Simulation and measurement of surface roughness via grey scale image of tool in finish turning

Shahabi, H. H.; Ratnam, Mani Maran

doi:10.1016/j.precisioneng.2015.07.004

Cited by 29 publications

(15 citation statements)

References 28 publications

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“…A threshold method, close to the algorithm proposed in [21] and using the Matlab Image Processing Toolbox, has been developed. The first step is image binarizing according to a threshold value determined with the well-known Otsus method [17].…”

Section: Surface Extractionmentioning

confidence: 99%

Characterization of surface topography of 3D printed parts by multi-scale analysis

Quinsat

Lartigue

Brown³

et al. 2017

Int J Interact Des Manuf

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Surface topography is a key element between process parameters and manufactured part performances. Within the context of 3D printed parts, one difficulty is to consider the total 3D surface topography including internal porosity. In this paper, an original method is proposed for the characterization of the surface topography, both internal and external. Starting from volumetric data obtained by Computed Tomography measurements, a method of surface extraction is performed that identifies skin voxels corresponding to the internal and external part surface,and which are the support to the calculation of the Specific Surface Area (SSA). A multi-scale analysis is thus proposed to characterize the total surface, (SSA), obtained at different scales. The interest of the multi-scale analysis is illustrated through various examples that attempt to link process parameters to part properties.

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Section: Surface Extractionmentioning

confidence: 99%

Characterization of surface topography of 3D printed parts by multi-scale analysis

Quinsat

Lartigue

Brown³

et al. 2017

Int J Interact Des Manuf

View full text Add to dashboard Cite

show abstract

“…Hence, a threshold method, close to the algorithm proposed in [17], and using Matlab© Image Processing Toolbox, has been developed. The first step is then image binarizing according to a threshold value determined with the well-known Otsu's method [16].…”

Section: Ct Data Treatmentmentioning

confidence: 99%

Multi-scale surface characterization in additive manufacturing using CT

Quinsat

Lartigue

Brown³

et al. 2016

Lecture Notes in Mechanical Engineering

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In additive manufacturing, the part geometry, including its internal structure, can be optimized to answer functional requirements by optimizing process parameters. This can be performed by linking process parameters to the resulting manufactured geometry. This paper deals with an original method for surface geometry characterization of printed parts (using Fused Filament Fabrication FFF) based on 3D Computer Tomography (CT) measurements. From 3D measured data, surface extraction is performed, giving a set of skin voxels corresponding to the internal and external part surface. A multi-scale analysis method is proposed to analyse the relative internal area of the total surface obtained at different scales (from sub-voxel to super-voxel scales) with different process parameters. This analysis turns out to be relevant for filling strategy discrimination.

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“…The crucial factors affecting the performance of the data-driven approach are two-fold: the features extracted for model inputs and the selection of the model used for prediction. Regarding the feature extraction aspects, surface roughness prediction can be achieved directly or indirectly based on various sensor inputs, including images [10][11][12][13], accelerometers [14][15][16][17], and dynamometers [18][19][20][21][22]. S. Ghodrati et al [11] utilized an image profilometry approach to measure the surface roughness of metallic samples and achieved a highly accurate result.…”

Section: Introductionmentioning

confidence: 99%

“…H.H. Shahabi [12] used 2-D images to evaluate the surface profile in the finish machining and successfully forecasted the final surface profile. O. M. Koura [13] applied an image processing technique to measure the surface roughness and explored the effects of the camera resolution and position setting with respect to the measured surfaces.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

Young

et al. 2019

Applied Sciences

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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.

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Simulation and measurement of surface roughness via grey scale image of tool in finish turning

Cited by 29 publications

References 28 publications

Characterization of surface topography of 3D printed parts by multi-scale analysis

Characterization of surface topography of 3D printed parts by multi-scale analysis

Multi-scale surface characterization in additive manufacturing using CT

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

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