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About 10,000 papers for “multi-response optimization”

Multi Response Optimization of NO

S. Saravanan, G. Nagarajan, S. Sampath · Springer Link (Chiba Institute of Technology) · 2012

In the present work, an attempt was made to reduce the NOx emission of crude rice bran oil methyl ester without any considerable increase in smoke density, when used as a fuel in a stationary CI engine. Three factors namely, fuel injection timing, Exhaust Gas Recirculation (EGR) and fuel injection pressure were chosen and their combined effect in controlling the NOx emission of a stationary Diesel engine fuelled with crude rice bran oil methyl ester was investigated. Three levels were chosen in each factor and NOx emission, smoke density and brake thermal efficiency were taken as the response variables. Experiments were designed by employing design of experiments method and Taguchi’s L9 orthogonal array was used to conduct the engine tests with different levels of the chosen factors. Multi Response Signal-to-Noise ratio (MRSN) was calculated for the response variables and the optimum combination level of factors was obtained simultaneously using Taguchi’s parametric design. Confirmation experiment was conducted for the obtained optimum combination level of factors and the results were compared with normal operating conditions and significant improvement was observed in the response variables.

Cited by 0Open access

Improved TOPSIS Methods for Multi-Response Optimization

Ying Wang, Zhen He · 2008

To find the optimal combination of parameters in product or process design, there are always more than one response needed to be optimized simultaneously, which is called as optimization of multi-response problems. TOPSIS (technique for order preference by similarity to ideal solution) method, as a multiple attribute decision making (MADM) methodology, is often used for solving problems with multivariate quality characteristics. In conventional TOPSIS method, responses are usually transformed into loss of quality, and then normalized by the largest quality loss. Based on the conventional TOPSIS method, two improved methods are proposed and illustrated by an example from literature, the two methods gain similar value from optimization, and the result indicates their reasonableness and effectiveness.

Cited by 8

Simulated Six Sigma: A multi response optimization

Abdallah A. Abdallah · Quality Engineering · 2020

Six Sigma is widely used in many industries for the purpose of improving process performance. Typical improvements sought in Six Sigma projects include decreased operational cost, increased customer satisfaction, decreased cycle time, and enhanced profits, but regardless of the nature of the improvement, all projects follow the “Define-Measure-Analyze-Improve-Control” (DMAIC) methodology.This paper proposes the use of simulation and multi response optimization in addition to other typical Six Sigma tools in order to arrive at optimum performance at the end of each project through an established optimization framework. The framework is used in a real case study performed at a global logistics company.The case study is concerned with an e-commerce logistics station located at John F. Kennedy airport (JFK) in New York; a station that suffers from low on-time delivery performance and high operational cost. As result, a Six Sigma project was implemented and the process was simulated to reveal various scenarios suggested by the Six Sigma team in order to optimize the process.The project resulted in 65 percent reduction in labor, 41 percent reduction in work in process (WIP) and 55 percent reduction in average time packages spend at the station. These improvements highly support the effectiveness of implementing Six Sigma projects in the format suggested.

Cited by 12

Multi-response optimization in cutting mild steels

Yusuf Şahin, Demiral Akbar · Applied Chemical Engineering · 2023

Machine tools are very important metal cutting process that used widely in manufacture/construction and energy sector. Material removal rate in any metal cutting process is very important because it significantly affects the production rate, generated energy/forces, tool life. Improper choice of the machine tools, cutting tools or parameters will lead to be produced early wear, more energy and deterioration of surface qualities of machined mechanical components. The cutting process should be controlled during cutting or shaping process. In this study, therefore, multi-response optimization is carried out on AISI 1040 hardened mild steels when machined with ceramic cutting tools using response surface methodology under different cutting conditions. It can be noted that there are two responses. One is the surface roughness (SR) while the second is the material removal rate (MRR). The experimental results exhibits that all three factors reveal significant influence on generating metal cutting energy. Optimal levels are found out in A3, B3 and C3 level. Namely; cutting tests are carried out at 170 m/min cutting speed, 0.15 mm/rev. feed rate and 0.5 mm depth of cut conditions in terms of multi response performance index (MRPI). Analysis of variance and Pareto chart indicate that besides basic factors, A × C, A × B, B × C interactions have also an influence on MRPI (combination of MRR with SR). It is concluded that the correlation coefficient is found about 99.06%. Therefore, MRPI approach is capable of providing good modelling results for the combination of SR and MRR.

Cited by 2Open access

Solution of Correlated Multi-Response Optimization Problem

Saurav Datta, Goutam Nandi, Asish Bandyopadhyay et al. · IGI Global eBooks · 2012

This paper highlights an integrated approach to solve the correlated multi-response optimization problem through a case study in submerged arc welding (SAW). The proposed approach has been presented to overcome different limitations and drawbacks of existing optimization techniques available in literature. Traditional Taguchi optimization technique is based under the assumption that quality responses are independent to each other; however, this assumption may not always be valid. A common trend in the solution of a multi-objective optimization problem is to convert these multi-objectives into an equivalent single objective function. While deriving this equivalent objective function, different weightage are assigned to different responses according to their relative priority. In this regard, it seems that no specific guideline is available for assigning individual response weighs. To avoid this, Principal Component Analysis (PCA) has been adopted to eliminate correlation among individual desirability values and to calculate uncorrelated quality indices that have been aggregated to calculate overall grey relational grade. This study combines PCA, Desirability Function (DF) approach, and grey relation theory to the entropy measurement technique. Finally, the Taguchi method has been used to derive optimal process environment capable of producing desired weld quality related to bead geometry.

Cited by 0

Multi-Response Robust Optimization Using Desirability Function

Jing Wang, Zhen He, Jinho Oh et al. · 2008

Among developing industrialized countries, under the trend of industrial globalization, multinational enterprises give more attention to the quality issue of the products. Response optimization is the goal of much design of experiment which is one of the most important tools in modern quality engineering. Desirability function approach is the most popular method for the multi-response optimization problem. However, the consideration of robustness is usually ignored in this method. This paper presents a robust desirability function for the multi-response robust optimization. And its practical application is illustrated with the data set from an example from previously published articles.

Cited by 6

Multi-response optimization for a low-cost multi-dimpling process

Govind Waghmare, R. R. Arakerimath · International Journal of Quality & Reliability Management · 2023

Purpose This study aims to identify the significant factors of the multi-dimpling process, determine the most influential parameters of multi-dimpling to increase the dimple sheet strength and make a low-cost model of the multi-dimpling for sheet metal industries. To create an empirical expression linking process performance to different input factors, the percentage contribution of these elements is also calculated. Design/methodology/approach Taguchi grey relational analysis is used to apply a new effective strategy to experimental data in order to optimize the dimpling process parameters while taking into account several performance factors and low-cost model. In addition, a statistical method called ANOVA is used to ensure that the results are adequate. The optimal process parameters that generate improved mechanical properties are determined via grey relational analysis (GRA). Every level of the process variables, a response table and a grey relational grade (GRG) has been established. Findings The factors created for experiment number 2 with 0.5 mm as the sheet thickness, 2 mm dimple diameter, 0.5 mm dimple depth, 8 mm dimples spacing and the material of SS 304 were allotted rank one, which belonged to the optimal parameter values giving the greatest value of GRG. Practical implications The study demonstrates that the process parameters of any dimple sheet manufacturing industry can be optimized, and the effect of process parameters can be identified. Originality/value The proposed low-cost model is relatively economical and readily implementable to small- and large-scale industries using newly developed multi-dimpling multi-punch and die.

Cited by 1

A DOE Based Approach to Multi-Response Optimization

Xiaoping Liu, Siyuan Jiang · SAE technical papers on CD-ROM/SAE technical paper series · 2003

<div class="htmlview paragraph">This paper presents a DOE (Design of Experimentation) based approach to optimize systems with numerous responses (y's) and design variables (x's). The approach first conducts a large scale DOE to identify important variables or critical X's and their main effects on the system responses; and then develops “trend of effect” for each important variable. The trend of effects will be used to determine an optimal design, which improves those responses that do not meet design requirements and meanwhile maintains response performances that have already met requirements. A successful application on a vehicle road NVH CAE system will be presented.</div>

Cited by 11

Multi Response Optimization of Cutting Forces in End Milling Using Response

Surface Methodology · 2015

ABSTRACT End milling is the most important milling operation due to its capability of producing complex geometric shapes with reasonable accuracy and good surface finish. This paper focuses on multiple response optimiza-tion of cutting forces in end milling operation on AlSiC metal matrix composites to get minimum forces in tangential(Fx), radial(Fy) and axial(Fz) directions using response surface methodology. Cutting forces provide the basis for surface accuracy prediction and improvement, tool wear rate, the energy consumption within the machine tool, depending on power consumption and operating time. In this work, second-order quadratic models were developed for Fx, Fy and Fz considering the spindle speed, feed rate, depth of cut and immersion angle as the cutting parameters, using central composite rotatable design. The adequacies of the models were checked using ANOVA. The developed models were used for multiple-response optimization by desirability function approach in conjunction

Cited by 0

Particle Swarm Optimization for Multi-response Parameter Optimization Based on Desirability Functions

Pengfei Hu, Xiaosong Zhao · 2011

In order to solve the multi-response parameter optimization problem faced by manufacture industry, particle swarm optimization was employed to find the maxima of the desirability function. By designing the experiment of central composite design, analyzing and testing significance of the polynomial models, constructing desirability function and finding the best factor levels based on the fitness function, we may obtain the model that can solve the optimization problem rationally. This paper employs a numerical example for illustration, and achieved with objective value 0.4377 in iteration less than 10. Empirical results reveal that the PSO algorithm based on desirability functions has powerful global search ability and high convergence on solve the optimization problem.

Cited by 2