Dual Response Surface Research Articles

Technical Study on Improvement of Endurance Capability of Limit Short-circuit Current of Charge Control SMART Meter

Smart meter represents the development direction of energy-saving smart grid in the future. The load switch, one of the core parts of smart meter, should be of high reliability, safety and endurance capability of limit short-circuit current. For this reason, this paper discusses the quick simulation of relationship between attraction and counterforce of load switch without iteration, establishes dual response surface model of attraction and counterforce and optimizes the design scheme of load switch for charge control smart meter, thus increasing electromagnetic attraction and spring counterforce. In this way, this paper puts forward a method to improve the withstand capacity of limit short-circuit current.

Generating evenly distributed nondominated solutions in dual response surface optimization

The dual response surface optimization (DRSO) approach attempts to simultaneously optimize the mean and standard deviation of a response variable. In DRSO, the mean and standard deviation of a response are often in conflict and thus, it is extremely critical to compromise the two conflicting functions. Recently, a posterior preference articulation approach for DRSO (P-DRSO) was proposed. P-DRSO generates a set of nondominated solutions and allows a decision-maker (DM) to select the most preferred solution. P-DRSO has an advantage in that the DM can better understand the trade-offs between the mean and standard deviation. Thus, the most satisfactory compromised solution can be easily obtained. One important issue of P-DRSO is to generate uniformly distributed nondominated solutions for the DM to obtain better understanding of the trade-offs between the mean and standard deviation. P-DRSO employs a conventional ε-constraint method. However, it often generates nondominated solutions that are not uniformly distributed. To address this issue, we propose a new approach to generate the nondominated solutions. We demonstrated that the uniformity of the generated nondominated solutions is better than that of P-DRSO in two well-known DRSO case problems.

Multi-objective robust design optimization of a novel NPR energy absorption structure for vehicles front ends to enhance pedestrian lower leg protection

The unique structure of negative Poisson’s ratio (NPR) materials provides a good energy absorption capacity that has found wide application in automotive and aeronautics industries. The present work proposes a novel NPR energy absorption structure installed between the bumper and anti-collision beam of automobiles for improving pedestrian lower leg protection. The performance of the proposed NPR structure based on tibia acceleration, knee bending angle, and knee shear displacement is evaluated by comparison with the performance of a conventional energy absorption structure. The performance of the proposed structure is further improved by conducting multi-objective design optimization with consideration for the perturbation induced by parameter uncertainties. For optimization, a parametric model of the NPR structure is first established based on the relationships between parameters. To promote computational efficiency, an optimal Latin hypercube sampling technique and the dual response surface method are combined to build surrogate models between responses and inputs. A multi-objective particle swarm optimization algorithm and six sigma criteria are then applied to obtain the optimal design parameters of the structure. The optimization results are validated by comparisons with the results of multi-objective deterministic optimization. The comparison results show that the proposed NPR energy absorption structure improves pedestrian lower leg protection significantly, and its performance is further improved by the proposed multi-objective robust design optimization. This study serves as a good example of the safety promotion provided by applying NPR structures with enhanced energy absorption capacity in vehicles.

다목적 유전 알고리즘을 이용한 쌍대반응표면최적화

Dual response surface optimization (DRSO) attempts to optimize mean and variability of a process response variable using a response surface methodology. In general, mean and variability of the response variable are often in conflict. In such a case, the process engineer need to understand the tradeoffs between the mean and variability in order to obtain a satisfactory solution. Recently, a Posterior preference articulation approach to DRSO (P-DRSO) has been proposed. P-DRSO generates a number of non-dominated solutions and allows the process engineer to select the most preferred solution. By observing the non-dominated solutions, the DM can explore and better understand the trade-offs between the mean and variability. However, the non-dominated solutions generated by the existing P-DRSO is often incomprehensive and unevenly distributed which limits the practicability of the method. In this regard, we propose a modified P-DRSO using multiple objective genetic algorithms. The proposed method has an advantage in that it generates comprehensive and evenly distributed non-dominated solutions.

Application of Dual-Response Surface Methodology and Radial Basis Function Artificial Neural Network on Surrogate Model of the Groundwater Flow Numerical Simulation

Application of Dual-Response Surface Methodology and Radial Basis Function Artificial Neural Network on Surrogate Model of the Groundwater Flow Numerical Simulation

Improvised Desirability Function for Dual Response Surface

Quality engineering practitioners have great interest for using response surface method in a real situation. Recently, dual response surface has been widely used extensively and is known as one powerful tool for robust design. However, existing methods do not consider the information provided by customers and design engineers. One of the methods that can be used to simultaneously optimize multiple responses is by using the desirability function technique. In this technique, the desirability function approach uses a dimensionality reduction approach that converts multiple predicted responses into a single response problem. The traditional procedures construct the process location and process scale based on sample mean and sample variance respectively. Then, for the regression fitting, the Ordinary Least Square (OLS) method is usually used to acquire the sufficient response functions for the process location and scale based on mean and variance. Nevertheless, these existing procedures are easily influenced by outliers. As an alternative, we propose an improvised desirability function for dual response (IDFDR) to rectify this problem. A numerical example is presented to assess the performance of the IDFDR method. The numerical results signify that the IDFDR method is more efficient than the existing methods.

Multi-objective robust design optimization of a novel negative Poisson’s ratio bumper system

Negative Poisson’s ratio (NPR) structure has outstanding performances in lightweight and energy absorption, and it can be widely applied in automotive industries. By combining the front anti-collision beam, crash box and NPR structure, a novel NPR bumper system for improving the crashworthiness is first proposed in the work. The performances of the NPR bumper system are detailed studied by comparing to traditional bumper system and aluminum foam filled bumper system. To achieve the rapid design while considering perturbation induced by parameter uncertainties, a multi-objective robust design optimization method of the NPR bumper system is also proposed. The parametric model of the bumper system is constructed by combining the full parametric model of the traditional bumper system and the parametric model of the NPR structure. Optimal Latin hypercube sampling technique and dual response surface method are combined to construct the surrogate models. The multi-objective robust optimization results of the NPR bumper system are then obtained by applying the multi-objective particle swarm optimization algorithm and six sigma criteria. The results yielded from the optimizations indicate that the energy absorption capacity is improved significantly by the NPR bumper system and its performances are further optimized efficiently by the multi-objective robust design optimization method.

Using dual response surface methodology as a benchmark to process multi-class imbalanced data

Constructing a classification model for the multi-class data is a critical problem in many areas. In practical applications, data in multiple classes are often imbalanced which might result in a classification model with high overall accuracy rate but with low accuracy rate for the minority class. However, minority class is usually the more important one compared to other classes in practice. This study integrates dual response surface methodology, logistic regression analysis, and desirability function to develop an optimal re-sampling strategy for classifying multi-class imbalanced data to effectively improve the low classification accuracy rate of the minority class(es) while still maintain a certain accuracy rate for the majority class(es). Three data-sets drawn from KEEL Database were used in the numerical experiments. The results showed that the proposed method can effectively improve the low classification accuracy rate of the minority class in contrast to the previous work.

Multi-objective reliability design optimization of electric multiple unit pantograph geometric parameters

In order to reduce the design variable fluctuations in the multi-objective optimization of the pantograph geometric parameters of electric multiple units, a multi-objective reliability design optimization method was proposed based on the dual response surface method in this article. The corresponding model of the pantograph was built by integrating the dual response surface method, the reliability indexes, and fuzzy preference method. Considering different working conditions and reliability index, the reliability optimal design target was realized by optimizing the established model. A case was studied to verify the feasibility and effectiveness of the proposed method.

Development of Brönsted acidic ionic liquid based microwave assisted method for simultaneous extraction of pectin and naringin from pomelo peels

Abstract Bronsted acidic ionic liquid was introduced in microwave assisted extraction procedure to simultaneously extract pectin and naringin from the albedo part of pomelo peels. The process for the extraction of pectin and naringin was optimized by a dual response surface methodology. The optimal conditions as follows: [HO 3 S(CH 2 ) 4 mim]HSO 4 concentration of 10 mmol/L, microwave irradiation time of 15 min, liquid–solid ratio of 26 mL/g, and microwave irradiation power of 331 W. Under the optimum conditions, the yields reached to 291.60 ± 7.25 mg/g for pectin and 8.38 ± 0.20 mg/g for naringin. [HO 3 S(CH 2 ) 4 mim]HSO 4 enjoyed excellent solvent effect for the extraction of pectin and naringin in microwave assisted extraction method in comparing with reference solvents. Fourier transform infrared spectroscopy, scanning electron microscope, and atomic force microscope were employed for the characterization analysis of pectin. Interestingly, the pectin extracted with [HO 3 S(CH 2 ) 4 mim]HSO 4 aqueous solution possessed relatively low degree of esterification and rough, hard and network structures which were apparently different with that extracted with hydrochloric acid solution. Additionally, 190.37 ± 3.28 mg naringin with a purity of 95.23% ± 1.3% were gained from 50 g albedo part of pomelo peels and confirmed by nuclear magnetic resonance, Fourier transform infrared spectroscopy, and mass spectroscopy analysis. This study provides a promising paradigm in modern separation science for the extraction of natural products from plant sources.

Optimizing a blend of a mixture slurry in chemical mechanical planarization for advanced semiconductor manufacturing using a posterior preference articulation approach to dual response surface optimization

Semiconductors are fabricated through unit processes including photolithography, etching, diffusion, ion implantation, deposition, and planarization processes. Chemical mechanical planarization, which is essential in advanced semiconductor manufacturing processes, aims to achieve high planarity across the wafer surface. This paper presents a case study in which the optimal blend of mixture slurry was obtained to improve the two response variables (material loss and roughness) at the same time. The mixture slurry consists of several pure slurries; when all of the abrasive particles within the slurry are of the same size, the slurry is referred to as a pure slurry. The optimal blend was obtained by applying a multiresponse surface optimization method. In particular, the recently developed posterior approach to dual response surface optimization was employed, which allows the chemical mechanical planarization process engineer to investigate tradeoffs between the two response variables. The two responses were better with the obtained blend than the existing blend. Copyright © 2016 John Wiley & Sons, Ltd.

Dual Responsive Surfaces Based on Host–Guest Interaction for Dynamic Mediation of Cell–Substrate Interaction and Cell Migration

A dual responsive surface is constructed on hydrophilic polymer brushes to modulate cell migration ability on demand. The responsibility is based on the dynamic host–guest interaction between β-CD and azobenzene. Both competitive guest molecules and UV radiation are able to release azobenzene-REDV from the substrate, weakening the cell–substrate interaction and modulating the cell migration rate. As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Reliability-based performance optimization of TMD for vibration control of structures with uncertainty in parameters and excitation

Recent development of system identification using Bayesian models or stochastic filtering provides probabilistic descriptions (i.e., probability density function or statistical parameters like mean and variance) of the identified model parameters (e.g., mass, stiffness, and damping). Optimal design of passive controllers for these systems whose parameters are uncertain has remained an open problem. With this in view, the present study aims to develop numerical solution scheme for the optimal design of tuned mass damper (TMD) operating in uncertain environment. Deterministic design of TMD in these cases suffers detuning as the system parameters are random. Thus, a reliability-based design optimization (RBDO) scheme is presented in this paper for better performance of the TMD when exposed to uncertainties. To solve the RBDO problem, response surface methodology is used along with the moving least squares technique. Dual response surfaces are used for separate handling of optimization and reliability analysis. First response surface performs optimization of the design variables of TMD, while the second response surfaces are used for the estimation of the statistical properties like mean and variance to satisfy the constrained conditions. Numerical analysis is presented to show the effectiveness of the proposed algorithm for RBDO of single degree of freedom-TMD system as a proof of concept. The proposed meta-model-based algorithm can be applied for the optimal design of controller for large structures where conventional technique may face difficulty to handle both optimization and uncertainty quantification simultaneously. Copyright © 2016 John Wiley & Sons, Ltd.

Ionic liquid–lithium salt based microwave pretreatment followed by ultrasonic-assisted extraction of syringin and oleuropein from Syringa reticulata var. mandshurica branch bark by a dual response surface methodology

An ionic liquid/lithium salt solvent system was successfully introduced into the separation technique for the preparation of syringin and oleuropein fromSyringa reticulatavar.mandshuricabranch bark.

A highly efficient robust design under data contamination

In robust design, the general assumption is that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and variance are often used to estimate the process mean and the process variance at each design point. The process mean and variance response functions are then fitted based on the sample mean and variance obtained at each design point.However, the non-contamination component of the assumption may not hold in practice. If this assumption is violated in a serious manner, the optimal operating conditions of the control factors may not be estimated properly. Thus, an outlier-resistant approach to the robust design problem is clearly warranted.In this article, we estimate a dual quadratic response surface model for the expected value and logarithm-transformed variance of the target response. The response surface is estimated from a designed experiment with a common number of replications at each design point. Then, given the observed responses at each design point, the following outlier-resistant statistics are computed at each design point: (i) the median and Hodges Lehmann estimators of the mean response, and (ii) the natural logarithm of the squared normal-consistent MAD, IQR and Shamos estimators of the response standard deviation.Through extensive Monte Carlo simulations, we show that alternative estimators exist which are quite efficient when the data are normally distributed yet also outlier-resistant when the data are contaminated.

Design optimization of RF-MEMS switch considering thermally induced residual stress and process uncertainties

This paper presents the Design of Experiments (DOE) based parametric design optimization of the Symmetric Toggle RF-MEMS Switch (STS) for minimizing the actuation voltage considering the fabrication process uncertainties and thermally induced residual stress. Initially, three-dimensional (3D) non-linear Finite Element Method (FEM) models are developed and the formation of residual stress during the plasma etching step of the microfabrication process is explained using the Bauschinger effect. The pull-in voltage values and the switch profiles obtained after the thermal loading–unloading cycle in the FEM models are compared with the experimental values and optical profile measurements which showed a close agreement. A DOE based Dual Response Surface Methodology (DRSM) is implemented to identify the significant design parameters affecting the STS switch pull-in voltage in the presence of thermally induced residual stress. Two separate response surface empirical models are developed; one for the mean pull-in voltage and other for variation in the pull-in voltage due to microfabrication process tolerances. The developed response surface models are optimized simultaneously using the desirability function approach. The optimal levels of the design parameters that result in minimum pull-in voltage with increased insensitivity to process uncertainties are obtained using the direct search algorithm.

The multi-objective robust optimization of the loading path in the T-shape tube hydroforming based on dual response surface model

In this study, a dual response surface model-based multi-objective robust optimization method is introduced to deal with the uncertainties in the tube hydroforming process. The objective of this study is to maximize the protrusion height and minimize the thinning ratio; meanwhile, the variations of the objectives should be minimized. A valid finite element model obtained from experimental result and LS-DYNA is employed to simulate the T-shape tube hydroforming process. To improve computation efficiency, radial basis function combined with Latin hypercube and orthogonal design sampling strategies is employed to construct dual response surface model, which are the mean and standard deviation response of the hydroforming process, respectively. The robust Pareto solutions can be obtained using NSGA-II; meanwhile, the ideal point method is used to obtain the most satisfactory solution from the Pareto solutions for the design engineers. As a conclusion, a significant improvement of the robustness can be achieved; however, the mean performance of the protrusion height has to be sacrificed.

An Alternative Approach of Dual Response Surface Optimization Based on Penalty Function Method

The dual response surface for simultaneously optimizing the mean and variance models as separate functions suffers some deficiencies in handling the tradeoffs between bias and variance components of mean squared error (MSE). In this paper, the accuracy of the predicted response is given a serious attention in the determination of the optimum setting conditions. We consider four different objective functions for the dual response surface optimization approach. The essence of the proposed method is to reduce the influence of variance of the predicted response by minimizing the variability relative to the quality characteristics of interest and at the same time achieving the specific target output. The basic idea is to convert the constraint optimization function into an unconstraint problem by adding the constraint to the original objective function. Numerical examples and simulations study are carried out to compare performance of the proposed method with some existing procedures. Numerical results show that the performance of the proposed method is encouraging and has exhibited clear improvement over the existing approaches.

A dual response surface optimization methodology for achieving uniform coating thickness in powder coating process

A dual response surface optimization methodology for achieving uniform coating thickness in powder coating process

쌍대반응표면최적화를 위한 가중평균제곱오차 최소화법: 공정능력지수 기반의 가중치 결정

Purpose: The purpose of this paper is to develop a systematic weighting procedure based on process capability indices method applying weighted mean squared error minimization (WMSE) approach to dual response surface optimization. Methods: The proposed procedure consists of 5 steps. Step 1 is to prepare the alternative vectors. Step 2 is to rank the vectors based on process capability indices in a pairwise manner. Step 3 is to transform the pairwise rankings into the inequalities between the corresponding WMSE values. Step 4 is to obtain the weight value by calculating the inequalities. Or, step 5 is to obtain the weight value by minimizing the total violation amount, in case there is no weight value in step 4. Results: The typical 4 process capability indices, namely, <TEX>$C_p$</TEX>, <TEX>$C_{pk}$</TEX>, <TEX>$C_{pm}$</TEX>, <TEX>$C_{pmk}$</TEX> are utilized for the proposed procedure. Conclusion: The proposed procedure can provide a weight value in WMSE based on the objective quality performance criteria, not on the decision maker's subjective judgments or experiences.