Optimization Of Multiple Characteristics Research Articles

Three-dimensional small glass hole evaluation using replica method and multiple characteristics optimization by stepwise procedure

Three-dimensional small glass hole evaluation using replica method and multiple characteristics optimization by stepwise procedure

Application of Particle Swarm Optimization for Optimizing the Process Parameters in Turning of PEEK CF30 Composites

This work deals with optimization of multiple characteristics in Computer Numerical Control (CNC) turning of reinforced Poly Ether Ether Ketone (PEEK CF30) with TiN coated tools under dry condition. The considered criteria included specific cutting pressure, machining force and cutting power. Three controllable factors of the turning process consisting of cutting speed, depth of cut and feed rate were incorporated. Taguchi design of experiments method was used to arrange the experimentation task. The developed response surface models were then employed with particle swarm optimization (PSO) to optimize the cutting conditions. PSO program gives the minimum values of the considered criteria and the corresponding optimal cutting conditions.

Multiple characteristics optimization in machining of GFRP composites using Grey relational analysis

Article history: Received January 6 2014 Received in Revised Format June 15 2014 Accepted July 25 2014 Available online August 1 2014 In the present work, a multi-response optimization method is used to optimize the machining parameters in turning of glass fiber reinforced polymer (GFRP) composites. Parameters like spindle speed (N), feed rate (f) and depth of cut (d) are taken to obtain the responses such as surface roughness (Ra) and material removal rate (MRR). Taguchi’s L9 orthogonal array has been used for machining the work-piece. Analysis of variance (ANOVA) has been carried out to check the significant process parameter in a single objective performance characteristic. The multiple performance characteristics have been analysed using Grey relational analysis and an appreciable result has been reported with this approach. © 2014 Growing Science Ltd. All rights reserved

Application of Desirability Function Based on Neural Network for Optimizing the Process Parameters in Turning of PEEK CF30 Composites

This work deals with optimization of multiple characteristics in CNC turning of reinforced Poly Ether Ether Ketone (PEEK CF30) with TiN coated tools. The considered criteria included surface roughness, machining force and cutting power. Three controllable factors of the turning process consisting of cutting speed, depth of cut and feed rate were incorporated. Taguchi design of experiments method was used to arrange the experimentation task. Artificial Neural Network approach was used for modeling the obtained responses. Then, desirability function approach was used to conduct single and multiple response optimizations.

Grey-based Taguchi method for optimisation of multiple characteristics in electrochemical deburring process

In the present study, a multi-objective optimisation technique, based on Taguchi method in combination with grey relational analysis, has been proposed and adopted in search of an optimal parametric combination during electrochemical deburring (ECD) of die steel. Deburring voltage, gap between tool and burr tip, electrolyte concentration and time are considered as input parameters when the performance characteristics include change in burr height and base material removal are taken into consideration. Taguchi’s L16 orthogonal array design is chosen for the experiments. An optimal parameter combination of ECD process is obtained by grey relational grade acquired from grey relational analysis (GRA). Optimal ECD parameters for multiple performance characteristics have been derived by the Taguchi method using grey relational grade as the quality index. Optimal setting has been validated using confirmatory test. Considering these ECD parameters, verification of the improvement in the quality characteristics was made with a confirmation test with respect to the chosen initial or reference parameter setting. The outcomes of these experiments indicate the application feasibility of the proposed optimisation technique for continuous improvement in electrochemical deburring process.

Otimização de múltiplos objetivos na soldagem de revestimento de chapas de aço carbono ABNT 1020 utilizando arame tubular inoxidável austenítico

A soldagem de revestimento de aços carbono com aços inoxidáveis tem obtido destaque no meio industrial por permitir que superfícies anti-corrosivas sejam obtidas a partir de materiais de baixo custo. No entanto, visando garantir a qualidade final dos revestimentos, é importante que o procedimento de soldagem seja bem ajustado, para que os cordões sejam depositados com a geometria desejada, com produtividade e sem defeitos. O objetivo deste trabalho é a otimização de múltiplas características do processo de revestimento de chapas de aço carbono ABNT 1020 utilizando arame tubular de aço inoxidável ABNT 316L. As características otimizadas incluem a largura, penetração, reforço e diluição, que representam a geometria do cordão. A produtividade foi maximizada através da taxa de deposição e do rendimento do processo. Como respostas de qualidade, considerou-se a formação de escória e o aspecto superficial. A estratégia de modelagem e otimização foi baseada em uma combinação da Metodologia de Superfície de Resposta, Método do Critério Global e Algoritmo Genético. Os resultados indicam que os modelos de superfície de resposta desenvolvidos para as características do processo apresentaram altos ajustes. O Método do Critério Global e o Algoritmo Genético foram aplicados com sucesso, o que permitiu a identificação do ponto de ótimo.

Modeling and Optimization of Multiple Characteristics in the AISI 52100 Hardened Steel Turning

This work aimed to develop a multiple response optimization procedure for the AISI 52100 hardened steel turning process. Optimizing this turning operation is important so that multiple quality characteristics are achieved simultaneously. The considered responses are: total cost, cutting time, total turning cycle time, tool life, material removal rate, and surface roughness. The adjusted process parameters were cutting speed, feed rate and depth of cut. A multi-objective optimization technique based on the Global Criterion Method and Genetic Algorithm were employed to identify the optimal settings for parameters with objective functions built through Response Surface Methodology. This two-fold approach lead up to optimized responses settled near the desired values were obtained with cutting speed = 214 m/min, feed rate = 0.088 mm/rev and depth of cut = 0.33 mm.

Optimisation of replicated multi-response, non-linear quality characteristics with non-normal distribution through artificial neural networks

Most related literature tackles quality characteristic(s) as single or simultaneous optimisation of multiple characteristics but through simple linear experimentation or mixture of linear orthogonal arrays. Due to their nature, characteristics demonstrate normal data distribution, analysed through traditional statistical methods or by computer aided solutions. This article tackles multiresponse quality characteristics through saturated non-linear OA experimentation that yields non-normal data distribution of a screening procedure in order to improve software design. Traditional analysis has limitations over such topics demanding a more sophisticated approach. Analysing such complex systems is achieved through artificial neural networks' function approximation ability which requires adequate data as training and testing exemplars that will produce a confident objective predicting system for the optimal control and level set. Experiment replication involves noise factors as being part of the screening process with a major role over the arrangements of the methodology proposed. E-mail spamming is used as a case study!