7,349 research outputs found
Eco-efficient process based on conventional machining as an alternative technology to chemical milling of aeronautical metal skin panels
El fresado químico es un proceso diseñado para la reducción de peso de pieles metálicas que, a
pesar de los problemas medioambientales asociados, se utiliza en la industria aeronáutica desde los
años 50. Entre sus ventajas figuran el cumplimiento de las estrictas tolerancias de diseño de piezas
aeroespaciales y que pese a ser un proceso de mecanizado, no induce tensiones residuales. Sin
embargo, el fresado químico es una tecnología contaminante y costosa que tiende a ser sustituida.
Gracias a los avances realizados en el mecanizado, la tecnología de fresado convencional permite
alcanzar las tolerancias requeridas siempre y cuando se consigan evitar las vibraciones y la flexión
de la pieza, ambas relacionadas con los parámetros del proceso y con los sistemas de utillaje
empleados.
Esta tesis analiza las causas de la inestabilidad del corte y la deformación de las piezas a través
de una revisión bibliográfica que cubre los modelos analíticos, las técnicas computacionales y las
soluciones industriales en estudio actualmente. En ella, se aprecia cómo los modelos analíticos y las
soluciones computacionales y de simulación se centran principalmente en la predicción off-line de
vibraciones y de posibles flexiones de la pieza. Sin embargo, un enfoque más industrial ha llevado al
diseño de sistemas de fijación, utillajes, amortiguadores basados en actuadores, sistemas de rigidez
y controles adaptativos apoyados en simulaciones o en la selección estadística de parámetros.
Además se han desarrollado distintas soluciones CAM basadas en la aplicación de gemelos virtuales.
En la revisión bibliográfica se han encontrado pocos documentos relativos a pieles y suelos
delgados por lo que se ha estudiado experimentalmente el efecto de los parámetros de corte en su
mecanizado. Este conjunto de experimentos ha demostrado que, pese a usar un sistema que
aseguraba la rigidez de la pieza, las pieles se comportaban de forma diferente a un sólido rígido en
términos de fuerzas de mecanizado cuando se utilizaban velocidades de corte cercanas a la alta
velocidad. También se ha verificado que todas las muestras mecanizadas entraban dentro de
tolerancia en cuanto a la rugosidad de la pieza. Paralelamente, se ha comprobado que la correcta
selección de parámetros de mecanizado puede reducir las fuerzas de corte y las tolerancias del
proceso hasta un 20% y un 40%, respectivamente. Estos datos pueden tener aplicación industrial en
la simplificación de los sistemas de amarre o en el incremento de la eficiencia del proceso.
Este proceso también puede mejorarse incrementando la vida de la herramienta al utilizar
fluidos de corte. Una correcta lubricación puede reducir la temperatura del proceso y las tensiones
residuales inducidas a la pieza. Con este objetivo, se han desarrollado diferentes lubricantes, basados
en el uso de líquidos iónicos (IL) y se han comparado con el comportamiento tribológico del par de
contacto en seco y con una taladrina comercial. Los resultados obtenidos utilizando 1 wt% de los
líquidos iónicos en un tribómetro tipo pin-on-disk demuestran que el IL no halogenado reduce
significativamente el desgaste y la fricción entre el aluminio, material a mecanizar, y el carburo de
tungsteno, material de la herramienta, eliminando casi toda la adhesión del aluminio sobre el pin, lo
que puede incrementar considerablemente la vida de la herramienta.Chemical milling is a process designed to reduce the weight of metals skin panels. This process
has been used since 1950s in the aerospace industry despite its environmental concern. Among its
advantages, chemical milling does not induce residual stress and parts meet the required tolerances.
However, this process is a pollutant and costly technology. Thanks to the last advances in
conventional milling, machining processes can achieve similar quality results meanwhile vibration
and part deflection are avoided. Both problems are usually related to the cutting parameters and the
workholding.
This thesis analyses the causes of the cutting instability and part deformation through a literature
review that covers analytical models, computational techniques and industrial solutions. Analytics
and computational solutions are mainly focused on chatter and deflection prediction and industrial
approaches are focused on the design of workholdings, fixtures, damping actuators, stiffening
devices, adaptive control systems based on simulations and the statistical parameters selection, and
CAM solutions combined with the use of virtual twins applications.
In this literature review, few research works about thin-plates and thin-floors is found so the
effect of the cutting parameters is also studied experimentally. These experiments confirm that even
using rigid workholdings, the behavior of the part is different to a rigid body at high speed machining.
On the one hand, roughness values meet the required tolerances under every set of the tested
parameters. On the other hand, a proper parameter selection reduces the cutting forces and process
tolerances by up to 20% and 40%, respectively. This fact can be industrially used to simplify
workholding and increase the machine efficiency.
Another way to improve the process efficiency is to increase tool life by using cutting fluids.
Their use can also decrease the temperature of the process and the induced stresses. For this purpose,
different water-based lubricants containing three types of Ionic Liquids (IL) are compared to dry and
commercial cutting fluid conditions by studying their tribological behavior. Pin on disk tests prove
that just 1wt% of one of the halogen-free ILs significantly reduces wear and friction between both
materials, aluminum and tungsten carbide. In fact, no wear scar is noticed on the ball when one of
the ILs is used, which, therefore, could considerably increase tool life
Inertial Load Compensation by a Model Spinal Circuit During Single Joint Movement
Office of Naval Research (N00014-92-J-1309); CONACYT (Mexico) (63462
Proprioceptive Learning with Soft Polyhedral Networks
Proprioception is the "sixth sense" that detects limb postures with motor
neurons. It requires a natural integration between the musculoskeletal systems
and sensory receptors, which is challenging among modern robots that aim for
lightweight, adaptive, and sensitive designs at a low cost. Here, we present
the Soft Polyhedral Network with an embedded vision for physical interactions,
capable of adaptive kinesthesia and viscoelastic proprioception by learning
kinetic features. This design enables passive adaptations to omni-directional
interactions, visually captured by a miniature high-speed motion tracking
system embedded inside for proprioceptive learning. The results show that the
soft network can infer real-time 6D forces and torques with accuracies of
0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also
incorporate viscoelasticity in proprioception during static adaptation by
adding a creep and relaxation modifier to refine the predicted results. The
proposed soft network combines simplicity in design, omni-adaptation, and
proprioceptive sensing with high accuracy, making it a versatile solution for
robotics at a low cost with more than 1 million use cycles for tasks such as
sensitive and competitive grasping, and touch-based geometry reconstruction.
This study offers new insights into vision-based proprioception for soft robots
in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International
Journal of Robotics Research for revie
Decentralized adaptive partitioned approximation control of high degrees-of-freedom robotic manipulators considering three actuator control modes
International audiencePartitioned approximation control is avoided in most decentralized control algorithms; however, it is essential to design a feedforward control term for improving the tracking accuracy of the desired references. In addition, consideration of actuator dynamics is important for a robot with high-velocity movement and highly varying loads. As a result, this work is focused on decentralized adaptive partitioned approximation control for complex robotic systems using the orthogonal basis functions as strong approximators. In essence, the partitioned approximation technique is intrinsically decentralized with some modifications. Three actuator control modes are considered in this study: (i) a torque control mode in which the armature current is well controlled by a current servo amplifier and the motor torque/current constant is known, (ii) a current control mode in which the torque/current constant is unknown, and (iii) a voltage control mode with no current servo control being available. The proposed decentralized control law consists of three terms: the partitioned approximation-based feedforward term that is necessary for precise tracking, the high gain-based feedback term, and the adaptive sliding gain-based term for compensation of modeling error. The passivity property is essential to prove the stability of local stability of the individual subsystem with guaranteed global stability. Two case studies are used to prove the validity of the proposed controller: a two-link manipulator and a six-link biped robot
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Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control
Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part
Benchmarking Cerebellar Control
Cerebellar models have long been advocated as viable models
for robot dynamics control. Building on an increasing insight
in and knowledge of the biological cerebellum, many models have been
greatly refined, of which some computational models have emerged
with useful properties with respect to robot dynamics control.
Looking at the application side, however, there is a totally different
picture. Not only is there not one robot on the market which uses
anything remotely connected with cerebellar control, but even in
research labs most testbeds for cerebellar models are restricted to
toy problems. Such applications hardly ever exceed the complexity of
a 2 DoF simulated robot arm; a task which is hardly representative for
the field of robotics, or relates to realistic applications.
In order to bring the amalgamation of the two fields forwards, we
advocate the use of a set of robotics benchmarks, on which existing
and new computational cerebellar models can be comparatively tested.
It is clear that the traditional approach to solve robotics dynamics
loses ground with the advancing complexity of robotic structures;
there is a desire for adaptive methods which can compete as traditional
control methods do for traditional robots.
In this paper we try to lay down the successes and problems in the
fields of cerebellar modelling as well as robot dynamics control.
By analyzing the common ground, a set of benchmarks is suggested
which may serve as typical robot applications for cerebellar models
Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches
Thin-wall parts are common in the aeronautical sector. However, their machining presents
serious challenges such as vibrations and part deflections. To deal with these challenges, di erent
approaches have been followed in recent years. This work presents the state of the art of thin-wall
light-alloy machining, analyzing the problems related to each type of thin-wall parts, exposing the
causes of both instability and deformation through analytical models, summarizing the computational
techniques used, and presenting the solutions proposed by di erent authors from an industrial point
of view. Finally, some further research lines are proposed
From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems
Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section
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