5 resultados para Variable Structure Control
em AMS Tesi di Laurea - Alm@DL - Università di Bologna
Resumo:
Laser Shock Peening (LSP) is a surface enhancement treatment which induces a significant layer of beneficial compressive residual stresses of up to several mm underneath the surface of metal components in order to improve the detrimental effects of the crack growth behavior rate in it. The aim of this thesis is to predict the crack growth behavior in metallic specimens with one or more stripes which define the compressive residual stress area induced by the Laser Shock Peening treatment. The process was applied as crack retardation stripes perpendicular to the crack propagation direction with the object of slowing down the crack when approaching the peened stripes. The finite element method has been applied to simulate the redistribution of stresses in a cracked model when it is subjected to a tension load and to a compressive residual stress field, and to evaluate the Stress Intensity Factor (SIF) in this condition. Finally, the Afgrow software is used to predict the crack growth behavior of the component following the Laser Shock Peening treatment and to detect the improvement in the fatigue life comparing it to the baseline specimen. An educational internship at the “Research & Technologies Germany – Hamburg” department of AIRBUS helped to achieve knowledge and experience to write this thesis. The main tasks of the thesis are the following: •To up to date Literature Survey related to “Laser Shock Peening in Metallic Structures” •To validate the FE model developed against experimental measurements at coupon level •To develop design of crack growth slowdown in Centered Cracked Tension specimens based on residual stress engineering approach using laser peened strip transversal to the crack path •To evaluate the Stress Intensity Factor values for Centered Cracked Tension specimens after the Laser Shock Peening treatment via Finite Element Analysis •To predict the crack growth behavior in Centered Cracked Tension specimens using as input the SIF values evaluated with the FE simulations •To validate the results by means of experimental tests
Resumo:
In collaboration with G.D. SpA I attended an internship with the purpose of developing a filter for the position control of industrial machines during testing and maintenance operations. The filter elaborates a signal in position provided by an electonic handwheel, in order to enable the application to be controlled with a signal in velocity with arbitrarily dynamics chosen during the design phase. Limiting the dynamics of the filter provide a more stable and less demanding reference trajectory which reduce the vibrations and tracking errors of the motor controlled by it. It also prevents misusages of the handwheel from the technician which could end up in harmful interferences between the mechanical parts moved by the handwheel.
Resumo:
In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.
Resumo:
The work of this thesis is on the implementation of a variable stiffness joint antagonistically actuated by a couple of twisted-string actuator (TSA). This type of joint is possible to be applied in the field of robotics, like UB Hand IV (the anthropomorphic robotic hand developed by University of Bologna). The purposes of the activities are to build the joint dynamic model and simultaneously control the position and stiffness. Three different control approaches (Feedback linearization, PID, PID+Feedforward) are proposed and validated in simulation. To improve the properties of joint stiffness, a joint with elastic element is taken into account and discussed. To the end, the experimental setup that has been developed for the experimental validation of the proposed control approaches.
Resumo:
The blue shark, Prionace glauca, is one of the most vagile shark species worldwide distributed. The particular body shape allows blue sharks make transoceanic movements, leading to a circumglobal distribution. Due to its reproductive cycle, an extraordinarily high number of specimens is globally registered but, even if it is still a major bycatch of longline fishery rather than a commercial target, it is characterized by a high vulnerability. In this perspective it is important to increase the amount of informations regarding its population extent in the different worldwide areas, evaluating the possible phylogeographic patterns between different locations. This study, included in the "MedBlueSGen" European project, aims exactly at filling a gap in knowledges regarding the genetic population structure of the Mediterranean blue sharks, which has never been investigated before, with a comparison with the North-Eastern Atlantic blue shark population. To reach this objective, we used a dataset of samples from different Mediterranean areas implementing it with some samples from North-Eastern Atlantic. Analyzing the variability of the two mitochondrial markers control region and cytochrome b, with the design of new species-specific primer pairs, we assessed the mitochondrial genetic structure of Mediterranean and North-Eastern Atlantic samples, focusing on the analysis of their possible connectivity, and we tried to reconstruct their demographic history and population size. Data analyses highlighted the absence of a genetic structuring within the Mediterranean and among it and North-Eastern Atlantic, suggesting that the Strait of Gibraltar doesn't represent a phylogeographic barrier. These results are coherent to what has been found in similar investigations on other worldwide blue shark populations. Analysis of the historical demographic trend revealed a general stable pattern for the cytochrome-b and a slightly population expansion for the control region marker.