Prediction and evaluation of surface roughness with hybrid kernel extreme learning machine and monitored tool wear

Cheng, Minghui; Jiao, Li; Yan, Pei; Li, Siyu; Dai, Zhicheng; Qiu, Tianyang; Wang, Xibin

doi:10.1016/j.jmapro.2022.10.072

Cited by 26 publications

(5 citation statements)

References 52 publications

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“…Related research proves that the SVR [37,38] method has good prediction performance and robustness for small samples problems. For a typical SVR, the initial optimization objective function can be presented as:…”

Section: Svrmentioning

confidence: 98%

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

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Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in-situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

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Section: Svrmentioning

confidence: 98%

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Man,

Zhou,

Sun

et al. 2024

Surf. Topogr.: Metrol. Prop.

View full text Add to dashboard Cite

show abstract

“…The results show that the predicted value is in good accordance with the experimental value. Different from the BP neural network, the RBF neural network [16][17][18] utilizes the Gaussian activation function, which can address some of the issues with the BP neural network, such as the lengthy training period, ease of local optimum, and so on. It can generalize well, make predictions quickly, and adapt better to various types of data.…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Prediction of Titanium Alloy during Abrasive Belt Grinding Based on an Improved Radial Basis Function (RBF) Neural Network

Shan,

Zhang,

Lan

et al. 2023

Materials

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Titanium alloys have become an indispensable material for all walks of life because of their excellent strength and corrosion resistance. However, grinding titanium alloy is exceedingly challenging due to its pronounced material characteristics. Therefore, it is crucial to create a theoretical roughness prediction model, serving to modify the machining parameters in real time. To forecast the surface roughness of titanium alloy grinding, an improved radial basis function neural network model based on particle swarm optimization combined with the grey wolf optimization method (GWO-PSO-RBF) was developed in this study. The results demonstrate that the improved neural network developed in this research outperforms the classical models in terms of all prediction parameters, with a model-fitting R2 value of 0.919.

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“…Pimenov et al [13] used a neural network model to predict the surface roughness of a part by realtime monitoring of a CNC machine tool's spindle power. Cheng et al [14] created an extreme learning machine model for surface roughness prediction and compared it to the response surface method and the support vector machine network; the results showed that the extreme learning machine network can achieve relatively higher accuracy in surface roughness prediction. Guo et al [15] proposed a hybrid feature selection method that selects features based on their correlation with surface roughness as well as hardware and time costs.…”

Section: Introductionmentioning

confidence: 99%

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

Wu,

Liu,

Huang

et al. 2023

Int J Adv Manuf Technol

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γ-TiAl alloy is a typically difficult material to machine, and machining defects such as grain pull-out and material spalling are common during machining, resulting in component scrap.As a result, it is critical to investigate the real-time monitoring method of surface roughness during milling. A new model for predicting surface roughness is proposed in this article. Based on the idea of deep learning, the prediction problem of surface roughness is turned into a classification problem.The features of the force signal are extracted using a continuous wavelet transform, and the onedimensional signal is converted into a two-dimensional time-frequency diagram to obtain more information. The transfer learning mechanism is introduced to make the model run faster and improve prediction accuracy. The overfitting problem is solved and the model's generalization ability is improved through batch normalization and dropout layer, and the prediction accuracy is increased by 3%. The results show that the method has high recognition accuracy, with a maximum classification accuracy of 98% and an average accuracy of 96.12%, a 6% improvement over the traditional model, allowing it to accurately realize real-time monitoring of surface quality in milling processing.

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Prediction and evaluation of surface roughness with hybrid kernel extreme learning machine and monitored tool wear

Cited by 26 publications

References 52 publications

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Surface Roughness Prediction of Titanium Alloy during Abrasive Belt Grinding Based on an Improved Radial Basis Function (RBF) Neural Network

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

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