Surface roughness monitoring and dimensional error control in turning by quasi-sensor fusion

2005 ◽  
Author(s):  
M. Shiraishi ◽  
H. Sumiya
Keyword(s):  
Surface Roughness ◽  
Sensor Fusion ◽  
Error Control ◽  
Dimensional Error

Practical Dimensional Error Control and Surface Roughness Inspection in Turning

2002 ◽  
Author(s):  
M. Shiraishi ◽  
T. Yamagiwa ◽  
A. Ito
Keyword(s):  
Surface Roughness ◽  
Optical Method ◽  
Machine Tools ◽  
Error Control ◽  
Manufacturing Processes ◽  
Dimensional Error ◽  
Final Output

Monitoring of machine tools and optimization of manufacturing processes require accurate values of in process measured quantities such as dimensional error, force, and surface roughness. The measurement as workpiece is in particular important because the final output in machining is evaluated as the quality machined workpiece itself. A new hybrid sensor using pneumatic and optical method has been developed which can monitor the dimensional error and surface roughness in turning. Satisfactory results were obtained through several experiments.

scholarly journals Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5338
Author(s):  
Pao-Ming Huang ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Deep Learning ◽  
Tool Wear ◽  
Sensor Fusion ◽  
Sensor Selection ◽  
Numerical Control ◽  
Estimation Accuracy ◽  
Machining Parameters ◽  
Estimation Model ◽  
Selection Analysis

This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

scholarly journals Process-machine interactions and a multi-sensor fusion approach to predict surface roughness in cylindrical plunge grinding process

2018 ◽  
Vol 26 ◽  
pp. 700-711 ◽  
Author(s):  
Bhaskar Botcha ◽  
Vairamuthu Rajagopal ◽  
Ramesh Babu N ◽  
Satish T.S. Bukkapatnam
Keyword(s):  
Surface Roughness ◽  
Sensor Fusion ◽  
Grinding Process ◽  
Plunge Grinding ◽  
Fusion Approach

N-dimensional error control multiresolution algorithms for the point value discretization

2020 ◽  
Vol 170 ◽  
pp. 79-97
Author(s):  
J. García ◽  
J.A. Padilla ◽  
J. Ruiz ◽  
J.C. Trillo
Keyword(s):  
Error Control ◽  
Dimensional Error

scholarly journals TWO-DIMENSIONAL ERROR CONTROL BASED ON MODULAR CORRECTIVE CODES

2015 ◽  
pp. 208-215
Author(s):  
Jürgen Sieck ◽  
Vasyl Yatskiv ◽  
Anatoly Sachenko ◽  
Taras Tsavolyk
Keyword(s):  
Error Correction ◽  
Error Control ◽  
Data Matrix ◽  
Two Dimensional ◽  
Dimensional Error ◽  
Matrix Coefficients ◽  
Quartus Ii

The paper presents a method of detecting and correcting packet errors in the block of data based on the modular corrective codes. Check symbols are calculated separately in rows and columns of the data matrix. Herewith, the same data matrix coefficients are used for calculating the check symbols in rows and columns. This allows the detection and correction of errors packets that are in the same row or column. When two or more distorted information symbols are in the same row (assuming that there is only one error in the column) then errors can be corrected through the analysis of the column syndrome. The possible cases of the distorted symbols placement in a block of data and ways of their fixing are considered. The algorithm for detecting and correcting errors packets is elaborated. In the general case the offered method of error correction, based on modular correcting code, provides a correction of: n errors, which are in the same row or column of the data matrix of n size; 2*n-1 errors that are in the same row and column. The proposed method of encoding / decoding is designed in Verilog and implemented on FPGA in the Quartus II of Altera company.

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