scholarly journals Emissivity measurement based on deep learning and surface roughness

AIP Advances ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 085305
Author(s):  
Xin Wu ◽  
Xiaolong Wei ◽  
Haojun Xu ◽  
Weifeng He ◽  
Yiwen Li ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Deep Learning ◽  
Emissivity Measurement

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.

Emissivity Estimation Using Thermographic Camera

2013 ◽  
Vol 811 ◽  
pp. 380-387 ◽  
Author(s):  
N. Nunak ◽  
K. Roonprasang ◽  
T. Suesut ◽  
T. Nunak
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Operating Time ◽  
Target Surface ◽  
Engineering Material ◽  
Ir Detectors ◽  
Surface Temperatures ◽  
Thermographic Camera ◽  
Emissivity Measurement ◽  
Effect Of Surface

This paper proposes a method based on the spectra response of IR detectors mounted on thermographic camera for emissivity measurement at various target surface temperatures, while the reflected temperature istaken into account, and also studies on the effect of surface roughness on the emissivity value. The emissivity (ε8-14μm) of general engineering material such as iron, stainless steel, brass, copper and aluminum obtained in this paper are in agreement with other literatures. Finally, results found that the roughness and emissivity of equipment increases with increasing of the operating time.

scholarly journals Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

2019 ◽  
Vol 9 (7) ◽  
pp. 1462 ◽  
Author(s):  
Wan-Ju Lin ◽  
Shih-Hsuan Lo ◽  
Hong-Tsu Young ◽  
Che-Lun Hung
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Fourier Transform ◽  
Deep Learning ◽  
Fast Fourier Transform ◽  
Short Term Memory ◽  
Intelligent System ◽  
Milling Process ◽  
Vibration Signals ◽  
One Dimensional

The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.

scholarly journals Noncontact Surface Roughness Evaluation of Milling Surface Using CNN-Deep Learning Models

2021 ◽  
Author(s):  
Binayak Bhandari ◽  
Gijun Park
Keyword(s):  
Surface Roughness ◽  
Quality Control ◽  
Deep Learning ◽  
Orthogonal Array ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Test Accuracy ◽  
Machined Surface ◽  
Practical Applications ◽  
Taguchi Orthogonal Array

Abstract This paper presents the analysis of end milled machined surfaces backed with experimental and deep learning model investigations. The effect of process parameters like spindle speed, feed rate, depth of cut, cutting speed, and machining duration were investigated to find machined surface roughness using Taguchi orthogonal array. The experiments were conducted on Aluminum A3003, a common material widely used in industries. Following standard DOE using Taguchi orthogonal array, surface roughness was recorded for each machining experiment. Surface roughnesses for the current study were categorized into four classes viz., fine, smooth, rough, and coarse based on the roughness value Ra. Images of the machined surface were used to develop CNN models for surface roughness class prediction. The prediction accuracies of the CNN models were compared for five types of optimizers. It was found that RAdam optimizer performed better among others with the training and test accuracy of 96.30% and 92.91% respectively. The accuracy of the prediction is higher than 90% thus has the potential to substitute human quality control procedures, saving time, energy, and cost. Conversely, the developed CNN model can assist in acquiring preferred machining conditions in advance. Finally, it can eliminate the dependency on expensive surface roughness measuring devices and have enormous practical applications in quality control processes.

Detection of surface roughness of mechanical drawings with deep learning

2021 ◽  
Vol 35 (12) ◽  
pp. 5541-5549
Author(s):  
Hao Hu ◽  
Chao Zhang ◽  
Yanxue Liang
Keyword(s):  
Surface Roughness ◽  
Deep Learning

Roughness measurements of nonlinear optical polymer films by scanning tunneling microscopy

1989 ◽  
Vol 47 ◽  
pp. 22-23
Author(s):  
I. H. Musselman ◽  
R.-T. Chen ◽  
P. E. Russell
Keyword(s):  
Surface Roughness ◽  
Scanning Tunneling Microscopy ◽  
Nonlinear Optical ◽  
Polymer Surface ◽  
Light Extinction ◽  
Scanning Tunneling ◽  
Silicon Substrates ◽  
Tunneling Microscopy ◽  
Optical Polymer ◽  
Total Light

Scanning tunneling microscopy (STM) has been used to characterize the surface roughness of nonlinear optical (NLO) polymers. A review of STM of polymer surfaces is included in this volume. The NLO polymers are instrumental in the development of electrooptical waveguide devices, the most fundamental of which is the modulator. The most common modulator design is the Mach Zehnder interferometer, in which the input light is split into two legs and then recombined into a common output within the two dimensional waveguide. A π phase retardation, resulting in total light extinction at the output of the interferometer, can be achieved by changing the refractive index of one leg with respect to the other using the electrooptic effect. For best device performance, it is essential that the NLO polymer exhibit minimal surface roughness in order to reduce light scattering. Scanning tunneling microscopy, with its high lateral and vertical resolution, is capable of quantifying the NLO polymer surface roughness induced by processing. Results are presented below in which STM was used to measure the surface roughness of films produced by spin-coating NLO-active polymers onto silicon substrates.

Surface roughness measurements of vanadium-contaminated fluidized cracking catalysts by atomic force microscopy

1996 ◽  
Vol 54 ◽  
pp. 866-867
Author(s):  
H. Kinney ◽  
M.L. Occelli ◽  
S.A.C. Gould
Keyword(s):  
Surface Roughness ◽  
Zeolite Y ◽  
Atomic Level ◽  
Microporous Structure ◽  
Contact Mode ◽  
Force Microscopy ◽  
Atomic Force ◽  
Before And After ◽  
Roughness Measurements ◽  
Cracking Catalysts

For this study we have used a contact mode atomic force microscope (AFM) to study to topography of fluidized cracking catalysts (FCC), before and after contamination with 5% vanadium. We selected the AFM because of its ability to well characterize the surface roughness of materials down to the atomic level. It is believed that the cracking in the FCCs occurs mainly on the catalysts top 10-15 μm suggesting that the surface corrugation could play a key role in the FCCs microactivity properties. To test this hypothesis, we chose vanadium as a contaminate because this metal is capable of irreversibly destroying the FCC crystallinity as well as it microporous structure. In addition, we wanted to examine the extent to which steaming affects the vanadium contaminated FCC. Using the AFM, we measured the surface roughness of FCCs, before and after contamination and after steaming.We obtained our FCC (GRZ-1) from Davison. The FCC is generated so that it contains and estimated 35% rare earth exchaged zeolite Y, 50% kaolin and 15% binder.

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