Cutting Force Model Prediction Considering Cutting Edge Parameters Base on Genetic Algorithm

Wang, Yu; Wang, L.Q.; Li, Y.F.; Zhai, Yuan Sheng; Liu, X.L.

doi:10.4028/www.scientific.net/amr.188.166

Cited by 1 publication

(3 citation statements)

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“…In the incremental learning stage, suppose 𝑁 1 cutting samples arrive, and then incremental online learning is carried out. At this time, the problem is transformed to find the 𝛽 (1) corresponding to the minimum value of Equation (10).…”

Section: Os-elm Mathematical Theory Modelmentioning

confidence: 99%

“…In order to realize the recursive update of β, 𝛽 (1) in Equation ( 13) is expressed as a function of 𝛽 (0) ，𝐾 1 ，H 1 ，Y 1 , namely:…”

Section: Os-elm Mathematical Theory Modelmentioning

confidence: 99%

“…In aerospace, power generation and other fields, structural components are often designed as thin-walled frame structures in order to reduce weight and increase bearing capacity [1]. These structures are usually multi-frame structures, and are mainly processed by milling.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On-line Evolutionary Identification Technology for Milling Chatter of Thin Walled Parts Based on the Incremental-Sparse K-Means and the Online Sequential Extreme Learning Machine

Wang¹,

Yue

Liu³

et al. 2023

Preprint

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In the milling process of thin-walled parts, chatter is very easy to occur, which has a very adverse impact on the surface quality and machining efficiency of the workpiece. In order to solve the problem of low accuracy of milling state identification caused by few initial samples and dynamic changes in the milling process, a hybrid online evolutionary chatter identification model combining unsupervised learning and supervised learning is proposed. First of all, aiming at the problem that traditional K-Means algorithm is difficult to adapt to online dynamic clustering of milling chatter, an online Incremental-Sparse K-Means algorithm (ISK-Means) was proposed, and the dynamic incremental-sparse strategy of K-Means was designed. Secondly, aiming at the problem that the Online Sequential Extreme Learning Machine (OS-ELM) algorithm directly adds its predicted samples to the training sample set during the incremental learning process, and the pseudo-samples in the training sample set would lead to the degradation of the OS-ELM model, a hybrid online evolutionary chatter identification model combining the ISK-Means and the OS-ELM was proposed, and the online identification and evolution strategy was designed.Finally, the experimental results show that the ISK-Means algorithm can greatly improve the clustering efficiency and is suitable for milling chatter online dynamic clustering. Meanwhile, compared with the existing model, the recognition accuracy of the hybrid online evolutionary chatter recognition model combined with ISK-Means algorithm and OS-ELM algorithm is improved by 1.31%. This is of great significance for the online control of subsequent chatter.

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Section: Os-elm Mathematical Theory Modelmentioning

confidence: 99%

“…In order to realize the recursive update of β, 𝛽 (1) in Equation ( 13) is expressed as a function of 𝛽 (0) ，𝐾 1 ，H 1 ，Y 1 , namely:…”

Section: Os-elm Mathematical Theory Modelmentioning

confidence: 99%

See 1 more Smart Citation

On-line Evolutionary Identification Technology for Milling Chatter of Thin Walled Parts Based on the Incremental-Sparse K-Means and the Online Sequential Extreme Learning Machine

Wang¹,

Yue

Liu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Cutting Force Model Prediction Considering Cutting Edge Parameters Base on Genetic Algorithm

Cited by 1 publication

References 1 publication

On-line Evolutionary Identification Technology for Milling Chatter of Thin Walled Parts Based on the Incremental-Sparse K-Means and the Online Sequential Extreme Learning Machine

On-line Evolutionary Identification Technology for Milling Chatter of Thin Walled Parts Based on the Incremental-Sparse K-Means and the Online Sequential Extreme Learning Machine

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