Thammasat University Research Unit in Industrial Statistics and Operational Research, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand.
Industrial and Production Management Research Unit, Faculty of Engineering at Sriracha, Kasetsart Univeristy Sriracha Campus, Chonburi, 20230, Thailand.
Sci Rep. 2023 May 31;13(1):8855. doi: 10.1038/s41598-023-35119-2.
This study proposes a novel hybrid approach, called Adaptive/Elevator Kinematics Optimization algorithm based on dual response algorithm (A/EKO-DRA), to enhance the robust parameters estimation and design of the plaster milling process. The A/EKO-DRA method reduces variability while maintaining the desired output target, thereby minimizing the impact of variance on the expected stucco combined water. The performance of the A/EKO-DRA is compared with conventional processes through numerical examples and simulations. The results show that the A/EKO-DRA method has the lowest mean absolute errors among other methods in terms of parameter estimation, and it achieves the response mean of 5.927 percent, which meets the target value of 5.9 percent for industrial enclosures, with much reduction in the response variance. Overall, the A/EKO-DRA method is a promising approach for optimizing the plaster milling process parameters.
本研究提出了一种新颖的混合方法，称为基于对偶响应算法的自适应/电梯运动学优化算法（A/EKO-DRA），以增强石膏铣削过程的稳健参数估计和设计。A/EKO-DRA 方法在保持期望输出目标的同时减少了可变性，从而最小化了方差对预期灰泥结合水的影响。通过数值示例和模拟比较了 A/EKO-DRA 的性能与传统工艺。结果表明，在参数估计方面，A/EKO-DRA 方法的平均绝对误差最低，其响应均值为 5.927%，达到了工业外壳 5.9%的目标值，响应方差大大降低。总的来说，A/EKO-DRA 方法是优化石膏铣削工艺参数的一种很有前途的方法。
