scholarly journals Probabilistic information fusion to model the pose-dependent dynamics of milling robots

2020 ◽  
Vol 14 (4) ◽  
pp. 435-444
Author(s):  
Maximilian Busch ◽  
Florian Schnoes ◽  
Thomas Semm ◽  
Michael F. Zaeh ◽  
Birgit Obst ◽  
...  
Keyword(s):  
Experimental Data ◽  
Information Fusion ◽  
Dynamic Properties ◽  
Industrial Robot ◽  
Milling Process ◽  
Industrial Robots ◽  
Robot Dynamics ◽  
Data Driven Approach ◽  
Conventional Machine ◽  
Fusion Scheme

Abstract Conventional industrial robots are increasingly used for milling applications of large workpieces due to their workspace and their low investment costs in comparison to conventional machine tools. However, static deflections and dynamic instabilities during the milling process limit the efficiency and productivity of such robot-based milling systems. Since the pose-dependent dynamic properties of the industrial robot structures are notoriously difficult to model analytically, machine learning methods are recently gaining more and more popularity to derive system models from experimental data. In this publication, a modeling concept based on a modern information fusion scheme, fusing simulation and experimental data, is proposed. This approach provides a precise model of the robot’s pose-dependent structural dynamics and is validated for a one-dimensional variation of the robot pose. The results of two information fusion algorithms are compared with a conventional, data-driven approach and indicate a superior model accuracy regarding interpolation and extrapolation of the pose-dependent dynamics. The proposed approach enables decreasing the necessary amount of experimental data needed to assess the vibrational properties of the robot for a desired pose. Additionally, the concept is able to predict the robot dynamics at poses where experimental data is very costly to gather.

Industrial Robot Kinematics Parameter Identification

2014 ◽  
Vol 889-890 ◽  
pp. 1136-1143
Author(s):  
Yong Gui Zhang ◽  
Chen Rong Liu ◽  
Peng Liu
Keyword(s):  
Experimental Data ◽  
Genetic Algorithms ◽  
Parameter Identification ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Robot Kinematics ◽  
Kinematic Parameters ◽  
Unknown Parameters ◽  
Preliminary Measurement ◽  
Kinematics Modeling

For an industrial robots with unknown parameters, on the basis of preliminary measurement and data of the Cartesian and joints coordinates which are shown on the FlexPendant, the kinematic parameters is identified by using genetic algorithms and accurate kinematics modeling of the robot is established. Experimental data could prove the validity of this method.

NEURAL NETWORK BASED DYNAMIC CONTROLLERS FOR INDUSTRIAL ROBOTS

1995 ◽  
Vol 06 (03) ◽  
pp. 257-271
Author(s):  
SE-YOUNG OH ◽  
WEON-CHANG SHIN ◽  
HYO-GYU KIM
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Industrial Robot ◽  
Industrial Robots ◽  
System Stability ◽  
Robot Dynamics ◽  
Time Dynamic ◽  
Dynamic Controller ◽  
And Control

The industrial robot’s dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller’s excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

SAFETY AND RISK ASSESSMENT AT AUTOMATED WORKPLACE

2018 ◽  
pp. 78-84
Author(s):  
Marek Vagas
Keyword(s):  
Risk Assessment ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Added Value ◽  
Growing Up ◽  
Target Setting ◽  
Clear View ◽  
General Safety

Urgency of the research. Automated workplaces are growing up in present, especially with implementation of industrial robots with feasibility of various dispositions, where safety and risk assessment is considered as most important issues. Target setting. The protection of workers must be at the first place, therefore safety and risk assessment at automated workplaces is most important problematic, which had presented in this article Actual scientific researches and issues analysis. Actual research is much more focused at standard workplaces without industrial robots. So, missing of information from the field of automated workplaces in connection with various dispositions can be considered as added value of article. Uninvestigated parts of general matters defining. Despite to lot of general safety instructions in this area, still is missed clear view only at automated workplace with industrial robots. The research objective. The aim of article is to provide general instructions directly from the field of automated workplaces The statement of basic materials. For success realization of automated workplace is good to have a helping hand and orientation requirements needed for risk assessment at the workplace. Conclusions. The results published in this article increase the awareness and information of such automated workplaces, together with industrial robots. In addition, presented general steps and requirements helps persons for better realization of these types of workplaces, where major role takes an industrial robot. Our proposed solution can be considered as relevant base for risk assessment such workplaces with safety fences or light barriers.

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Chen Zhang ◽  
Zhuo Tang ◽  
Kenli Li ◽  
Jianzhong Yang ◽  
Li Yang
Keyword(s):  
Time Series ◽  
Control System ◽  
Force Control ◽  
Time Series Data ◽  
Industrial Robot ◽  
Industrial Robots ◽  
Series Data ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Force Control System

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

scholarly journals Comparative Analysis of the Life-Cycle Cost of Robot Substitution: A Case of Automobile Welding Production in China

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 226
Author(s):  
Xuyang Zhao ◽  
Cisheng Wu ◽  
Duanyong Liu
Keyword(s):  
Life Cycle ◽  
Systems Engineering ◽  
Life Cycle Cost ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Cost Allocation ◽  
Industrial Robot ◽  
Case Analysis ◽  
Industrial Robots ◽  
Analysis Model

Within the context of the large-scale application of industrial robots, methods of analyzing the life-cycle cost (LCC) of industrial robot production have shown considerable developments, but there remains a lack of methods that allow for the examination of robot substitution. Taking inspiration from the symmetry philosophy in manufacturing systems engineering, this article further establishes a comparative LCC analysis model to compare the LCC of the industrial robot production with traditional production at the same time. This model introduces intangible costs (covering idle loss, efficiency loss and defect loss) to supplement the actual costs and comprehensively uses various methods for cost allocation and variable estimation to conduct total cost and the cost efficiency analysis, together with hierarchical decomposition and dynamic comparison. To demonstrate the model, an investigation of a Chinese automobile manufacturer is provided to compare the LCC of welding robot production with that of manual welding production; methods of case analysis and simulation are combined, and a thorough comparison is done with related existing works to show the validity of this framework. In accordance with this study, a simple template is developed to support the decision-making analysis of the application and cost management of industrial robots. In addition, the case analysis and simulations can provide references for enterprises in emerging markets in relation to robot substitution.

scholarly journals Research of Calibration Method for Industrial Robot Based on Error Model of Position

2021 ◽  
Vol 11 (3) ◽  
pp. 1287
Author(s):  
Tianyan Chen ◽  
Jinsong Lin ◽  
Deyu Wu ◽  
Haibin Wu
Keyword(s):  
Coordinate System ◽  
Industrial Robot ◽  
Calibration Method ◽  
Error Model ◽  
Position Error ◽  
Industrial Robots ◽  
Positioning Error ◽  
Robot Position ◽  
General Measurement ◽  
Parameter Error

Based on the current situation of high precision and comparatively low APA (absolute positioning accuracy) in industrial robots, a calibration method to enhance the APA of industrial robots is proposed. In view of the "hidden" characteristics of the RBCS (robot base coordinate system) and the FCS (flange coordinate system) in the measurement process, a comparatively general measurement and calibration method of the RBCS and the FCS is proposed, and the source of the robot terminal position error is classified into three aspects: positioning error of industrial RBCS, kinematics parameter error of manipulator, and positioning error of industrial robot end FCS. The robot position error model is established, and the relation equation of the robot end position error and the industrial robot model parameter error is deduced. By solving the equation, the parameter error identification and the supplementary results are obtained, and the method of compensating the error by using the robot joint angle is realized. The Leica laser tracker is used to verify the calibration method on ABB IRB120 industrial robot. The experimental results show that the calibration method can effectively enhance the APA of the robot.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

Finding Features of Positioning Error for Large Industrial Robots Based on Convolutional Neural Network

2021 ◽  
Author(s):  
Daiki Kato ◽  
Kenya Yoshitugu ◽  
Naoki Maeda ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Production Systems ◽  
Industrial Robot ◽  
Three Dimensional ◽  
Industrial Robots ◽  
Numerical Control ◽  
Robot Motion ◽  
Positioning Error ◽  
End Effector

Abstract Most industrial robots are taught using the teaching playback method; therefore, they are unsuitable for use in variable production systems. Although offline teaching methods have been developed, they have not been practiced because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have attempted to calibrate the position and posture but have not reached a practical level, as such methods consider the joint angle when the robot is stationary rather than the features during robot motion. Currently, it is easy to obtain servo information under numerical control operations owing to the Internet of Things technologies. In this study, we propose a method for obtaining servo information during robot motion and converting it into images to find features using a convolutional neural network (CNN). Herein, a large industrial robot was used. The three-dimensional coordinates of the end-effector were obtained using a laser tracker. The positioning error of the robot was accurately learned by the CNN. We extracted the features of the points where the positioning error was extremely large. By extracting the features of the X-axis positioning error using the CNN, the joint 1 current is a feature. This indicates that the vibration current in joint 1 is a factor in the X-axis positioning error.

Precision Evaluation of NeXus, a Custom Multi-Robot System for Microsystem Integration

2021 ◽  
Author(s):  
Danming Wei ◽  
Alireza Tofangchi ◽  
Andriy Sherehiy ◽  
Mohammad Hossein Saadatzi ◽  
Moath Alqatamin ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Industrial Robot ◽  
Sensor Arrays ◽  
Industrial Robots ◽  
Element Analysis ◽  
Product Packaging ◽  
Precision Evaluation ◽  
High Efficient ◽  
Supporting Frame ◽  
Microsystem Integration

Abstract Industrial robots, as mature and high-efficient equipment, have been applied to various fields, such as vehicle manufacturing, product packaging, painting, welding, and medical surgery. Most industrial robots are only operating in their own workspace, in other words, they are floor-mounted at the fixed locations. Just some industrial robots are wall-mounted on one linear rail based on the applications. Sometimes, industrial robots are ceiling-mounted on an X-Y gantry to perform upside-down manipulation tasks. The main objective of this paper is to describe the NeXus, a custom robotic system that has been designed for precision microsystem integration tasks with such a gantry. The system tasks include assembly, bonding, and 3D printing of sensor arrays, solar cells, and microrobotic prototypes. The NeXus consists of a custom designed frame, providing structural rigidity, a large overhead X-Y gantry carrying a 6 degrees of freedom industrial robot, and several other precision positioners and processes. We focus here on the design and precision evaluation of the overhead ceiling-mounted industrial robot of NeXus and its supporting frame. We first simulated the behavior of the frame using Finite Element Analysis (FEA), then experimentally evaluated the pose repeatability of the robot end-effector using three different types of sensors. Results verify that the performance objectives of the design are achieved.

FE analysis of the effects of process representations on the prediction of residual stresses and chip morphology in the down-milling of Ti6Al4V

2021 ◽  
pp. 1-50
Author(s):  
Nejah Tounsi ◽  
Tahany El-Wardany
Keyword(s):  
Experimental Data ◽  
Residual Stresses ◽  
Orthogonal Cutting ◽  
Chip Thickness ◽  
Chip Morphology ◽  
Milling Process ◽  
Uncut Chip Thickness ◽  
Orthogonal Machining ◽  
Cutting Length ◽  
Sequential Cuts

Abstract Part I of these two-part papers will investigate the effect of three FEM representations of the milling process on the prediction of chip morphology and residual stresses (RS), when down-milling small uncut chips with thickness in the micrometer range and finite cutting edge radius. They are: i) orthogonal cutting with the mean uncut chip thickness t, obtained by averaging the uncut chip thickness over the cutting length, ii) orthogonal cutting with variable t, which characterizes the down-milling process and which is imposed on a flat surface of the final workpiece, and iii) modelling the true kinematics of the down milling process. The appropriate constitutive model is identified through 2D FEM investigation of the effects of selected constitutive equations and failure models on the prediction of RS and chip morphology in the dry orthogonal machining of Ti6Al4V and comparison to experimental measurements. The chip morphology and RS prediction capability of these representations is assessed using the available set of experimental data. Models featuring variable chip thickness have revealed the transition from continuous chip formation to the rubbing mode and have improved the predictions of residual stresses. The use of sequential cuts is necessary to converge toward experimental data.

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