated the behaviour of RPCs tested with relativistic heavy ions. We measured the efficiency and streamer presence for ions with atomic numbers up to 38. Electron beams were used to study the timing capabilities of the prototypes.

The split torque gearbox is a practical solution to the transmission of high torques with the lowest possible weight. In this article, we perform the mathematics necessary to calculate possible solutions for the simultaneous meshing of four wheels on the basis of geometric conditioning factors. These calculations will be illustrated by numerical applications. Finally, particular cases of planetary gearboxes and a gearbox with equal idler pinions are studied, with the conclusion that their application is appropriate in specific conditions.

The granite processing sector of the northwest of Spain handles many varieties of granite with specific technical and aesthetic properties that command different prices in the natural stone market. Hence, correct granite identification and classification from the outset of processing to the end-product stage optimizes the management and control of stocks of granite slabs and tiles and facilitates the operation of traceability systems. We describe a methodology for automatically identifying granite varieties by processing spectral information captured by a spectrophotometer at various stages of processing using functional machine learning techniques.

We evaluate the appropriateness of applying a functional rather than the typical vectorial approach to a pattern recognition problem. The problem to be resolved was to construct an online system for controlling wood-pulp chip granulometry quality for implementation in a wood-pulp factory. A functional linear model and a functional logistic model were used to classify the hourly empirical distributions of wood-chip thicknesses estimated on the basis of images produced by a 3D laser scanner. The results obtained using these functional techniques were compared to the results of their vectorial counterparts and support vector machines, whose input consisted of several statistics of the hourly empirical distribution. We conclude that the empirical distributions have sufficiently rich functional traits so as to permit the pattern recognition process to benefit from the functional representation.

We describe a real-time quality control system for wood chips using a 3D laser scanner. The work evaluates the appropriateness of applying a functional rather than the typical vectorial approach to a pattern recognition problem. The problem to be resolved was to construct an online system for controlling wood-pulp chip granulometry quality for implementation in a wood-pulp factory. A functional linear model and a functional logistic model were used to classify the hourly empirical distributions of wood-chip thicknesses estimated on the basis of images produced by a 3D laser scanner. The results obtained using these functional techniques were compared to the results of their vectorial counterparts and support vector machines, whose input consisted of several statistics of the hourly empirical distribution. We conclude that the empirical distributions have sufficiently rich functional traits so as to permit the pattern recognition process to benefit from the functional representation.

In this work we describe the mechanical design of a time-of-flight detector based on strip RPCs dedicated to measure relativistic heavy ions. The proposed design includes innovative solutions which meet the specific requirements to work with ions. The proposal is based on the results of the previous R&D program to build prototypes to test designs, materials and construction solutions, complemented by tests with relativistic ion beams. The first module of the detector has been built to be studied and characterized under beam conditions.