Hidden semi-Markov model-based method for tool wear estimation in milling process

Kong, Dongdong; Yong-jie, Chen; Li, Ning

doi:10.1007/s00170-017-0404-0

Cited by 34 publications

(15 citation statements)

References 27 publications

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“…Most of the popular melodies are mixed to learn, and the advantages of various styles are integrated to provide a data reference for the accompaniment arrangement of the input single melody songs. e problem of accompaniment chord selection of single note theme and the optimization of chord sequence are solved by the accompaniment hidden Markov model [18][19][20]. e input melody is segmented, and the input melody mode is unified in different songs and modes, and the single melody song is transformed into the standard C major without changing the internal sound group structure of the melody itself.…”

Section: Frame To Movementioning

confidence: 99%

“…erefore, it greatly facilitates the arrangement of chords. Fragments of theme features are extracted, according to the characteristics of the combination of machine learning algorithm to obtain sample songs under the different styles of melody-matching chord by relevant probability, and in the accompaniment, chords knowledge database matching options, this segment of the right chord, and repeat the above steps, until get the chord accompaniment matching probability, the optimal And record and update the relevant probability parameters [19,20]. Melody characteristic ratio is in this period of music notes weight relations.…”

Section: Automated Chord Tag Constructionmentioning

confidence: 99%

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Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

Shi

Tsai

2021

Mathematical Problems in Engineering

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Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.

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Section: Frame To Movementioning

confidence: 99%

Section: Automated Chord Tag Constructionmentioning

confidence: 99%

Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

Shi

Tsai

2021

Mathematical Problems in Engineering

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“…The fault tree analysis (FTA) and failure mode and effects analysis (FMEA) method was used to analyze the reliability of the engines, and the Artificial Neural Network (ANN) was used to predict the characteristic parameters of exhaust gas temperatures of main engine cylinders [27]. Kong et al [28] presented a hidden semi-Markov model (HSMM) method to estimate the tool wear in milling process. The experiments showed that the proposed method can achieve higher accuracy in tool wear evaluation.…”

Section: Introductionmentioning

confidence: 99%

Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

Kang

Lü

Luo

et al. 2021

Mechanics & Industry

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The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.

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“…Since the indirect methods can implement Sensors 2020, 20, 6113 2 of 20 online monitoring, it has been widely adopted. The most commonly used indirect monitoring signals include cutting force signal [2][3][4], vibration signal [5][6][7], acoustic emission signal [8,9], machined surface image [10,11], and current signal [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

Yuan

Liu

Yang

et al. 2020

Sensors

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Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

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Hidden semi-Markov model-based method for tool wear estimation in milling process

Cited by 34 publications

References 27 publications

Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

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