scholarly journals Modelling and in-process monitoring of abrasive belt grinding process

2019 ◽  
Author(s):  
Pandiyan Solai Raja Pandiyan Vigneashwara
Keyword(s):  
Process Monitoring ◽  
Grinding Process ◽  
Belt Grinding ◽  
Abrasive Belt

Research on the Key Technology of NC Abrasive Belt Grinding for the Leading and Trailing Edges of Aeroengine Blades

2012 ◽  
Vol 565 ◽  
pp. 76-81 ◽  
Author(s):  
Yun Huang ◽  
Xiao Xiao Ye ◽  
Ming De Zhang ◽  
Hong Wen Fang
Keyword(s):  
Control Method ◽  
Dimensional Accuracy ◽  
Pressure Control ◽  
Grinding Process ◽  
Belt Grinding ◽  
Key Technology ◽  
Abrasive Belt ◽  
Model Research ◽  
Trailing Edges ◽  
Blade Machining

This document provides an analysis of the structure characteristics and grinding process requirements of leading and trailing edges, and proposes a grinding process of leading and trailing edges, established a uneven grinding margin model, research the quantitative grinding pressure control method of uneven margin, as well as the error compensation technology of blade machining deformation, and experiments were carried out on the basis of theories above. The experimental results demonstrate that: after grinding, the edge roundness improved greatly, dimensional accuracy of edge radius can reach ±0.07mm.Compared with the traditional manual polishing method, the grinding quality improved significantly.

Investigation of the effect of abrasive belt constant force grinding based on mechanical decoupling tool system

2020 ◽  
pp. 095440542094937
Author(s):  
Guohong Xie ◽  
Ji Zhao ◽  
Xin Wang ◽  
Huan Liu ◽  
Yan Mu ◽  
...  
Keyword(s):  
Control System ◽  
Force Control ◽  
Normal Force ◽  
Decoupling Control ◽  
Grinding Process ◽  
Constant Force ◽  
Belt Grinding ◽  
Abrasive Belt ◽  
Coupling System ◽  
Mechanical Decoupling

In the abrasive belt grinding process, there are factors affecting the machining stability, efficiency, and quality. Based on the analysis of the grinding process, the normal force in the contact area between the abrasive belt and the workpiece is a major factor. By comparing constant force and non-constant force grinding, the results imply that keeping the grinding force constant will achieve desired material removal and better surface quality. The phenomenon of over- and under-cutting of the workpieces can also be avoided by a constant normal force. In this article, a controllable and flexible belt grinding mechanism accompanied with a mechanical decoupling control strategy is built and tested. Afterward, a detailed comparison is made between the traditional force-position coupling system and the proposed decoupling control system. The proposed control system suppresses the interference between the position and force control systems. The contact force is directly measured and controlled without detecting the position of other components in the tool system. The complexity of the control system is thereby reduced. Finally, several grinding experiments are carried out. The standard deviation and coefficient of variation of the measured normal force are kept within 0.25 and 0.02, respectively. The experiment results reveal that the mechanical decoupling system performs well in force control compared with the traditional force-position coupling system. In addition, the surface roughness Ra < 0.4 μm, the surface quality of the workpiece is improved significantly with the constant force controller.

scholarly journals Modelling of Material Removal in Abrasive Belt Grinding Process: A Regression Approach

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 99 ◽  
Author(s):  
Vigneashwara Pandiyan ◽  
Wahyu Caesarendra ◽  
Adam Glowacz ◽  
Tegoeh Tjahjowidodo
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Material Removal ◽  
Grit Size ◽  
Support Vector ◽  
Grinding Process ◽  
Belt Grinding ◽  
Abrasive Belt ◽  
Taguchi Design Of Experiments ◽  
Material Removal Model

This article explores the effects of parameters such as cutting speed, force, polymer wheel hardness, feed, and grit size in the abrasive belt grinding process to model material removal. The process has high uncertainty during the interaction between the abrasives and the underneath surface, therefore the theoretical material removal models developed in belt grinding involve assumptions. A conclusive material removal model can be developed in such a dynamic process involving multiple parameters using statistical regression techniques. Six different regression modelling methodologies, namely multiple linear regression, stepwise regression, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and random forests (RF) have been applied to the experimental data determined using the Taguchi design of experiments (DoE). The results obtained by the six models have been assessed and compared. All five models, except multiple linear regression, demonstrated a relatively low prediction error. Regarding the influence of the examined belt grinding parameters on the material removal, inference from some statistical models shows that the grit size has the most substantial effect. The proposed regression models can likely be applied for achieving desired material removal by defining process parameter levels without the need to conduct physical belt grinding experiments.

Research of Surface Roughness of Fluid Piston Rod Based on the Abrasive Belt Grinding

2009 ◽  
Vol 416 ◽  
pp. 187-191
Author(s):  
Zhi Ming Lv ◽  
Yun Huang ◽  
Zhi Huang ◽  
Li Na Si
Keyword(s):  
Surface Roughness ◽  
Manufacturing Process ◽  
Research Result ◽  
Grinding Process ◽  
Experiment Research ◽  
Belt Grinding ◽  
Abrasive Belt ◽  
Grinding Parameters

The method of abrasive belt finishing slender piston rod was proposed in this paper, which based on low surface roughness weaknesses of low rigidity slender piston rod in the grinding process. And the ralation between the surface roughness and the grinding parameters was analyzed by the experiment research. The research result has a reasonably guidance for the actual manufacturing process.

Research on Accuracy of Abrasive Belt Grinding

2011 ◽  
Vol 101-102 ◽  
pp. 1101-1104
Author(s):  
Hong Li
Keyword(s):  
High Efficiency ◽  
Research Result ◽  
Dimensional Accuracy ◽  
Grinding Process ◽  
Belt Grinding ◽  
Roundness Error ◽  
Factors Affecting ◽  
Abrasive Belt ◽  
Research Objects ◽  
Open Cycle

The experiment on slender shaft open-cycle belt grinding process is conducted in this paper. The research objects are dimensional accuracy and deviation from roundness error, the changes of which are emphasized after the belt grinding. And the factors affecting the working accuracy of the belt grinding are analyzed. Some measures for improving working accuracy of the belt grinding are put forward. Research result shows that by installing a belt grinding device on a lathe to grind the slender shafts can improve the accuracy with high efficiency.

scholarly journals Predictive Modelling and Analysis of Process Parameters on Material Removal Characteristics in Abrasive Belt Grinding Process

2017 ◽  
Vol 7 (4) ◽  
pp. 363 ◽  
Author(s):  
Vigneashwara Pandiyan ◽  
Wahyu Caesarendra ◽  
Tegoeh Tjahjowidodo ◽  
Gunasekaran Praveen
Keyword(s):  
Process Parameters ◽  
Material Removal ◽  
Predictive Modelling ◽  
Grinding Process ◽  
Belt Grinding ◽  
Abrasive Belt

scholarly journals A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1429
Author(s):  
Wahyu Caesarendra ◽  
Triwiyanto Triwiyanto ◽  
Vigneashwara Pandiyan ◽  
Adam Glowacz ◽  
Silvester Dian Handy Permana ◽  
...  
Keyword(s):  
Tool Wear ◽  
Cycle Time ◽  
Critical Phenomenon ◽  
Size Variation ◽  
Grinding Process ◽  
Belt Grinding ◽  
Accelerometer Sensor ◽  
Abrasive Belt ◽  
Axis Parallel ◽  
Abrasive Process

 This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or fine material removal. Degradation of the belt condition is a critical phenomenon that affects the workpiece quality during grinding. This work focuses on the identifation and the study of force and vibrational signals taken from sensors along an axis or combination of axes that carry important information of the contact conditions, i.e., belt wear. Three axes of the two sensors are aligned and labelled as X-axis (parallel to the direction of the tool during the abrasive process), Y-axis (perpendicular to the direction of the tool during the abrasive process) and Z-axis (parallel to the direction of the tool during the retract movement). The grinding process was performed using a customized abrasive belt grinder attached to a multi-axis robot on a mild-steel workpiece. The vibration and force signals along three axes (X, Y and Z) were acquired for four discrete sequential belt wear conditions: brand-new, 5-min cycle time, 15-min cycle time, and worn-out. The raw signals that correspond to the sensor measurement along the different axes were used to supervisedly train a 10-Layer CNN architecture to distinguish the belt wear states. Different possible combinations within the three axes of the sensors (X, Y, Z, XY, XZ, YZ and XYZ) were fed as inputs to the CNN model to sort the axis (or combination of axes) in the order of distinct representation of the belt wear state. The CNN classification results revealed that the combination of the XZ-axes and YZ-axes of the accelerometer sensor provides more accurate predictions than other combinations, indicating that the information from the Z-axis of the accelerometer is significant compared to the other two axes. In addition, the CNN accuracy of the XY-axes combination of dynamometer outperformed that of other combinations. 

Sign in / Sign up

Export Citation Format

Share Document