Abebe Misganaw, Cho Yonggeun, Han Seung Chul, Koo Bonyong
Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea.
Korea Construction Equipment Technology Institute, 52, Saemangeumsandan 2-ro, Gunsan 54002, Republic of Korea.
Sensors (Basel). 2024 May 23;24(11):3347. doi: 10.3390/s24113347.
The advent of digital twins facilitates the generation of high-fidelity replicas of actual systems or assets, thereby enhancing the design's performance and feasibility. When developing digital twins, precise measurement data is essential to ensure alignment between the actual and digital models. However, inherent uncertainties in sensors and models lead to disparities between observed and predicted (simulated) behaviors. To mitigate these uncertainties, this study originally proposes a multi-objective optimization strategy utilizing a Gaussian process regression surrogate model, which integrates various uncertain parameters, such as load angle, bucket cylinder stroke, arm cylinder stroke, and boom cylinder stroke. This optimization employs a genetic algorithm to indicate the Pareto frontiers regarding the pressure exerted on the boom, arm, and bucket cylinders. Subsequently, TOPSIS is applied to ascertain the optimal candidate among the identified Pareto optima. The findings reveal a substantial congruence between the experimental and numerical outcomes of the devised virtual model, in conjunction with the TOPSIS-derived optimal parameter configuration.
数字孪生的出现促进了实际系统或资产的高保真复制品的生成，从而提高了设计的性能和可行性。在开发数字孪生时，精确的测量数据对于确保实际模型与数字模型的一致性至关重要。然而，传感器和模型中固有的不确定性会导致观测行为与预测（模拟）行为之间存在差异。为了减轻这些不确定性，本研究最初提出了一种利用高斯过程回归代理模型的多目标优化策略，该模型整合了各种不确定参数，如负载角度、铲斗油缸行程、动臂油缸行程和斗杆油缸行程。该优化采用遗传算法来指示动臂、斗杆和铲斗油缸上所施加压力的帕累托前沿。随后，应用理想解法（TOPSIS）来确定在已识别的帕累托最优解中最优的候选解。研究结果表明，所设计的虚拟模型的实验结果与数值结果之间存在高度一致性，同时也与通过理想解法得出的最优参数配置相符。
