Path Approximation Strategies for Robot Manufacturing: A Preliminary Experimental Evaluation

Industrial Robots (IRs) are increasingly adopted for material subtraction or deposition functions owing to their advantages over machine tools, like cost-effectiveness and versatility. Unfortunately, the development of efficient robot manufacturing processes still faces unsolved issues related to the IRs poor positioning accuracy and to the tool path generation process. Novel engineering methods and tools are needed for CAD based programming of accurate paths and continuous robot motions to obtain the required manufacturing quality and tolerances. Within this context, to achieve smoothness along the tool path formed by linear G-code segments, the IR controllers’ approximation strategies, summarily reported in the manufacturer’s manuals, must be considered. The aim of this paper is to present the preliminary work carried out to identify the approximation algorithms of a Kuka IR when executing linear moves. An experimental study is conducted by varying the controller settings and the maximum translational velocity. The robot behavior has been acquired thanks to the controller tracing function and then processed to yield relations readily employable for the interpretation of G-Code commands and the subsequent generation of proper robot motion instructions. The obtained formulas allow to accurately predict the robot geometric path and kinematics within the corner transition between two linear segments

Path Approximation Strategies for Robot Manufacturing: A Preliminary Experimental Evaluation

Industrial Robots (IRs) are increasingly adopted for material subtraction or deposition functions owing to their advantages over machine tools, like cost-effectiveness and versatility. Unfortunately, the development of efficient robot manufacturing processes still faces unsolved issues related to the IRs poor positioning accuracy and to the tool path generation process. Novel engineering methods and tools are needed for CAD based programming of accurate paths and continuous robot motions to obtain the required manufacturing quality and tolerances. Within this context, to achieve smoothness along the tool path formed by linear G-code segments, the IR controllers’ approximation strategies, summarily reported in the manufacturer’s manuals, must be considered. The aim of this paper is to present the preliminary work carried out to identify the approximation algorithms of a Kuka IR when executing linear moves. An experimental study is conducted by varying the controller settings and the maximum translational velocity. The robot behavior has been acquired thanks to the controller tracing function and then processed to yield relations readily employable for the interpretation of G-Code commands and the subsequent generation of proper robot motion instructions. The obtained formulas allow to accurately predict the robot geometric path and kinematics within the corner transition between two linear segments

The introduction of a bi-adaptive smoothed particle hydrodynamics formulation beneficial for machining process simulations

Machining is one of the most important industrial production processes and, therefore, it is of great interest to have a tool suitable to describe the behavior of a real machined workpiece in a simulation. Due to its meshless nature, the Smoothed Particle Hydrodynamics (SPH) method is a quite promising, but frequently not well known alternative to mesh-based techniques, with the Finite Element Method leading the way, when simulating cutting processes. In order to demonstrate this, we present a broad variety of simulation results generated by our enhanced, bi-adaptive SPH cutting algorithm and evaluate them with respect to experimental data. But not only the final results, also the way how to extend the basic SPH code for solids to two adaptive components in order to meet the special requirements demanded by the cutting scenario, the ASPH extension and a newly developed refinement algorithm, is presented

Smooth Trajectory Generation for Machine Tools and Industrial Robots

This thesis presents accurate and time-optimal smooth reference trajectory generation techniques for manufacturing equipment such as high-speed machine tools (MT) and industrial robots (IR). Typical machining tool-paths for MTs and IRs are defined as a series of discrete linear moves. Although Point-to-Point (P2P) feed motion can be generated by interpolating each linear segment with high-order velocity profiles, the continuous and accurate transition between consecutive segments is necessary to realize a non-stop contouring motion for efficient manufacturing. To generate continuous feed motion along sharp cornered tool-paths, most numerical control (NC) systems blend (smooth) corners locally using various curves and splines. The feed (speed) is reduced around the blend sections so that the motion system’s kinematic limits are respected. This thesis proposes 2 novel techniques to enable modern MT and IR to generate non-stop rapid motion along discrete tool-paths. Firstly, a Kinematic Corner Smoothing (KCS) technique has been proposed to generate time-optimal (minimum time) motion trajectories in a real-time within axis kinematic limits. A novel real-time interpolation technique based on Finite Impulse Response (FIR) filtering has also been proposed to suppress residual vibrations for high positioning accuracy of machine tools and motion systems as well. These two techniques are tailored for Cartesian structured motion systems such as 2-3 axis machine tools. Finally, a decoupled FIR filtering technique has been developed to synchronously interpolate tool position and orientation for accurate motion generation for 5-axis MTs and IRs. These techniques are computationally lightweight and suitable for real-time implementation on modern NC systems. Simulation and experimental validation on Cartesian and 5-axis machine tools are presented to validate the effectiveness of the developed algorithms to interpolate along with discrete commands for high-speed and high-accuracy motion

Process dependent path planning for machining with industrial robots

The use of industrial robots in machining operations, such as milling, is an area of growing interest due to potential workflow and efficiency benefits. However, the inherent mechanical design of robot manipulators results in low stiffness and easy-to-excite dynamics when compared to the traditionally used \gls{cnc} machines. While research exists to compensate for deficiencies in robot manipulators, such as trajectory planning, online and offline error compensation, no integrated solution combining process-force compensation, robotic trajectory planning, and online error compensation exists, as would be required for industrial settings. This thesis introduces a deflection-limited trajectory planning algorithm for curvilinear slotting and linear peripheral milling cuts. The research purpose is to develop a solution involving a variable feed rate trajectory that limits the deflection-induced part errors when milling with an industrial robot. Thus, given a set of points to be approximated into a path, the methodology in this thesis generates a process-aware trajectory in which feed-rate has been adjusted to meet a user-specified deflection limit. The trajectory is formatted to be compatible with a closed-loop feedback and communication system with the industrial robot. Experiments are conducted using a large (range of 2855 mm), industrial robot milling system controlled by a closed-loop, laser tracker feedback system. Experimental data supports that the deflection-limited variable feed rate trajectory provides better part accuracy and surface roughness than the constant feed rate case. Furthermore, the variable feed rate trajectory executed by the closed-loop system maintains better positional accuracy than the open-loop, native robot controller using native motion types. Thus, the merit of a process dependent trajectory planner is argued, and future work for improvements and use-case generalization is suggested.M.S

A Parallel Solution Adaptive Implementation of the Direct Simulation Monte Carlo Method

This thesis deals with the direct simulation Monte Carlo (DSMC) method of analysing gas flows. The DSMC method was initially proposed as a method for predicting rarefied flows where the Navier-Stokes equations are inaccurate. It has now been extended to near continuum flows. The method models gas flows using simulation molecules which represent a large number of real molecules in a probabilistic simulation to solve the Boltzmann equation. Molecules are moved through a simulation of physical space in a realistic manner that is directly coupled to physical time such that unsteady flow characteristics are modelled. Intermolecular collisions and moleculesurface collisions are calculated using probabilistic, phenomenological models. The fundamental assumption of the DSMC method is that the molecular movement and collision phases can be decoupled over time periods that are smaller than the mean collision time. Two obstacles to the wide spread use of the DSMC method as an engineering tool are in the areas of simulation configuration, which is the configuration of the simulation parameters to provide a valid solution, and the time required to obtain a solution. For complex problems, the simulation will need to be run multiple times, with the simulation configuration being modified between runs to provide an accurate solution for the previous run's results, until the solution converges. This task is time consuming and requires the user to have a good understanding of the DSMC method. Furthermore, the computational resources required by a DSMC simulation increase rapidly as the simulation approaches the continuum regime. Similarly, the computational requirements of three-dimensional problems are generally two orders of magnitude more than two-dimensional problems. These large computational requirements significantly limit the range of problems that can be practically solved on an engineering workstation or desktop computer. The first major contribution of this thesis is in the development of a DSMC implementation that automatically adapts the simulation. Rather than modifying the simulation configuration between solution runs, this thesis presents the formulation of algorithms that allow the simulation configuration to be automatically adapted during a single run. These adaption algorithms adjust the three main parameters that effect the accuracy of a DSMC simulation, namely the solution grid, the time step and the simulation molecule number density. The second major contribution extends the parallelisation of the DSMC method. The implementation developed in this thesis combines the capability to use a cluster of computers to increase the maximum size of problem that can be solved while simultaneously allowing excess computational resources to decrease the total solution time. Results are presented to verify the accuracy of the underlying DSMC implementation, the utility of the solution adaption algorithms and the efficiency of the parallelisation implementation

A Parallel Solution Adaptive Implementation of the Direct Simulation Monte Carlo Method

This thesis deals with the direct simulation Monte Carlo (DSMC) method of analysing gas flows. The DSMC method was initially proposed as a method for predicting rarefied flows where the Navier-Stokes equations are inaccurate. It has now been extended to near continuum flows. The method models gas flows using simulation molecules which represent a large number of real molecules in a probabilistic simulation to solve the Boltzmann equation. Molecules are moved through a simulation of physical space in a realistic manner that is directly coupled to physical time such that unsteady flow characteristics are modelled. Intermolecular collisions and moleculesurface collisions are calculated using probabilistic, phenomenological models. The fundamental assumption of the DSMC method is that the molecular movement and collision phases can be decoupled over time periods that are smaller than the mean collision time. Two obstacles to the wide spread use of the DSMC method as an engineering tool are in the areas of simulation configuration, which is the configuration of the simulation parameters to provide a valid solution, and the time required to obtain a solution. For complex problems, the simulation will need to be run multiple times, with the simulation configuration being modified between runs to provide an accurate solution for the previous run's results, until the solution converges. This task is time consuming and requires the user to have a good understanding of the DSMC method. Furthermore, the computational resources required by a DSMC simulation increase rapidly as the simulation approaches the continuum regime. Similarly, the computational requirements of three-dimensional problems are generally two orders of magnitude more than two-dimensional problems. These large computational requirements significantly limit the range of problems that can be practically solved on an engineering workstation or desktop computer. The first major contribution of this thesis is in the development of a DSMC implementation that automatically adapts the simulation. Rather than modifying the simulation configuration between solution runs, this thesis presents the formulation of algorithms that allow the simulation configuration to be automatically adapted during a single run. These adaption algorithms adjust the three main parameters that effect the accuracy of a DSMC simulation, namely the solution grid, the time step and the simulation molecule number density. The second major contribution extends the parallelisation of the DSMC method. The implementation developed in this thesis combines the capability to use a cluster of computers to increase the maximum size of problem that can be solved while simultaneously allowing excess computational resources to decrease the total solution time. Results are presented to verify the accuracy of the underlying DSMC implementation, the utility of the solution adaption algorithms and the efficiency of the parallelisation implementation

Contributions to improve the technologies supporting unmanned aircraft operations

Mención Internacional en el título de doctorUnmanned Aerial Vehicles (UAVs), in their smaller versions known as drones, are becoming increasingly important in today's societies. The systems that make them up present a multitude of challenges, of which error can be considered the common denominator. The perception of the environment is measured by sensors that have errors, the models that interpret the information and/or define behaviors are approximations of the world and therefore also have errors. Explaining error allows extending the limits of deterministic models to address real-world problems. The performance of the technologies embedded in drones depends on our ability to understand, model, and control the error of the systems that integrate them, as well as new technologies that may emerge. Flight controllers integrate various subsystems that are generally dependent on other systems. One example is the guidance systems. These systems provide the engine's propulsion controller with the necessary information to accomplish a desired mission. For this purpose, the flight controller is made up of a control law for the guidance system that reacts to the information perceived by the perception and navigation systems. The error of any of the subsystems propagates through the ecosystem of the controller, so the study of each of them is essential. On the other hand, among the strategies for error control are state-space estimators, where the Kalman filter has been a great ally of engineers since its appearance in the 1960s. Kalman filters are at the heart of information fusion systems, minimizing the error covariance of the system and allowing the measured states to be filtered and estimated in the absence of observations. State Space Models (SSM) are developed based on a set of hypotheses for modeling the world. Among the assumptions are that the models of the world must be linear, Markovian, and that the error of their models must be Gaussian. In general, systems are not linear, so linearization are performed on models that are already approximations of the world. In other cases, the noise to be controlled is not Gaussian, but it is approximated to that distribution in order to be able to deal with it. On the other hand, many systems are not Markovian, i.e., their states do not depend only on the previous state, but there are other dependencies that state space models cannot handle. This thesis deals a collection of studies in which error is formulated and reduced. First, the error in a computer vision-based precision landing system is studied, then estimation and filtering problems from the deep learning approach are addressed. Finally, classification concepts with deep learning over trajectories are studied. The first case of the collection xviiistudies the consequences of error propagation in a machine vision-based precision landing system. This paper proposes a set of strategies to reduce the impact on the guidance system, and ultimately reduce the error. The next two studies approach the estimation and filtering problem from the deep learning approach, where error is a function to be minimized by learning. The last case of the collection deals with a trajectory classification problem with real data. This work completes the two main fields in deep learning, regression and classification, where the error is considered as a probability function of class membership.Los vehículos aéreos no tripulados (UAV) en sus versiones de pequeño tamaño conocidos como drones, van tomando protagonismo en las sociedades actuales. Los sistemas que los componen presentan multitud de retos entre los cuales el error se puede considerar como el denominador común. La percepción del entorno se mide mediante sensores que tienen error, los modelos que interpretan la información y/o definen comportamientos son aproximaciones del mundo y por consiguiente también presentan error. Explicar el error permite extender los límites de los modelos deterministas para abordar problemas del mundo real. El rendimiento de las tecnologías embarcadas en los drones, dependen de nuestra capacidad de comprender, modelar y controlar el error de los sistemas que los integran, así como de las nuevas tecnologías que puedan surgir. Los controladores de vuelo integran diferentes subsistemas los cuales generalmente son dependientes de otros sistemas. Un caso de esta situación son los sistemas de guiado. Estos sistemas son los encargados de proporcionar al controlador de los motores información necesaria para cumplir con una misión deseada. Para ello se componen de una ley de control de guiado que reacciona a la información percibida por los sistemas de percepción y navegación. El error de cualquiera de estos sistemas se propaga por el ecosistema del controlador siendo vital su estudio. Por otro lado, entre las estrategias para abordar el control del error se encuentran los estimadores en espacios de estados, donde el filtro de Kalman desde su aparición en los años 60, ha sido y continúa siendo un gran aliado para los ingenieros. Los filtros de Kalman son el corazón de los sistemas de fusión de información, los cuales minimizan la covarianza del error del sistema, permitiendo filtrar los estados medidos y estimarlos cuando no se tienen observaciones. Los modelos de espacios de estados se desarrollan en base a un conjunto de hipótesis para modelar el mundo. Entre las hipótesis se encuentra que los modelos del mundo han de ser lineales, markovianos y que el error de sus modelos ha de ser gaussiano. Generalmente los sistemas no son lineales por lo que se realizan linealizaciones sobre modelos que a su vez ya son aproximaciones del mundo. En otros casos el ruido que se desea controlar no es gaussiano, pero se aproxima a esta distribución para poder abordarlo. Por otro lado, multitud de sistemas no son markovianos, es decir, sus estados no solo dependen del estado anterior, sino que existen otras dependencias que los modelos de espacio de estados no son capaces de abordar. Esta tesis aborda un compendio de estudios sobre los que se formula y reduce el error. En primer lugar, se estudia el error en un sistema de aterrizaje de precisión basado en visión por computador. Después se plantean problemas de estimación y filtrado desde la aproximación del aprendizaje profundo. Por último, se estudian los conceptos de clasificación con aprendizaje profundo sobre trayectorias. El primer caso del compendio estudia las consecuencias de la propagación del error de un sistema de aterrizaje de precisión basado en visión artificial. En este trabajo se propone un conjunto de estrategias para reducir el impacto sobre el sistema de guiado, y en última instancia reducir el error. Los siguientes dos estudios abordan el problema de estimación y filtrado desde la perspectiva del aprendizaje profundo, donde el error es una función que minimizar mediante aprendizaje. El último caso del compendio aborda un problema de clasificación de trayectorias con datos reales. Con este trabajo se completan los dos campos principales en aprendizaje profundo, regresión y clasificación, donde se plantea el error como una función de probabilidad de pertenencia a una clase.I would like to thank the Ministry of Science and Innovation for granting me the funding with reference PRE2018-086793, associated to the project TEC2017-88048-C2-2-R, which provide me the opportunity to carry out all my PhD. activities, including completing an international research internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Antonio Berlanga de Jesús.- Secretario: Daniel Arias Medina.- Vocal: Alejandro Martínez Cav

Magnetic Actuators and Suspension for Space Vibration Control

The research on microgravity vibration isolation performed at the University of Virginia is summarized. This research on microgravity vibration isolation was focused in three areas: (1) the development of new actuators for use in microgravity isolation; (2) the design of controllers for multiple-degree-of-freedom active isolation; and (3) the construction of a single-degree-of-freedom test rig with umbilicals. Described are the design and testing of a large stroke linear actuator; the conceptual design and analysis of a redundant coarse-fine six-degree-of-freedom actuator; an investigation of the control issues of active microgravity isolation; a methodology for the design of multiple-degree-of-freedom isolation control systems using modern control theory; and the design and testing of a single-degree-of-freedom test rig with umbilicals

Applied Mathematics to Mechanisms and Machines

This book brings together all 16 articles published in the Special Issue "Applied Mathematics to Mechanisms and Machines" of the MDPI Mathematics journal, in the section “Engineering Mathematics”. The subject matter covered by these works is varied, but they all have mechanisms as the object of study and mathematics as the basis of the methodology used. In fact, the synthesis, design and optimization of mechanisms, robotics, automotives, maintenance 4.0, machine vibrations, control, biomechanics and medical devices are among the topics covered in this book. This volume may be of interest to all who work in the field of mechanism and machine science and we hope that it will contribute to the development of both mechanical engineering and applied mathematics