An image-based approach to predict instantaneous cutting forces using convolutional neural networks in end milling operation

2021 ◽  
Author(s):  
Shuo Su ◽  
Gang Zhao ◽  
Wenlei Xiao ◽  
Yiqing Yang ◽  
Xian Cao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cutting Forces ◽  
End Milling ◽  
Milling Operation

FRACTAL-BASED ANALYSIS OF THE VARIATIONS OF CUTTING FORCES ALONG DIFFERENT AXES IN END MILLING OPERATION

Fractals ◽  
2018 ◽  
Vol 26 (06) ◽  
pp. 1850089 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
ALI AKHAVAN FARID ◽  
TECK SENG CHANG
Keyword(s):  
Fractal Dimension ◽  
Cutting Force ◽  
Cutting Forces ◽  
End Milling ◽  
Fractal Theory ◽  
Radial Force ◽  
Machining Conditions ◽  
Feed Force ◽  
Milling Operation ◽  
Force Signal

Analysis of cutting forces in machining operation is an important issue. The cutting force changes randomly in milling operation where it makes a signal by plotting over time span. An important type of analysis belongs to the study of how cutting forces change along different axes. Since cutting force has fractal characteristics, in this paper for the first time we analyze the variations of complexity of cutting force signal along different axes using fractal theory. For this purpose, we consider two cutting depths and do milling operation in dry and wet machining conditions. The obtained cutting force time series was analyzed by computing the fractal dimension. The result showed that in both wet and dry machining conditions, the feed force (along [Formula: see text]-axis) has greater fractal dimension than radial force (along [Formula: see text]-axis). In addition, the radial force (along [Formula: see text]-axis) has greater fractal dimension than thrust force (along [Formula: see text]-axis). The method of analysis that was used in this research can be applied to other machining operations to study the variations of fractal structure of cutting force signal along different axes.

On modeling of cutting forces in micro-end milling operation

2017 ◽  
Vol 21 (4) ◽  
pp. 562-581 ◽  
Author(s):  
Tesfaye M. Moges ◽  
K. A. Desai ◽  
P. V. M. Rao
Keyword(s):  
Cutting Forces ◽  
End Milling ◽  
Milling Operation ◽  
Micro End Milling

Surface Topography Predication in High-Speed End Milling of Flexible Milling System

2010 ◽  
Vol 29-32 ◽  
pp. 1832-1837
Author(s):  
Zhong Qun Li ◽  
Shuo Li ◽  
Ming Zhou
Keyword(s):  
Surface Topography ◽  
Cutting Forces ◽  
High Speed ◽  
End Milling ◽  
Cutting Parameters ◽  
Numerical Approach ◽  
Depth Of Cut ◽  
Machined Surface ◽  
Milling Operation ◽  
The Impact

During milling operation, the cutting forces will induce vibrations on both the cutting tool and the workpiece, which will affect the topography of the machined surface. Based on the Z-map representation of the workpiece, an improved model is presented to predicate the 3D surface topography along with the dynamic cutting forces during an end milling operation. A numerical approach is employed to solve the differential equations governing the dynamics of the milling system. The impact of cutting parameters such as the feedrate, the axial depth of cut and the dynamic characteristic of milling system on the surface topography is investigated by simulation. The all above can provide some instructive directions to the manufacturing engineers in determining the optimal cutting conditions of an end milling operation.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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