• 소성∙가공
• /
• 제30권3호
• /
• pp.125-133
• /
• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2004년도 추계학술대회
• /
• pp.870-875
• /
• 2004

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, the robust design strategy is developed based on the DACE and the global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the system. The robustness is determined by the DACE model to reduce the real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.3-5
• /
• 2005

In this paper. we introduce a new structure of fuzzy-neural networks Fuzzy Set-based Polynomial Neural Networks (FSPNN). The two underlying design mechanisms of such networks involve genetic optimization and information granulation. The resulting constructs are Fuzzy Polynomial Neural Networks (FPNN) with fuzzy set-based polynomial neurons (FSPNs) regarded as their generic processing elements. First, we introduce a comprehensive design methodology (viz. a genetic optimization using Genetic Algorithms) to determine the optimal structure of the FSPNNs. This methodology hinges on the extended Group Method of Data Handling (GMDH) and fuzzy set-based rules. It concerns FSPNN-related parameters such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of a specific subset of input variables realized through the mechanism of genetic optimization. Second, the fuzzy rules used in the networks exploit the notion of information granules defined over systems variables and formed through the process of information granulation. This granulation is realized with the aid of the hard C-Means clustering (HCM). The performance of the network is quantified through experimentation in which we use a number of modeling benchmarks already experimented with in the realm of fuzzy or neurofuzzy modeling.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.99.6-99
• /
• 2001

The parameter optimization for agile beam radar tracking is addressed to minimize the radar resources that are required to maintain a target under track. The parameters to be optimized include the track-revisit interval and the sequence of pairs of target signal strengths and detection thresholds associated with repeated illumination attempts in each track-revisit. The optimization problem is solved numerically for typical examples.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2011년도 춘계학술대회 논문집
• /
• pp.1113-1114
• /
• 2011

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
• /
• pp.18-18
• /
• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• 한국항공우주학회지
• /
• 제42권2호
• /
• pp.108-118
• /
• 2014

근사모델을 이용한 최적설계 문제에서는 설계변수의 수가 증가함에 따라 근사모델의 정확도를 확보하기 위한 계산 횟수가 급격히 증가한다. 이를 해결하기 위해 저정확도 모델을 바탕으로 고정확도 모델로 보정하는 Variable-Fidelity Modeling을 이용하였다. 본 논문에서 Variable-Fidelity Model로는 계층적 크리깅 모델을 이용하였으며, 다목적 유전자 알고리즘과 결합하여 최적화 프레임워크를 제안하였다. 이 방법의 유용성을 검증하기 위하여 천음속 영역에 대한 익형 최적 설계를 하였다. 설계변수로는 PARSEC의 파라메터를 이용하였으며, 서로 다른 격자수를 가지는 경우 그리고 서로 다른 정확도를 가지는 해석자를 이용한 경우에 관하여 해석을 수행하였다. 검증을 위해 단일 정확도 모델에 대한 최적화 결과와 비교하였다. 모든 경우에 관하여 파레토 라인이 유사하게 나오는 것을 확인 할 수 있었으며, 계산시간은 계층적 크리깅 모델을 이용한 Variable-Fidelity Model이 단일 정확도 모델에 비하여 훨씬 줄어들었다. 이를 바탕으로 본 논문의 방법이 단일 정확도를 가지는 모델에 대한 최적화 방법과 유사한 정확도를 가지며 더욱 효율적임을 확인 할 수 있다.

• 한국전기전자재료학회:학술대회논문집
• /
• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
• /
• pp.33-36
• /
• 2004

The process modeling for the growth rate in pulsed laser deposition(PLD)-grown ZnO thin films is investigated using neural networks(NNets) and the process recipes is optimized via genetic algorithms(GAs). D-optimal design is carried out and the growth rate is characterized by NNets based on the back-propagation(BP) algorithm. GAs is then used to search the desired recipes for the desired growth rate. The statistical analysis is used to verify the fitness of the nonlinear process model. This process modeling and optimization algorithms can explain the characteristics of the desired responses varying with process conditions.

• Journal of Computing Science and Engineering
• /
• 제6권4호
• /
• pp.294-309
• /
• 2012

In the age of multi-core and specialized accelerators in high performance computing (HPC) systems, it is critical to understand application characteristics and apply suitable optimizations in order to fully utilize advanced computing system. Often time, the process involves multiple stages of application performance diagnosis and a trial-and-error type of approach for optimization. In this study, a general guideline of performance optimization has been demonstrated with two class-representing applications. The main focuses are on node-level optimization and inter-node scalability improvement. While the number of optimization case studies is somewhat limited in this paper, the result provides insights into the systematic approach in HPC applications performance engineering.

• JSTS:Journal of Semiconductor Technology and Science
• /
• 제5권4호
• /
• pp.229-236
• /
• 2005

This paper presents a system level architecture evaluation technique that leverages transaction level modeling but also significantly extends it to the realm of system level performance evaluation. A major issue lies with the modeling effort. To reduce the modeling effort the proposed technique develops the concept of worst case scenarios. Since the memory controller is often found to be an important component that critically affects the system performance and thus needs optimization, the paper further addresses how to evaluate and optimize the memory controllers, focusing on the test environment and the methodology. The paper also presents an industrial case study using a real state-of-the-art design. In the case study, it is reported that the proposed technique has helped successfully find the performance bottleneck and provide appropriate feedback on time.