유압 드리프터의 동적성능 최적화 사례

Case of Dynamic Performance Optimization for Hydraulic Drifter

Abstract

그간 국내 유압 드리프터는 해외 선진사의 제품을 벤치마킹하여 개발되었지만, 이는 한국형 유압드릴 파워팩 특성(대유량, 저압)에 적합하지 않아 우수한 타격성능을 나타내지 못하였으며, 이로 인해 최적설계에 대한 연구 진행도 미진한 상태였다. 이에 본 연구에서는 대유량을 소화할 수 있도록 용량이 재설계된 유압 드리프터에 대해 다목적함수 최적화를 수행하고, 이를 통해 타격출력 향상과 공급압력 및 서지압력을 감소시키는 것을 목표로 한다. 이러한 연구의 진행방법을 요약하면 다음과 같다. 먼저 타격출력과 공급압력 및 서지압력 개선에 대한 목표를 설정하고 이에 대한 다목적함수 최적화를 수행한다. 이후 최적화된 설계치로 제작된 시제품의 시험결과와 해석모델의 해석결과 비교를 통해 최적화된 해석모델의 신뢰성을 확보한다. 본 연구에서는 유압시스템 해석 소프트웨어인 SimulationX와 다목적함수 최적화 프로그램인 EasyDesign이 사용되었으며, 위와 같은 연구를 통하여 한국형 유압드릴에 적합한 고출력 드리프터 개발 목적에 부합하는 결과를 얻을 수 있었다.

Domestic hydraulic drifters till now have been developed by benchmarking products from overseas leading companies. However, they do not have excellent impact performance as they are not suitable for characteristics (large flow rate and low pressure) of Korean hydraulic drill power pack, and therefore, research on the optimum design has not made much headway. This study performs multi-objective function optimization for hydraulic drifters whose capacity has been redesigned to deal with the large flow rate, and also with the help of this function, it aims to improve impact power and reduce supply and surge pressure. A summary of the research study is as follows: First, we set goals for improving impact power, supply pressure, and surge pressure, and then perform multi-objective function optimization on them. After that, we secure the reliability of the optimized analytical model by comparing the test results of the prototype built by the optimized design with the analysis results of the analytical model. This study used SimulationX, that is the hydraulic system analysis software, and EasyDesign, which is a multi-objective function optimization program. Through this research, we have achieved the results that satisfy the goal of developing high power drifters suitable for Korean type hydraulic drills.

Fig. 1. Drawing of drifter

Fig. 2. Analysis model of drifter

Fig. 3. Convergency history in optimization process

Fig. 4. Comparison of Impact energy and impact frequency in 4 variable combinations

Fig. 5. Comparison of impact energy before and after optimization

Fig. 6. Comparison of impact frequency before and after optimization

Fig. 7. Comparison of overall impact performance before and after optimization

Fig. 8. Comparison of surge pressure before and after optimization

Fig. 9. Comparison of supply line formation pressure characteristics

Fig. 10. Comparison of input-output power characteristic

Fig. 11. Comparison of impact efficiency

Fig. 12. Test bench experiment

Fig. 13. Hydraulic drill experiment

Fig. 14. Comparison of drifter’s characteristic between simulation and experimental results

Fig. 15. Overall impact performance of drifter considering leakage

Table 1. Multi-objective function optimization goal setting

Table 2. Creation process of the initial meta model

Table 3. Process of sequential approximate optimization

Table 4. Comparison of relative error between simulation and experimental results

Table 5. Comparison of potential limit performance before and after optimization