Process Parameter Optimization for Machining SiCp/Al-MMC Using RSM and GA

2006 ◽  
Vol 326-328 ◽  
pp. 1213-1216
Author(s):  
Jae Seob Kwak ◽  
Long Zhu Chi ◽  
Yang Koo ◽  
Yeong Deug Jeong ◽  
Man Kyung Ha
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Metal Matrix ◽  
Response Surface Model ◽  
Surface Model ◽  
Matrix Composites ◽  
Grinding Forces ◽  
Response Surface Models ◽  
Grinding Parameters ◽  
Surface Models

This study aimed to achieve optimization of grinding parameters for aluminum-based metal matrix composites using response surface model and genetic algorithm. Experiments were conducted in accordance with a preplanned orthogonal array. The effect of grinding parameters on surface roughness and grinding forces was evaluated and second-order response surface models were developed for predicting grinding outcomes. Optimal grinding parameters were determined from the genetic algorithm and the response surface models.

A Method for Aerodynamic Characteristic Analysis of Hypersonic Aircraft Based on Response Surface Model

2013 ◽  
Vol 477-478 ◽  
pp. 277-280 ◽  
Author(s):  
Jie Yang ◽  
Song Ping Wu ◽  
Wen Xin Hou
Keyword(s):  
Flow Field ◽  
Response Surface ◽  
Aerodynamic Characteristic ◽  
Aerodynamic Characteristics ◽  
Surface Model ◽  
Complex Flow ◽  
Characteristic Analysis ◽  
Response Surface Models ◽  
Hypersonic Aircraft ◽  
Surface Models

Aerodynamic characteristic analysis of hypersonic cruise aircraft is more difficult than that of conventional aircraft, for the complex flow field simulation and inadequate amount of results under limited flight conditions. In this paper, numerical schemes applicable for hypersonic flow field are adopted to acquire a set of aerodynamic characteristics of a typical hypersonic cruise aircraft as sample data, based on which response surface models (RSM) are constructed to provide approximation of aerodynamic characteristics under any flight conditions within the design domain, finally the overall approximation performance of the response surface models are analyzed.

Cleaning and Bacteria Removal in Milking Systems by Alkaline Electrolyzed Oxidizing Water with Response Surface Design

2019 ◽  
Vol 62 (5) ◽  
pp. 1251-1258 ◽  
Author(s):  
Yu Liu ◽  
Chaoyuan Wang ◽  
Zhengxiang Shi ◽  
Baoming Li
Keyword(s):  
Water Treatment ◽  
Response Surface ◽  
Treatment Time ◽  
System Response ◽  
Surface Model ◽  
Response Surface Design ◽  
Surface Design ◽  
Response Surface Models ◽  
Surface Models ◽  
Bacteria Removal

Abstract. A wash cycle using an alkaline solution with a dissolved chemical detergent is a standard clean-in-place (CIP) process for cleaning milking systems. However, long-term chemical use may corrode equipment and create difficulties in wastewater treatment. This study investigated the potential for using alkaline electrolyzed oxidizing (EO) water as an alternative to alkaline chemical detergent for removal of microorganisms and adenosine triphosphate (ATP) on milking system materials. Laboratory trials were performed based on a Box-Behnken response surface design to assess the cleaning effect of alkaline EO water on three materials typically used in milking systems: stainless steel, rubber gasket, and polyvinyl chloride (PVC) hose. Results showed that alkaline EO water treatment was generally enhanced with increased treatment time, temperature, and pH, and their interaction effects were also observed in ATP removal. However, treatment time did not have a dominant role in cleaning PVC hose. Response surface models were developed to reliably predict detected microorganisms and relative light units (RLU) on the three materials after alkaline EO water treatment. Based on the response surface models, the three parameters for alkaline EO water cleaning were optimized as treatment time of 10.0 min, temperature of 61.8°C, and pH of 12, after which microorganisms and RLU were nearly undetectable. Alkaline EO water treatment with the optimized parameters had an equivalent or better cleaning ability compared to the commercial detergent, suggesting its potential as a cleaning and bacteria removal agent for milking systems. Keywords: Alkaline electrolyzed oxidizing water, Cleanliness, Milking system, Response surface model.

scholarly journals Sevoflurane Remifentanil Interaction

2012 ◽  
Vol 116 (2) ◽  
pp. 311-323 ◽  
Author(s):  
Bjorn Heyse ◽  
Johannes H. Proost ◽  
Peter M. Schumacher ◽  
Thomas W. Bouillon ◽  
Hugo E. M. Vereecke ◽  
...  
Keyword(s):  
Laryngeal Mask Airway ◽  
Response Surface ◽  
Hierarchical Model ◽  
Laryngeal Mask ◽  
Surface Model ◽  
Laryngeal Mask Airway Insertion ◽  
Tetanic Stimulation ◽  
Response Surface Models ◽  
Drug Concentrations ◽  
Surface Models

Background Various pharmacodynamic response surface models have been developed to quantitatively describe the relationship between two or more drug concentrations with their combined clinical effect. We examined the interaction of remifentanil and sevoflurane on the probability of tolerance to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy in patients to compare the performance of five different response surface models. Methods Forty patients preoperatively received different combined concentrations of remifentanil (0-12 ng/ml) and sevoflurane (0.5-3.5 vol.%) according to a criss-cross design (160 concentration pairs, four per patient). After having reached pseudosteady state, the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy was recorded. For the analysis of the probability of tolerance, five different interaction models were tested: Greco, Reduced Greco, Minto, Scaled C50(O) Hierarchical, and Fixed C50(O) Hierarchical model. All calculations were performed with NONMEM VI (Icon Development Solutions, Ellicott City, MD). Results The pharmacodynamic interaction between sevoflurane and remifentanil was strongly synergistic for both the hypnotic and the analgesic components of anesthesia. The Greco model did not result in plausible parameter estimates. The Fixed C50(O) Hierarchical model performed slightly better than the Scaled C50(O) Hierarchical and Reduced Greco models, whereas the Minto model fitted less well. Conclusion We showed the importance of exploring various surface model approaches when studying drug interactions. The Fixed C50(O) Hierarchical model fits our data on sevoflurane remifentanil interaction best and appears to be an appropriate model for use in hypnotic-opioid drug interaction.

Parameter Optimization of Surface Grinding Process Based on Taguchi and Response Surface Methods

2006 ◽  
Vol 306-308 ◽  
pp. 709-714 ◽  
Author(s):  
Jae Seob Kwak ◽  
In Kwan Kim
Keyword(s):  
Response Surface ◽  
Parameter Optimization ◽  
Removal Rate ◽  
Geometric Error ◽  
Response Surface Model ◽  
Surface Grinding ◽  
Surface Model ◽  
Grinding Process ◽  
Response Surface Methods ◽  
Grinding Parameters

To minimize the geometric error made in ground surface, the optimization of grinding parameters is essential. This paper focused on the parameter optimization of the surface grinding process based on Taguchi and response surface methods for minimizing the geometric error. Firstly, the effect of grinding parameters on the geometric error was evaluated and optimum grinding conditions were determined. Then, a second-order response model for predicting the geometric error was developed and the validation of the response surface model was examined with industrial constraints such as the surface roughness and the material removal rate. Finally, experimental verification was conducted at an optimal condition and two selected conditions to see accuracy of the developed response surface model.

Optimization of micro-grinding process with compressed air using response surface methodology

2011 ◽  
Vol 225 (11) ◽  
pp. 2040-2050 ◽  
Author(s):  
P-H Lee ◽  
H Chung ◽  
S W Lee
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Compressed Air ◽  
Depth Of Cut ◽  
Grinding Process ◽  
Grinding Forces ◽  
Response Surface Models ◽  
Surface Models ◽  
Grinding Conditions

This paper addresses the optimization of a micro-grinding process using compressed air to minimize specific grinding forces and surface roughness while maximizing specific material removal rate (MRR). The design-of-experiments (DOE) approach and response surface methodology (RSM) are introduced to obtain the optimal grinding conditions. In the DOE approach, a central composite design approach is used for experimental design. Micro-grinding experiments are conducted, and the experimental results are used to obtain response surface models of specific grinding forces and surface roughness in terms of depth of cut, feed rate and air temperature. Multi-objective optimization is then conducted by introducing desirability functions, and the optimal values of depth of cut, feed rate and air temperature are obtained for minimum specific grinding forces and surface roughness and maximum specific MRR. The experimental results under the optimal grinding conditions are similar to those estimated from the response surface models, and thus the validity of the models is verified.

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