Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Liao, Qihao; Yin, Qin; Xie, Luofeng; Yin, Guofu

doi:10.1177/09544062211066243

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…When the iterations grows, the diversity of the population gradually reduces, and the search space is limited, leading to the FF algorithm often falling into local optima. In addition, in traditional FF algorithms, the FF population searches for food within the search range with a fixed step size, resulting in slower convergence speed of the algorithm [32][33]. Therefore, in combination with the needs of visual prosthetic image recognition models, study introduces Gaussian backbone mutation mechanism, dynamic step size mechanism, and random abandonment strategy to improve FOA.…”

Section: B Design Of Image Recognition Model On the Ground Of Improve...mentioning

confidence: 99%

Improved VGG Algorithm for Visual Prosthesis Image Recognition

Li,

Jahng

et al. 2024

IEEE Access

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With the development of bio-informatics, visual prostheses have become an effective method for blind people to restore visual function. To meet the visual needs of visual implant recipients, this study explored the problem of in vitro image processor image recognition and classification. It selected the convolutional neural network framework VGG as the technical core, introduced the fruit fly optimization algorithm for optimizing the VGG recognition model, and constructed a visual prosthesis image recognition model on the ground of improved VGG. The experiment demonstrated that the improved fruit fly search algorithm had an average absolute error and root mean square error values lower than 0.4, which was superior to other intelligent optimization algorithms. The performance of the improved image recognition model has been significantly improved, with a maximum AUC value of 0.942, a recognition accuracy range of 68.29%-97.23%, and a stable fitness curve of around 97.00. The maximum F1 value of the image recognition model designed in the study reached 91.47%, and the loss function curve converged to the minimum value. In the application of visual prostheses, the recognition accuracy and R-squared performance of this model were both the best. Compared with natural human vision, the contrast and functional visual effects matched well, the processing speed was faster, and the delay time did not affect the actual application of visual prostheses, achieving high user satisfaction. This study can enrich and improve the theoretical foundation of visual image analysis and visual prosthetics, and help visually impaired groups improve their lives and quality of life.

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Section: B Design Of Image Recognition Model On the Ground Of Improve...mentioning

confidence: 99%

Improved VGG Algorithm for Visual Prosthesis Image Recognition

Li,

Jahng

et al. 2024

IEEE Access

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“…Li et al proposed a thermal error prediction approach for electrical spindles using an optimized extreme learning machine algorithm [7]. Liao et al established a robust thermal error model for spindles based on the improved fruit fly optimization algorithm [8]. Kumar et al designed a real-time LSTM-based predictive model for thermal perception [9].…”

Section: Related Work 21 Lstm-based Thermal Error Predictionmentioning

confidence: 99%

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

Lu,

Zhu,

Wang

et al. 2023

Lubricants

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Traditional methods for predicting thermal error ignore the correlation between physical world data and virtual world data, leading to the low prediction accuracy of thermal errors and affecting the normal processing of the CNC machine tool (CNCMT) spindle. To solve the above problem, we propose a thermal error prediction approach based on digital twins and long short-term memory (DT-LSTM). DT-LSTM combines the high simulation capabilities of DT and the strong data processing capabilities of LSTM. Firstly, we develop a DT system for the thermal characteristics analysis of a spindle. When the DT system is implemented, we can obtain the theoretical value of thermal error. Then, the experimental data is used to train LSTM. The output of LSTM is the actual value of thermal error. Finally, the particle swarm optimization (PSO) algorithm fuses the theoretical values of DT with the actual values of LSTM. The case study demonstrates that DT-LSTM has a higher accuracy than the single method by nearly 11%, which improves the prediction performance and robustness of thermal error.

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Improved exponential model for thermal error modeling of machine-tool spindle based on fruit fly optimization algorithm

Cited by 2 publications

References 24 publications

Improved VGG Algorithm for Visual Prosthesis Image Recognition

Improved VGG Algorithm for Visual Prosthesis Image Recognition

Digital Twin-Driven Thermal Error Prediction for CNC Machine Tool Spindle

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