Industrial Robot Joint Research Articles

Flatness Based Control of an Industrial Robot Joint Using Secondary Encoders

Due to their compliant structure, industrial robots without precision-enhancing measures are only to a limited extent suitable for machining applications. Apart from structural, thermal and bearing deformations, the main cause for compliant structure is backlash of transmission drives. This paper proposes a method to improve trajectory tracking accuracy by using secondary encoders and applying a feedback and a flatness based feed forward control strategy. For this purpose, a novel nonlinear, continuously differentiable dynamical model of a flexible robot joint is presented. The robot joint is modeled as a two-mass oscillator with pose-dependent inertia, nonlinear friction and nonlinear stiffness, including backlash. A flatness based feed forward control is designed to improve the guiding behaviour and a feedback controller, based on secondary encoders, is implemented for disturbance compensation. Using Automatic Differentiation, the nonlinear feed forward controller can be computed in a few microseconds online. Finally, the proposed algorithms are evaluated in simulations and experimentally on a real KUKA Quantec KR300 Ultra SE.

Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network

Industrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The majority of the previous research on industrial robots health monitoring is focused on monitoring of a limited number of faults, such as backlash in gears, but does not diagnose the other gear and bearing faults. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults that could be progressed in the bearings of industrial robot joints, such as inner/outer race bearing faults, using vibration signal analysis. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded faults in the robot.

A Methodology and Experimental Implementation for Industrial Robot Health Assessment via Torque Signature Analysis

This manuscript focuses on methodological and technological advances in the field of health assessment and predictive maintenance for industrial robots. We propose a non-intrusive methodology for industrial robot joint health assessment. Torque sensor data is used to create a digital signature given a defined trajectory and load combination. The signature of each individual robot is later used to diagnose mechanical deterioration. We prove the robustness and reliability of the methodology in a real industrial use case scenario. Then, an in depth mechanical inspection is carried out in order to identify the root cause of the failure diagnosed in this article. The proposed methodology is useful for medium and long term health assessment for industrial robots working in assembly lines, where years of almost uninterrupted work can cause irreversible damage.

Numerical Analysis on Magnetic Field and Torque of a Disk-Type Nutation Magnetic Drive

Nutation reducers have recently attracted increasing attention, as they are used in important components of industrial robot joints. A novel disk-type nutation magnetic drive (NMD), based on the nutation gearing principle, is proposed in this article. Subsequently, a 3-D method for analyzing the magnetic field and predicting the torque of the disk-type NMD is presented. The 3-D analytical formula of the disk-type NMD is established on the basis of the equivalent current theory of permanent magnet, which is verified by the finite-element approach. Optimization design of the disk-type NMD is also carried out by applying the genetic algorithm. The results provide a reference for the design and optimization of the magnetic gears.

Tooth contact analysis with latent error of double circular-arc spiral bevel gears for industrial robot joint nutation drive

The influence of the cone vertex error and tilt error on the tooth contact trace of double circular-arc spiral bevel gears for industrial robot joint nutation drive has been studied in this paper. According to the meshing relationship between double circular-arc spiral bevel gear and crown gear, the meshing coordinate systems with latent error parameters of cone vertex error and tilt error were established, and the tooth profile equations of external and internal spiral bevel gears were deduced. According to the meshing equation and coordinate transformation, the contact trace equation of the tooth surface was established, and the influence of different latent error values on the location of the contact trace was solved and analyzed. The finite element loading contact simulation of the double circular-arc spiral bevel gears with different latent error values was carried out. The results of the simulation analysis and numerical analysis were basically consistent. The latent errors would cause noise and vibration of industrial robot joint nutation drive and affect the service life of double circular-arc spiral bevel gears.

Friction Model of Industrial Robot Joint with Temperature Correction by Example of KUKA KR10

The quality of industrial robots essentially depends on the properties of their kinematic couples. This research has involved conducting an experimental study of the friction torque in a joint of the KUKA KR10 industrial robot and building its model. It has been established that the largest impact on friction in the joint is caused by its axial load and velocity, as well as the temperature of the mechanism, which is generally not homogeneous. It is not possible to measure temperature fields in the joints of a serial industrial robot directly. This study has set forth a method to estimate friction torque taking into account the temperature factor indirectly. For this, we have used the motor temperature available for measuring in combination with special periodical motions, performed by the robot, during which we estimated the actual friction torque in the joint and calculated a temperature correction based on our findings.

Design and Dynamic Modeling of Variable Stiffness Joint Actuator Based on Archimedes Spiral

Traditional rigid actuators are not applicable to robot applications involving explosive actions. Solutions suggested by previous studies have mainly focused on the concept of stiffness variation, its design and verification, and the explosive actions of variable stiffness joint actuators. This paper proposes a variable stiffness actuator that features a leaf spring as the elastic element and a pair of disks with two Archimedes spiral grooves as the stiffness adjusting mechanism, maintaining $\phi 114\,\,\text {mm}\times 130$ mm overall dimensions and a full range (i.e., 127–2095 Nm/rad) of stiffness variation driven by a 7.2-W motor and, therefore, creating a competitive advantage in both compactness and energy saving capacity. The proposed actuator is intended as a dynamics model to predict the exact kicking speed under any given initial conditions of joint stiffness, kicking time, and load mass and for uses in the joints of industrial and/or service robots that frequently involve explosive actions.

The problem in Accuracy Compensation of Industrial Robot

Industrial robots are increasingly being used in automated production line with their high flexibility and low cost However the poor features of repeat positioning accuracy and absolute positioning accuracy of industrial robots have been a bottleneck restricting its application in higher accuracy situation Many scholarshave studied methods to improve the accuracy of robot But there is congenital defect for industrial robots when their accuracy is further improved by compensation and few people pay attention to this In this article the backlash of industrial robot joint is discussed and its impact on accuracy compensation is analyzed Finally the method of eliminating the backlash and further improving the robot s accuracy based on joint feedback is proposed

Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.

Design of a Wireless Sensor Node for Vibration Monitoring of Industrial Machinery

Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reducing the maintenance costs. Vibration signals analysis was extensively used for machines fault detection and diagnosis in various industrial applications, as it respond immediately to manifest itself if any change is appeared in the monitored machine. However, recent developments in electronics and computing have opened new horizons in the area of condition monitoring and have shown their practicality in fault detection and diagnosis processes. The main aim of using wireless embedded systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to design and develop an online condition monitoring system based on wireless embedded technology that can be used to detect and diagnose the most common faults in the transmission systems (gears and bearings) of an industrial robot joints using vibration signal analysis.

Industrial Robot Backlash Fault Diagnosis Based on Discrete Wavelet Transform and Artificial Neural Network

Industrial robots are commonplace in production systems and have long been used in order to improve productivity, quality and safety in automated manufacturing processes. An unforeseen robot stoppage due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. The ability to continuously monitor the status and condition of robots has become a research issue in recent years and is now receiving considerable attention. Thus, the main aim of this research is to develop an intelligent condition monitoring system to diagnose the most common faults (backlash) that could be progressed in the gearbox of industrial robot joints. For accurate fault diagnosis, time-frequency signal analysis based on the discrete wavelet transform (DWT) is adopted to extract the most salient features related to faults, and the artificial neural network (ANN) is used for faults classification. A data acquisition system based on National Instruments (NI) software and hardware was developed for robot vibration analysis and feature extraction. An experimental investigation was accomplished using the PUMA 560 robot. Firstly, vibration signals are captured from the robot when it is moving one joint cyclically. Then, by utilising the wavelet transform, signals are decomposed into multi-band frequency levels starting from higher to lower frequencies. For each of these levels the standard deviation feature is computed and used to design, train and test the proposed neural network. The developed system has showed high reliability in diagnosing several seeded backlash levels in the robot.

Developing an Efficient Calibration System for Joint Offset of Industrial Robots

Joint offset calibration is one of the most important methods to improve the positioning accuracy for industrial robots. This paper presents an efficient method to calibrate industrial robot joint offset. The proposed method mainly relies on a laser pointer mounted on the robot end-effector and a position sensitive device (PSD) located in the work space arbitrarily. A vision based control was employed to aid the laser beam shooting at the center of PSD surface from several initial robot postures. For each shoot, the laser beam was a line in space which can be determined by the robot joint angles and their offsets which were recorded when the laser beam was brought to the center of the PSD surface. Therefore, there are several lines in space parameterized by robot joint offsets only and all these lines were constrained by the same point, that is, the center of the PSD surface. Consequently, an optimization model was formulated and the Levenberg-Marquardt (LM) algorithm was employed to identify the joint offsets. The developed calibration system was implemented on an ABB industrial robot IRB1600 successfully. And the joint offsets of this robot can be calibrated within 6 minutes.

A Data-driven Method for Monitoring Systems that Operate Repetitively -Applications to Wear Monitoring in an Industrial Robot Joint

This paper presents a method for monitoring of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against a nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback-Leibler distance. The method is simple to implement and can be used without process interruption, in a batch manner. The method was developed with interests in industrial robotics where a repetitive behavior is commonly found. The problem of wear monitoring in a robot joint is studied. Real data from accelerated wear tests are considered. Promising results are achieved, where the method output shows a clear response to the wear increases.

Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties

AbstractThis paper considers the problem of wear estimation in a standard industrial robot joint. The effects of wear on the static friction of a robot joint are analyzed from experiments. An extended static friction model is proposed that explains changes related to joint speed, load, temperature and wear. Based on this model and static friction observations, a model-based wear estimator is proposed. The performance of the estimator under temperature uncertainties is found both by means of simulations and experiments on an industrial robot. Special attention is given to the analyses of the best speed region for wear estimation. As it is shown, the method can distinguish the effects of wear even under large temperature variations, opening up for the use of robust joint diagnosis for industrial robots.

산업용 로봇의 유연관절 제어기 설계: Part 2 - 진동억제 제어 및 게인스케듈링

Increasing requirements for the high quality of industrial robot performance made the vibration control issue very important because the vibration makes it difficult to achieve quick response of robot motion and may bring mechanical damage to the robot. This paper presents a vibration control solution for industrial robots which have flexible joints. The joint flexibility is modeled as a two-mass system. And we analyze the vibration problem of a classical P-PI controller when it used for the flexible joints of industrial robots. Then a state feedback controller is designed for vibration suppression of the two-mass system. Finally, a gain-scheduling method is designed for maintaining control performance in spite of the time-varying nature of each joint`s load side inertia. Simulation and experimental results show effective vibration suppression and uniform properties in overshoot in spite of the variation of load. The result of this study can be applied to the appropriate gain manipulation of many other mechatronic devices which have the two-mass system with varying load side inertia.

Stiffness constants identification methods for industrial robot joints

This paper discusses two methods for the identification of the stiffness constants of industrial robots joints. The first one requires free motion experiments and aims at the parametric identification of the linearized model of the joint. Some coasideratioas are worked out for a correct design of the experiment, mainly with respect to inertial coupling among links and to the effects of friction. The second method exploits the properties of the manipulator constrained by a rigid surface: in this condition it is possible to perturb one motor position set-point in such a way as to cause no motion of the structure. Measurements of the reaction force at the contact and of the motor position allow a simple estimation of the stiffness constant of the joint. Experimental results, gathered on the industrial robot COMAU SMART3S. are presented together with some comments on the comparison between the proposed methods.

Determination of elastodynamic errors in joints of industrial robots

SUMMARYThis paper presents a method for automatic forming and solving dynamic equations of motion of manipulator with elastic joints and rigid segments. For the minimal configuration of a cylindrical manipulator the numerical integration of these equations is performed, and vibrations in the joints are obtained as dynamic errors with respect to the prescribed trajectory.