scholarly journals Diagnosis of the Misaligned Faults of the Vertical Test Instrument of High-Precision Industrial Robot Reducer

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zhen Yu ◽  
Yuan Zhang
Keyword(s):  
High Precision ◽  
Industrial Robot ◽  
Performance Testing ◽  
Rotation Frequency ◽  
Torque Ripple ◽  
Industrial Robots ◽  
Rotating Machine ◽  
Torque Transducer ◽  
Misalignment Fault ◽  
The Relationship

High-precision reducer is the core component of industrial robots. In order to achieve the comprehensive performance testing of precision reducers, an instrument with a vertical layout and a cylindrical structure is designed. As a rotating machine, the inevitable coupling misalignment of the instrument can lead to vibration faults which lead to errors in the test. So it is pretty necessary to diagnose and monitor the coupling misalignment while the instrument is working. The causes of the coupling misaligned fault of the instrument and the relationship between the misalignment fault and torque ripple are analyzed in this paper. A method of using the torque transducer in the measurement chain of the instrument to diagnose the coupling misalignment is proposed in this paper. Many experiments have been done to test the capability of detecting the coupling misalignment using this method. Experimental results show that the amplitude of torque ripple of the shaft is linearly related to the coupling misalignment and is quadratically related to the rotation speed of the shaft when the misalignment exists in the shaft. The combination of components at the rotation frequency (fr) and the additional components can be used to diagnose faults due to coupling misalignment.

scholarly journals A Novel Information Fusion Method for Vision Perception and Location of Intelligent Industrial Robots

2019 ◽  
Vol 25 (5) ◽  
pp. 4-10 ◽  
Author(s):  
Shoufeng Jin ◽  
Qiangqiang Lin ◽  
Jian Yang ◽  
Yu Bie ◽  
Mingrui Tian ◽  
...  
Keyword(s):  
Affine Transformation ◽  
Hamming Distance ◽  
Industrial Robot ◽  
Hessian Matrix ◽  
Recognition Rate ◽  
Industrial Robots ◽  
Transformation Model ◽  
Feature Points ◽  
Multi Scale ◽  
The Relationship

An improved SURF (Speeded-Up Robust Feature） algorithm is proposed to deal with the time-consuming and low precision of positioning of industrial robot. Hessian matrix determinant is used to extract feature points from the target image and a multi-scale spatial pyramid is constructed. The location and scale value of feature points are determined by neighbourhood non-maximum suppression method. The direction of feature points is defined as directional feature descriptors by the binary robust independent elementary feature (BRIEF). The progressive sample consensus (PROSAC) is used to carry out second precise matching and remove mismatching points based on the Hamming distance. Then, an affine transformation model is established to describe the relationship between the template and target images. Centroid coordinates of the target can be obtained based on the affine transformation. Comparative tests were carried out to demonstrate that the proposed method can effectively improve the recognition rate and positioning accuracy of the industrial robots. The average time consuming is less than 0.2 s, the matching accuracy is 96 %, and the positioning error of the robot is less than 1.5 mm. Therefore, the proposed method has practical application importance.

scholarly journals The Impact of Intelligent Manufacturing on Export Sophistication: Evidence from Industrial Robots

2019 ◽  
Vol 14 (12) ◽  
pp. 183
Author(s):  
Lihua Gu
Keyword(s):  
Panel Data ◽  
Financial Crisis ◽  
Industrial Revolution ◽  
Industrial Robot ◽  
Intelligent Manufacturing ◽  
International Federation ◽  
Industrial Robots ◽  
Proxy Variables ◽  
The Impact ◽  
The Relationship

We analyze the relationship between intelligent manufacturing and export sophistication from the perspective of industrial robots. We use industrial robot panel data from International Federation of Robotics in 70 countries from 1995 to 2016. Our empirical research shows that if a country increases 1% of industrial robots in production process, export sophistication will increase 0.0036%. The result is very robust when we use two kind of proxy variables. And we also find that financial crisis depresses the effect of industrial robots on export sophistication. Our estimates suggest that intelligent manufacturing is a way to realize export upgrading in the background of a new industrial revolution. Countries without producing any industrial robots can import some robots from other countries to increase export sophistication.

scholarly journals Torque Measurement of High-Precision Reducer for Industrial Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhen Yu ◽  
Yuan Zhang
Keyword(s):  
High Precision ◽  
Measurement System ◽  
Measurement Errors ◽  
Industrial Robot ◽  
Industrial Robots ◽  
System Structure ◽  
Torque Measurement ◽  
Accuracy Measurement ◽  
Input And Output

Torque testing is crucial to improve the quality of high-precision reducers—the core component of industrial robots. Herein, a torque-measurement system for a novel vertical measuring instrument is designed. The distance from the torque transducers to the robot reducer is minimized to ensure the shortest measurement chain. The symmetrical system structure improves the overall rigidity, and error compensation can be performed easily. The characteristics of the torque measurement errors due to shaft bending and torsional deformations were also analyzed. A torque calibrator comprising two high-precision torque output systems was used to calibrate torque transducers in the measurement system. Reasonable and practical compensation models based on a backpropagation neural network were developed to accurately obtain the input and output torques of the reducer. As the torque-measurement precision of the reducer detector reached 0.1% over the entire torque range, the instrument can be used for accuracy measurement of the input and output torques of the robot reducer.

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.

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