A review of artificial intelligent methods for machined surface roughness prediction

Yang, Huguang; Zheng, Han; Zhang, Taohong

doi:10.1016/j.triboint.2024.109935

Cited by 7 publications

References 81 publications

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Flame Retardation of Polyamide 6 by Electro-Sprayed Phosphorus–Copper Complex Supported by Layered Silicate

Majka,

Bukowczan,

Pielichowski

2024

J. of Materi Eng and Perform

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The primary objective of this study was to demonstrate the effect of a phosphorus–copper complex on the flammability of polyamide 6 (PA6) materials, including organoclay-reinforced nanocomposites. The secondary objective was to compare the effectiveness of the deposition methods of the three-layer copper complex. Our previous work successfully revealed the effectiveness of reducing the flammability of polyamide composites by depositing phosphorus and copper compounds using a Layer-by-Layer technique. In the current work, we turned our attention to modifying the PA6 surface by electro-spray (ES) using the same flame retardants as before, and we investigated the effectiveness of the resulting surface protection against high temperatures during combustion. Surface roughness analysis revealed that the use of the ES method favors the formation of surface unevenness. The UL-94 test results showed that composite materials having 5-15 triple layers were among the best flammability classes compared to the others. Both types of materials tested also showed a tendency to reduce the heat release rate (HRR) by increasing the degree of surface coverage when using the electro-spray technique. Remarkably, deposition of 15 triple layers on the surface of the nanocomposite reduced the maximum HRR point by 20%. Graphical abstract

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Flame Retardation of Polyamide 6 by Electro-Sprayed Phosphorus–Copper Complex Supported by Layered Silicate

Majka,

Bukowczan,

Pielichowski

2024

J. of Materi Eng and Perform

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Milling surface roughness monitoring using real-time tool wear data

Wang,

Song,

Peng

et al. 2025

International Journal of Mechanical Sciences

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Adaptive hybrid prediction model for adapting to data distribution shifts in machining quality prediction

Li,

Yang,

Gao

et al. 2024

Meas. Sci. Technol.

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The accuracy of data-driven intelligent prediction for machining quality relies on the training samples. However, in actual applications, the continuous operation of machining equipment leads to gradual distribution shifts between the process data and the training samples for modeling. The shifts result in a degradation in the performance of predictive model, previous studies have often overlooked this issue. To tackle with the intricate problem, this research proposes a real-time model optimization approach. Firstly, a method for detecting machining data distribution shifts based on the two-sample Kolmogorov-Smirnov (KS) test is proposed. Then, an adaptive hybrid prediction model (AHPM) capable of real-time optimization is developed. This model consists of a deep neural network (DNN) and a broad learning system (BLS). DNN plays a primary role in prediction within the hybrid model with excellent generalization capability. BLS quickly completes optimization prior to DNN with its unique parameter update mechanism to compensate for prediction loss. Experimental results indicate that AHPM achieves the shortest optimization time while maintaining high accuracy, with post-optimization error reduction rates for MSE, MAE, and MAPE all exceeding 10%. In the test of application to actual machining cases, accuracy improved by 8.88% compared to traditional methods without optimization.

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A review of artificial intelligent methods for machined surface roughness prediction

Cited by 7 publications

References 81 publications

Flame Retardation of Polyamide 6 by Electro-Sprayed Phosphorus–Copper Complex Supported by Layered Silicate

Flame Retardation of Polyamide 6 by Electro-Sprayed Phosphorus–Copper Complex Supported by Layered Silicate

Milling surface roughness monitoring using real-time tool wear data

Adaptive hybrid prediction model for adapting to data distribution shifts in machining quality prediction

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