scholarly journals Error Modeling and Experimental Validation of a Planar 3-PPR Parallel Manipulator With Joint Clearances

2012 ◽  
Vol 4 (4) ◽  
Author(s):  
Guanglei Wu ◽  
Shaoping Bai ◽  
Jørgen A. Kepler ◽  
Stéphane Caro
Keyword(s):  
Prediction Model ◽  
Parallel Manipulator ◽  
Experimental Validation ◽  
Upper Bounds ◽  
Error Modeling ◽  
Error Prediction ◽  
Experimental Measurements ◽  
Modeling And Analysis ◽  
Planar Parallel Manipulator ◽  
Joint Clearances

This paper deals with the error modeling and analysis of a 3-PPR planar parallel manipulator with joint clearances. The kinematics and the Cartesian workspace of the manipulator are analyzed. An error model is established with considerations of both configuration errors and joint clearances. Using this model, the upper bounds and distributions of the pose errors for this manipulator are established. The results are compared with experimental measurements and show the effectiveness of the error prediction model.

scholarly journals Improvement of Shape Error for Slender Parts in Cylindrical Traverse Grinding by Part-Deformation Modelling and Compensation

Metals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1990
Author(s):  
Ivan Mendez ◽  
Jorge Alvarez ◽  
David Barrenetxea ◽  
Leire Godino
Keyword(s):  
Prediction Model ◽  
Experimental Validation ◽  
Grinding Force ◽  
Error Prediction ◽  
Grinding Wheel ◽  
Shape Error ◽  
Distributed Load ◽  
Force Monitoring ◽  
Traverse Grinding ◽  
Ground Part

Achieving geometrical accuracy in cylindrical traverse grinding for high-aspect slender parts is still a challenge due to the flexibility of the workpiece and, therefore, the resulting shape error. This causes a bottleneck in production due to the number of spark-out strokes that must be programmed to achieve the expected dimensional and geometrical tolerances. This study presents an experimental validation of a shape-error prediction model in which a distributed load, corresponding to the grinding wheel width, is included, and allows inclusion of the effect of steady rests. Headstock and tailstock stiffness must be considered and a procedure to obtain their values is presented. Validation of the model was performed both theoretically (by comparing with FEM results) and experimentally (by comparing with the deformation profile of the real workpiece shape), obtaining differences below 5%. Having determined the shape error by monitoring the normal grinding force, a solution was presented to correct it, based on a cross-motion of the grinding wheel during traverse strokes, thus decreasing non-productive spark-out strokes. Due to its simplicity (based on the shape-error prediction model and normal grinding force monitoring), this was easily automatable. The corrective compensation cycle gave promising results with a decrease of 77% in the shape error of the ground part, and improvement in geometrically measured parameters, such as cylindricity and straightness.

scholarly journals An Unsupervised Regularization and Dropout based Deep Neural Network and Its Application for Thermal Error Prediction

2020 ◽  
Vol 10 (8) ◽  
pp. 2870
Author(s):  
Yang Tian ◽  
Guangyuan Pan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Machine Tool ◽  
Deep Neural Network ◽  
Thermal Error ◽  
Error Modeling ◽  
Error Prediction ◽  
Learning Technology ◽  
Heavy Duty ◽  
Self Organizing

Due to the large size of the heavy duty machine tool-foundation systems, space temperature difference is high related to thermal error, which affects to system’s accuracy greatly. The recent highly focused deep learning technology could be an alternative in thermal error prediction. In this paper, a thermal prediction model based on a self-organizing deep neural network (DNN) is developed to facilitate accurate-based training for thermal error modeling of heavy-duty machine tool-foundation systems. The proposed model is improved in two ways. Firstly, a dropout self-organizing mechanism for unsupervised training is developed to prevent co-adaptation of the feature detectors. In addition, a regularization enhanced transfer function is proposed to further reduce the less important weights of the process and improve the network feature extraction capability and generalization ability. Furthermore, temperature sensors are used to acquire temperature data from the heavy-duty machine tool and concrete foundation. In this way, sample data of thermal error predictive model are repeatedly collected from the same locations at different times. Finally, accuracy of the thermal error prediction model was validated by thermal error experiments, thus laying the foundation for subsequent studies on thermal error compensation.

Stiffness analysis of a planar parallel manipulator with variable platforms

2021 ◽  
pp. 1-18
Author(s):  
Xiaoyong Wu ◽  
Yujin Wang ◽  
Zhaowei Xiang ◽  
Ran Yan ◽  
Rulong Tan ◽  
...  
Keyword(s):  
Parallel Manipulator ◽  
Stiffness Analysis ◽  
Planar Parallel Manipulator

Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 789-790 ◽  
pp. 263-267
Author(s):  
Yan Lei Li ◽  
Ming Yan Wang ◽  
You Min Hu ◽  
Bo Wu
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Temperature Data ◽  
Current Sensor ◽  
Error Prediction ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

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