Chatter Stability of Machining Operations

Altintaş, Yusuf; Stépàn, Gábor; Budak, Erhan; Schmitz, Tony L.; Kılıç, Zekai Murat

doi:10.1115/1.4047391

Cited by 89 publications

(37 citation statements)

References 100 publications

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“…In [28][29][30], the prediction and control methods of chatter vibration in different machining processes were comprehensively reviewed. However, there is still not a comprehensive review to critically summarize the published works about thin-wall machining.…”

Section: Introductionmentioning

confidence: 99%

A state-of-art review on chatter and geometric errors in thin-wall machining processes

Pan

et al. 2021

Journal of Manufacturing Processes

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Section: Introductionmentioning

confidence: 99%

A state-of-art review on chatter and geometric errors in thin-wall machining processes

Pan

et al. 2021

Journal of Manufacturing Processes

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“…Unfortunately, the collection of data is a strenuous and troublesome process and amassing large amounts of data is both inefficient and unnecessary and could also take months, or even years. Chatter only occurs at specific feed rates and cutting force and also depends on the manufacturing process and the kind of cutting tool used [29]. Chatter can cause serious damage to the cutters and even the machine tools themselves.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis

Kuo

Luan

Tseng

et al. 2021

Preprint

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Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. Nowadays, deep learning models have shown an extraordinary ability to extract data features which are their necessary fuel. In this study deep learning models have been substituted for more traditional methods. Chatter data are rare and valuable because the collecting process is extremely difficult. To solve this practical problem an innovative training strategy has been proposed that is combined with a modified convolutional neural network and deep convolutional generative adversarial nets. This improves chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. The convolutional neural networks were trained using original data as well as by forged data produced by the generator. Original training data were collected and preprocessed by the Chen-Lee chaotic system. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results were compared with without a data generator and data augmentation. The proposed method had an accuracy of 95.3% on leave-one-out cross-validation over ten runs and surpassed other methods and models. The forged data were also compared with original training data as well as data produced by augmentation. The distribution shows that forged data had similar quality and characteristics to the original data. The proposed training strategy provides a high-quality deep learning chatter detection model.

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“…All this takes place in conditions defined by the mechanical system of the headstock of the grinding wheel and headstock of the workpiece mounted on the machine tool with specified stiffness and dynamic properties. For this reason, the problem of modeling grinding process and chatter has been the subject of deliberations of many scientists [1][2][3][4][5][6][7][8][9][10][11], and it is rather difficult to find any useful general principles or observations in these studies that would be directly applicable to any specific dynamic system including the grinding process.…”

Section: Introductionmentioning

confidence: 99%

Experimental oscillation-assisted cylindrical plunge grinding

Pawłowski

Kępczak

Bechciński

et al. 2021

Journal of Manufacturing Science and Engineering

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The paper presents experimental studies of the influence of amplitude, frequency, time of oscillation and average value of the rotational speed of the workpiece on the waviness of the ground surface during cylindrical outer diameter plunge grinding on a mass-produced cylindrical grinder. Oscillations were introduced in order to counteract the development of self-excited vibrations during spark-out. The waviness of the surface was determined by a laser distance measurement system. The results of the experiments indicate the important positive influence of the oscillations applied during spark-out on the waviness of the ground surface. The conclusions of the study regarding the process as well as technology of cylindrical plunge grinding are presented.

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Chatter Stability of Machining Operations

Cited by 89 publications

References 100 publications

A state-of-art review on chatter and geometric errors in thin-wall machining processes

A state-of-art review on chatter and geometric errors in thin-wall machining processes

Nonlinear Chen-Lee Chaotic System Based Deep Convolutional Generative Adversarial Nets for Chatter Diagnosis

Experimental oscillation-assisted cylindrical plunge grinding

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