<p>In order to enhance the impact performance and stability of hydraulic impact hammers, this paper first conducts a Design of Experiments (DOE) study on hydraulic impact hammers, identifying ten key parameters that influence their impact performance and stability. Consequently, multi-objective optimization is employed to enhance the impact performance and stability of hydraulic impact hammers. In comparison with the baseline configuration, the optimized configuration attains a 4.6% increase in impact power and an 11.3% reduction in working pressure. Furthermore, a performance analysis surrogate model for the hydraulic impact hammer was developed to enhance the computational efficiency of multi-objective optimization. Research indicates that the errors in impact power and working pressure between the optimized configuration of the surrogate model trained using Elliptic Basis Functions (EBF) and that of the traditional model are minimal, at 0.50% and 0.30%, respectively. In comparison with the baseline configuration, the EBF-trained surrogate model optimization configuration increases impact power by 4.1% and reduces working pressure by 11.0%. Finally, experimental validation confirms the accuracy of both the traditional model and surrogate model optimization configurations. In summary, this study provides a reliable theoretical foundation and experimental support for the multi-objective optimization of hydraulic impact hammers, offering a clear paradigm for their multi-objective optimization design.</p>

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Research and experiments on the performance optimization of hydraulic impact hammers

  • Hu Chen,
  • Boqiang Shi,
  • Hui Guo,
  • Zirui Liu,
  • Bingbing Liu

摘要

In order to enhance the impact performance and stability of hydraulic impact hammers, this paper first conducts a Design of Experiments (DOE) study on hydraulic impact hammers, identifying ten key parameters that influence their impact performance and stability. Consequently, multi-objective optimization is employed to enhance the impact performance and stability of hydraulic impact hammers. In comparison with the baseline configuration, the optimized configuration attains a 4.6% increase in impact power and an 11.3% reduction in working pressure. Furthermore, a performance analysis surrogate model for the hydraulic impact hammer was developed to enhance the computational efficiency of multi-objective optimization. Research indicates that the errors in impact power and working pressure between the optimized configuration of the surrogate model trained using Elliptic Basis Functions (EBF) and that of the traditional model are minimal, at 0.50% and 0.30%, respectively. In comparison with the baseline configuration, the EBF-trained surrogate model optimization configuration increases impact power by 4.1% and reduces working pressure by 11.0%. Finally, experimental validation confirms the accuracy of both the traditional model and surrogate model optimization configurations. In summary, this study provides a reliable theoretical foundation and experimental support for the multi-objective optimization of hydraulic impact hammers, offering a clear paradigm for their multi-objective optimization design.