Effects of Extrinsic Noise Factors on Machine Learning–Based Chatter Detection in Machining

Lance Lu; Thomas Kurfess; Christopher Saldana

doi:10.1520/ssms20210007

Effects of Extrinsic Noise Factors on Machine Learning–Based Chatter Detection in Machining

Smart and Sustainable Manufacturing Systems ◽

10.1520/ssms20210007 ◽

2021 ◽

Vol 5 (1) ◽

pp. 20210007

Author(s):

Lance Lu ◽

Thomas Kurfess ◽

Christopher Saldana

Keyword(s):

Machine Learning ◽

Chatter Detection ◽

Extrinsic Noise ◽

Noise Factors

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Classification of Weld Defects Based on Convolution Neural Network

Herald of the Bauman Moscow State Technical University Series Instrument Engineering ◽

10.18698/0236-3933-2021-2-23-36 ◽

2021 ◽

pp. 23-36

Author(s):

A.I. Gavrilov ◽

M.Tr. Do

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Weld Seam ◽

Control Process ◽

Welding Process ◽

Optimal Solution ◽

Automatic Welding ◽

Welding Seam ◽

Noise Factors

Automatic welding technology has been widely applied in many industrial fields. It is a complex process with many nonlinear parameters and noise factors affecting weld quality. Therefore, it is necessary to inspect and evaluate the quality of the weld seam during welding process. However, in practice there are many types of welding seam defects, causes and the method of corrections are also different. Therefore, welding seam defects need to be classified to determine the optimal solution for the control process with the best quality. Previously, the welder used his experience to classify visually, or some studies proposed visual classification with image processing algorithms and machine learning. However, it requires a lot of time and accuracy is not high. The paper proposes a convolutional neural network structure to classify images of welding seam defects from automatic welding machines on pipes. Based on comparison with the classification results of some deep machine learning networks such as VGG16, Alexnet, Resnet-50, it shows that the classification accuracy is 99.46 %. Experimental results show that the structure of convolutional neural network is proposed to classify images of weld seam defects have availability and applicability

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A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts

Sensors ◽

10.3390/s21175779 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5779

Author(s):

Runqiong Wang ◽

Qinghua Song ◽

Zhanqiang Liu ◽

Haifeng Ma ◽

Munish Kumar Gupta ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Learning Algorithms ◽

Bending Moment ◽

Extraction Methods ◽

Machine Learning Algorithms ◽

Chatter Detection ◽

Unsupervised Machine Learning ◽

Milling Chatter ◽

Better Than

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.

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On-line chatter detection in milling with hybrid machine learning and physics-based model

CIRP Journal of Manufacturing Science and Technology ◽

10.1016/j.cirpj.2021.05.006 ◽

2021 ◽

Vol 35 ◽

pp. 25-40

Author(s):

M. Hossein Rahimi ◽

Hoai Nam Huynh ◽

Yusuf Altintas

Keyword(s):

Machine Learning ◽

Chatter Detection ◽

On Line ◽

Hybrid Machine

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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