Zhizhong Wu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Zhizhong Wu has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Lijun Tang
School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
2 shared publications - Zhao Zhang
School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha, China
2 shared publications - Chenxin Guo
School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha, China
1 shared publication - Deguang Wu
School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
1 shared publication - Deguang Wu
School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
1 shared publication - Fan Hu
School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
1 shared publication
Latest Publications
- Open Access
Intelligent Multi-Objective Optimization of Structural Parameters for High-Frequency Ultrasonic Transducers
The detection of micro-defects within cemented carbides necessitates a high-frequency, high-sensitivity ultrasonic non-destructive testing transducer (UNDTT), whose performance is highly sensitive to geometric structural parameters. Conventional design approaches rely heavily on empirical trial-and-error, resulting in low efficiency and difficulty in achieving globally optimal solutions. To address this limitation, an intelligent multi-objective optimization method is proposed for transducer structural parameters—namely, radius, matching layer thickness, and backing layer thickness—to simultaneously maximize sensitivity (), center frequency (), and bandwidth (BW). By investigating the relationship between structural parameters and performance metrics, a dataset was constructed and used to develop a convolutional neural network (CNN) surrogate model that captures their nonlinear mapping. The CNN was integrated with the NSGA-III multi-objective optimization algorithm to iteratively generate a Pareto-optimal solution set, from which the best design was selected using the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finite element analysis (FEA) validation confirmed prediction errors below 7.0%. Compared to conventional designs, the proposed approach delivers a 46.1% higher sensitivity and a 7.7% broader bandwidth while maintaining a thinner matching layer. These results confirm the effectiveness and practical advantage of the proposed framework. This data-driven approach offers an efficient alternative for designing a high-performance UNDTT.Open AccessIntelligent Multi-Objective Optimization of Structural Parameters for High-Frequency Ultrasonic Transducers
The detection of micro-defects within cemented carbides necessitates a high-frequency, high-sensitivity ultrasonic non-destructive testing transducer (UNDTT), whose performance is highly sensitive to geometric structural parameters. Conventional design approaches rely heavily on empirical trial-and-error, resulting in low efficiency and difficulty in achieving globally optimal solutions. To address this limitation, an intelligent multi-objective optimization method is proposed for transducer structural parameters—namely, radius, matching layer thickness, and backing layer thickness—to simultaneously maximize sensitivity (), center frequency (), and bandwidth (BW). By investigating the relationship between structural parameters and performance metrics, a dataset was constructed and used to develop a convolutional neural network (CNN) surrogate model that captures their nonlinear mapping. The CNN was integrated with the NSGA-III multi-objective optimization algorithm to iteratively generate a Pareto-optimal solution set, from which the best design was selected using the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Finite element analysis (FEA) validation confirmed prediction errors below 7.0%. Compared to conventional designs, the proposed approach delivers a 46.1% higher sensitivity and a 7.7% broader bandwidth while maintaining a thinner matching layer. These results confirm the effectiveness and practical advantage of the proposed framework. This data-driven approach offers an efficient alternative for designing a high-performance UNDTT. - Open Access
- 19 February 2025
- Published by MDPI AG in Micromachines
- Vol. 16 (2) , 241
Study on Spatiotemporal Variation in Internal Temperature Field in Quartz Flexible Accelerometer
Quartz flexible accelerometers (QFAs) are a type of temperature-sensitive sensor, whereby a change in temperature will cause the key parameters of the accelerometer to drift and cause stability errors. Due to the absence of effective methods for sensing the temperature of internal accelerometer components, existing temperature error correction approaches primarily rely on shell temperature measurements to establish correction models. Consequently, most correction methods achieve higher accuracy during the steady-state heat conduction phase of the accelerometer, whereas the correction error markedly increases during the transient heat conduction phase. To elucidate the temperature discrepancy between the QFA shell and its internal components and to support the development of a temperature error correction method for QFAs based on the internal temperature as a reference, this paper investigated the heat exchange dynamics between the interior and exterior of a QFA. A thermal conduction simulation model of the QFA was established, from which the spatiotemporal distribution patterns of the internal temperature field were derived. The results indicate that the temperature of the QFA shell changes significantly faster than that of the internal meter head in the early stage of the temperature change. The temperature gradient between the shell and the meter head first increases and then decreases, and the rate of temperature change in the upper part of the accelerometer is faster than that in the lower part. Before thermal equilibrium is reached, the temperature distribution inside the accelerometer is uneven in terms of time and space. Inside the accelerometer, the yoke iron, swing plate assembly, servo circuit, and magnetic steel assembly are the main components that exhibit differences in the internal temperature change in the QFA. When developing the temperature error correction method, it was crucial to address and mitigate the impact of temperature variations among these components. The average RMSE between the predicted temperature from the heat transfer model established in this paper and the experimental results was 0.4 °C. This indicates that the model can accurately predict the temperature variation within the QFA, thereby providing robust support for investigating the temperature behavior inside the QFA and offering essential technical foundations for enhancing the accuracy of the temperature error correction method.
- 19 February 2025
- Published by MDPI AG in Micromachines
- Vol. 16 (2) , 241
Open AccessStudy on Spatiotemporal Variation in Internal Temperature Field in Quartz Flexible Accelerometer
Quartz flexible accelerometers (QFAs) are a type of temperature-sensitive sensor, whereby a change in temperature will cause the key parameters of the accelerometer to drift and cause stability errors. Due to the absence of effective methods for sensing the temperature of internal accelerometer components, existing temperature error correction approaches primarily rely on shell temperature measurements to establish correction models. Consequently, most correction methods achieve higher accuracy during the steady-state heat conduction phase of the accelerometer, whereas the correction error markedly increases during the transient heat conduction phase. To elucidate the temperature discrepancy between the QFA shell and its internal components and to support the development of a temperature error correction method for QFAs based on the internal temperature as a reference, this paper investigated the heat exchange dynamics between the interior and exterior of a QFA. A thermal conduction simulation model of the QFA was established, from which the spatiotemporal distribution patterns of the internal temperature field were derived. The results indicate that the temperature of the QFA shell changes significantly faster than that of the internal meter head in the early stage of the temperature change. The temperature gradient between the shell and the meter head first increases and then decreases, and the rate of temperature change in the upper part of the accelerometer is faster than that in the lower part. Before thermal equilibrium is reached, the temperature distribution inside the accelerometer is uneven in terms of time and space. Inside the accelerometer, the yoke iron, swing plate assembly, servo circuit, and magnetic steel assembly are the main components that exhibit differences in the internal temperature change in the QFA. When developing the temperature error correction method, it was crucial to address and mitigate the impact of temperature variations among these components. The average RMSE between the predicted temperature from the heat transfer model established in this paper and the experimental results was 0.4 °C. This indicates that the model can accurately predict the temperature variation within the QFA, thereby providing robust support for investigating the temperature behavior inside the QFA and offering essential technical foundations for enhancing the accuracy of the temperature error correction method.
- Open Access
Structural Optimization Study on a Three-Degree-of-Freedom Piezoelectric Ultrasonic Transducer
A three-degree-of-freedom (3-DOF) piezoelectric ultrasonic transducer is a critical component in elliptical and longitudinal ultrasonic vibration-assisted cutting processes, with its geometric structure directly influencing its performance. This paper proposes a structural optimization method based on a convolutional neural network (CNN) and non-dominated sorting genetic algorithm II (NSGA2). This method establishes a transducer lumped model to obtain the electromechanical coupling coefficients (X- and Z-) and thermal power (X-P) indicators, evaluating the bending and longitudinal vibration performance of the transducer. By creating a finite element model of the transducer with mechanical losses, a dataset of different transducer performance parameters, including the tail mass, piezoelectric stack, and dimensions of the horn, is obtained. Training a CNN model with this dataset yields objective functions for the relationship between different transducer geometric structures and performance parameters. The NSGA2 algorithm solves the X- and Z- objective functions, obtaining the Pareto set of the transducer geometric dimensions and determining the optimal transducer geometry in conjunction with X-P. This method achieves simultaneous improvements in X- and Z- of the transducer by 22.33% and 25.89% post-optimization and reduces X-P to 18.97 W. Furthermore, the finite element simulation experiments of the transducer validate the effectiveness of this method.Open AccessStructural Optimization Study on a Three-Degree-of-Freedom Piezoelectric Ultrasonic Transducer
A three-degree-of-freedom (3-DOF) piezoelectric ultrasonic transducer is a critical component in elliptical and longitudinal ultrasonic vibration-assisted cutting processes, with its geometric structure directly influencing its performance. This paper proposes a structural optimization method based on a convolutional neural network (CNN) and non-dominated sorting genetic algorithm II (NSGA2). This method establishes a transducer lumped model to obtain the electromechanical coupling coefficients (X- and Z-) and thermal power (X-P) indicators, evaluating the bending and longitudinal vibration performance of the transducer. By creating a finite element model of the transducer with mechanical losses, a dataset of different transducer performance parameters, including the tail mass, piezoelectric stack, and dimensions of the horn, is obtained. Training a CNN model with this dataset yields objective functions for the relationship between different transducer geometric structures and performance parameters. The NSGA2 algorithm solves the X- and Z- objective functions, obtaining the Pareto set of the transducer geometric dimensions and determining the optimal transducer geometry in conjunction with X-P. This method achieves simultaneous improvements in X- and Z- of the transducer by 22.33% and 25.89% post-optimization and reduces X-P to 18.97 W. Furthermore, the finite element simulation experiments of the transducer validate the effectiveness of this method.