Development of Parameter Optimization System using Iterative Experiment and Optimization for Injection Foam Molding

Kyung-min Lee(이경민) 제어로봇시스템학회 2017 제어·로봇·시스템학회 논문지 Vol.23 No.12

Injection molding (IM) is one of the most important processes for mass-producing plastic products. There are several significant challenges in using IM. The IM process requires many input parameters, but the relationships between the desired material properties and parameter settings (e.g. gas content and pressure drop) are not well understood collectively. We propose an optimization-based computational framework that will provide computer-based decision support for setting parameters in the IM process. The decision support will enable dramatic time and cost efficiencies in that the settings for parameters. It can discover optimized parameters much more rapidly than conventional methods that require extensive experimentation. Key elements in the framework involve approximating the governing equations using analysis of variance (ANOVA) techniques and normative optimization modeling to achieve optimal parameter settings. We illustrate the computational framework on HDPE materials in which parameter settings such as gate geometry, N2 content, void fraction, and injection speed are considered. The proposed framework will provide an improved understanding of the relationships between material properties and parameter settings in general IM process environments.

Research on PID Parameter Tuning Based on Intelligent Fusion Optimization Algorithm in Control System

Gai-lian Zhang 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.6

This article will mainly introduce the effective access to get the value of PID parameter, and the method of tuning PID parameters is studied from two aspects. To ensure the PID parameter value of the whole set is optimal, the controlled object must be given and the value of intelligent optimization algorithm also must be optimal. Then to compare and test the parameters, we can use the method of simulation and experiment. Research deep traditional Z-N method and PID structure control model, and preliminary parameter values of PID can be acquired by it. After that, an intelligent fusion optimization algorithm based on intelligent computation and simulation analysis is constructed, finally, we can use this method to tuning PID parameters’ value effectively.

Parameters Tuning via Simplex-Search based Model-Free Optimization for the Steam Generator Level Control

Guan Jiansheng,Kong Xiansong 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4

Control performance is critical to a control system. To improve the performance of the steam generator level control system, the control system parameters need to be optimized. Traditional parameters tuning methods, such as trial and error and Design of Experiments etc., are usually experience-based, cumbersome and time-consuming. To address the above inefficiencies, in this paper, the simplex-search based Model-Free Optimization (MFO) has been proposed to search for the optimal control system parameters. The optimized parameters will be gained to maximize the system’s control performance. Rather than traditional controller parameter tuning method, this method optimizes the control system by directly using measurements of control performance. An example of the PID parameters tuning for the steam generator level control was illustrated. The efficiency and the effectiveness of the Simplex-search based Model-Free Optimization – based control parameters tuning methodology has been verified through simulation experiments.

Optimization of parameters in mathematical models of biological systems

추상목,김영희 한국전산응용수학회 2008 Journal of applied mathematics & informatics Vol.26 No.1

Under pathological stress stimuli, dynamics of a biological system can be changed by alteration of several components such as functional proteins, ultimately leading to disease state. These dynamics in disease state can be modeled using differential equations in which kinetic or system parameters can be obtained from experimental data. One of the most effective ways to restore a particular disease state of biology system (i.e., cell, organ and organism) into the normal state makes optimization of the altered components usually represented by system parameters in the differential equations. There has been no such approach as far as we know. Here we show this approach with a cardiac hypertrophy model in which we obtain the existence of the optimal parameters and construct an optimal system which can be used to find the optimal parameters. Under pathological stress stimuli, dynamics of a biological system can be changed by alteration of several components such as functional proteins, ultimately leading to disease state. These dynamics in disease state can be modeled using differential equations in which kinetic or system parameters can be obtained from experimental data. One of the most effective ways to restore a particular disease state of biology system (i.e., cell, organ and organism) into the normal state makes optimization of the altered components usually represented by system parameters in the differential equations. There has been no such approach as far as we know. Here we show this approach with a cardiac hypertrophy model in which we obtain the existence of the optimal parameters and construct an optimal system which can be used to find the optimal parameters.

Parameter Optimization of SVM Based on Improved ACO for Data Classification

Wen Chen,Yixiang Tian 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.1

The parameters of support vector machine have a great influence on the learning ability and generalization ability, so an improved ant colony optimization algorithm is proposed to optimize the parameters of SVM, then an optimized SVM classifier (IMACO-SVM) is proposed for data classification. In the IMACO-SVM, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromone distribution and the improved pheromone updating method is used to submerge the heuristic factor by the residual pheromone information, in order to effectively solve the contradiction between expanding search and finding optimal solution. The selection of parameters of the SVM is regarded as a combination optimization of parameters in order to establish the objective function of combination optimization. The improved ACO algorithm with good robustness and positive feedback characteristics and parallel searching is used to search for the optimal value of objective function. In order to validate the classification effectiveness of the IMACO-SVM algorithm, some experimental data from the UCI machine learning database are selected in this paper. The classification results show that the proposed IMACO-SVM algorithm has higher classification ability and classification accuracy.

Optimal Path Planning for UAVs to Reduce Radar Cross Section

Boo-Sung Kim,Hyochoong Bang 한국항공우주학회 2007 International Journal of Aeronautical and Space Sc Vol.8 No.1

Parameter optimization technique is applied to planning UAVs(Unmanned Aerial Vehicles) path under artificial enemy radar threats. The ground enemy radar threats are characterized in terms of RCS(Radar Cross Section) parameter which is a measure of exposure to the radar threats. Mathematical model of the RCS parameter is constructed by a simple mathematical function in the three-dimensional space. The RCS model is directly linked to the UAVs attitude angles in generating a desired trajectory by reducing the RCS parameter. The RCS parameter is explicitly included in a performance index for optimization. The resultant UAVs trajectory satisfies geometrical boundary conditions while minimizing a weighted combination of the flight time and the measure of ground radar threat expressed in RCS.

Collaborative optimization for ring-stiffened composite pressure hull of underwater vehicle based on lamination parameters

Li, Bin,Pang, Yong-jie,Cheng, Yan-xue,Zhu, Xiao-meng The Society of Naval Architects of Korea 2017 International Journal of Naval Architecture and Oc Vol.9 No.4

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.

Multicriteria Optimization via Parameter Space Investigation for Ultralow-Altitude Airdrop L1 Adaptive Controller

Peng Zhu,Wen-han Dong,Yuhao Mao,Ri Liu 한국항공우주학회 2020 International Journal of Aeronautical and Space Sc Vol.21 No.1

This paper presents preliminary results of the application of the parameter space investigation method for the design of the L1 flight control system implemented on the ultralow-altitude airdrop process. In particular, the study has established the task performance level of airdrop mission which is chosen as the optimization objective in this paper. According to the characteristics of ultralow-altitude airdrop process, 12 optimization criteria are designated as the constraints during the multicriteria optimization. After two iterations, the task performance level of the airdrop is improved from “Moderate” to “Expected”. The results in the paper confirms the suitability of the parameter space investigation method for the multicriteria design optimization of an airdrop flight control system subject to desired control specifications. In addition, the robustness and dynamic response of the flight control system are significantly improved after optimization. Moreover, the analysis of optimization results has contributed to a deeper understanding of the relationship between L1 controller parameters and airdrop task performance.

Collaborative optimization for ring-stiffened composite pressure hull of underwater vehicle based on lamination parameters

BIN LI,Yong-jie Pang,Yan-xue Cheng,Xiao-meng Zhu 대한조선학회 2017 International Journal of Naval Architecture and Oc Vol.9 No.4

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.

상대선호도함수 모델을 위한 매개변수 최적화가 토지피복 변화 예측성에 미치는 영향

박종철 ( Jongchul Park ),김동우 ( Dongwoo Kim ),장동호 ( Dong Ho Jang ) 한국사진지리학회 2016 한국사진지리학회지 Vol.26 No.3

3개의 매개변수를 가지는 상대선호도함수(relative favorability function, RFF) 모델은 다양한 공간자료를 통합하여 미래 토지피복 변화를 추정할 수 있다. 본 연구는 이 매개변수들을 최적화하는 방안을 제시하고, 최적화가 토지피복 변화 예측성에 미치는 영향에 대하여 연구하였다. 연구 결과, 매개변수 최적화가 RFF 모델 수립 단계에서 충실도를 향상시킨다는 것을 확인하였다. 하지만 이 충실도가 모델의 예측성 향상과 직접적으로 연관되어 있지는 않았다. RFF 모델의 예측성은 매개변수 최적화 보다는 모델 학습 기간의 설계와 연관되어 있었다. 본 연구 결과는 매개변수 최적화가 RFF 모델의 예측 결과에 미치는 영향과 한계를 확인하였다는 점과 RFF 모델의 개선 방안을 도출 하였다는 점에서 의의가 있다. The relative favorability function (RFF) model, which has three parameters, can estimate future changes in land cover by integrating a variety of spatial data. The study suggested methods to optimize these parameters and the impact of the optimization was studied on predictability in land cover change. As a result, it was confirmed that the parameter optimization is to improve the feasibility in the established process of the RFF model. However that is not directly related to improve the predictability of the model. The predictability of the model was more associated with designation of period for model calibration than the parameter optimization. This result is meaningful that impact and limitation of the parameter optimization was confirmed in RFF modeling. Furthermore, this study is meaningful in that it was derived the improvement of the model.