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Article
Publication date: 24 November 2020

Sakthivel Murugan R. and Vinodh S.

This paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based…

Abstract

Purpose

This paper aims to optimize the process parameters of the fused deposition modelling (FDM) process using the Grey-based Taguchi method and the results to be verified based on a technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) calculation.

Design/methodology/approach

The optimization of process parameters is gaining a potential role to develop robust products. In this context, this paper presents the parametric optimization of the FDM process using Grey-based Taguchi, TOPSIS and AHP method. The effect of slice height (SH), part fill style (PFS) and build orientation (BO) are investigated with the response parameters machining time, surface roughness and hardness (HD). Multiple objective optimizations were performed with weights of w1 = 60%, w2 = 20% and w3 = 20%. The significance of the process parameters over response parameters is identified through analysis of variance (ANOVA). Comparisons are made in terms of rank order with respect to grey relation grade (GRG), relative closeness and AHP index values. Response table, percentage contributions of process parameters for both GRG and TOPSIS evaluation are done.

Findings

The optimum factor levels are identified using GRG via the Grey Taguchi method and TOPSIS via relative closeness values. The optimized factor levels are SH (0.013 in), PFS (solid) and BO (45°) using GRG and SH (0.013 in), PFS (sparse-low density) and BO (45°) using TOPSIS relative closeness value. SH has higher significance in both Grey relational analysis and TOPSIS which were analysed using ANOVA.

Research limitations/implications

In this research, the multiple objective optimizations were done on an automotive component using GRG, TOPSIS and AHP which showed a 27% similarity in their ranking order among the experiments. In the future, other advanced optimization techniques will be applied to further improve the similarity in ranking order.

Practical implications

The study presents the case of an automotive component, which illustrates practical relevance.

Originality/value

In several research studies, optimization was done on the standard test specimens but not on a real-time component. Here, the multiple objective optimizations were applied to a case automotive component using Grey-based Taguchi and verified with TOPSIS. Hence, an effort has been taken to find optimum process parameters on FDM, for achieving smooth, hardened automotive components with enhanced printing time. The component can be explored as a replacement for the existing product.

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Article
Publication date: 21 August 2018

Vesna Rubežić, Luka Lazović and Ana Jovanović

The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model.

Abstract

Purpose

The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model.

Design/methodology/approach

The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function.

Findings

The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops.

Originality/value

Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 37 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 5 March 2018

Dylan Agius, Mladenko Kajtaz, Kyriakos I. Kourousis, Chris Wallbrink and Weiping Hu

This study presents the improvements of the multicomponent Armstrong–Frederick model with multiplier (MAFM) performance through a numerical optimisation methodology…

Abstract

Purpose

This study presents the improvements of the multicomponent Armstrong–Frederick model with multiplier (MAFM) performance through a numerical optimisation methodology available in a commercial software. Moreover, this study explores the application of a multiobjective optimisation technique for the determination of the parameters of the constitutive models using uniaxial experimental data gathered from aluminium alloy 7075-T6 specimens. This approach aims to improve the overall accuracy of stress–strain response, for not only symmetric strain-controlled loading but also asymmetrically strain- and stress-controlled loading.

Design/methodology/approach

Experimental data from stress- and strain-controlled symmetric and asymmetric cyclic loadings have been used for this purpose. The analysis of the influence of the parameters on simulation accuracy has led to an adjustment scheme that can be used for focused optimisation of the MAFM model performance. The method was successfully used to provide a better understanding of the influence of each model parameter on the overall simulation accuracy.

Findings

The optimisation identified an important issue associated with competing ratcheting and mean stress relaxation objectives, highlighting the issues with arriving at a parameter set that can simulate ratcheting and mean stress relaxation for load cases not reaching at complete relaxation.

Practical implications

The study uses a strain-life fatigue application to demonstrate the importance of incorporating a technique such as the presented multiobjective optimisation method to arrive at robust parameters capable of accurately simulating a variety of transient cyclic phenomena.

Originality/value

The proposed methodology improves the accuracy of cyclic plasticity phenomena and strain-life fatigue simulations for engineering applications. This study is considered a valuable contribution for the engineering community, as it can act as starting point for further exploration of the benefits that can be obtained through material parameter optimisation methodologies for models of the MAFM class.

Details

Aircraft Engineering and Aerospace Technology, vol. 90 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

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Article
Publication date: 6 June 2016

Roozbeh Hesamamiri and Atieh Bourouni

Customer support has always been considered a competitive advantage in many industries. In recent years, firms have begun to provide customers with a high-quality service…

Abstract

Purpose

Customer support has always been considered a competitive advantage in many industries. In recent years, firms have begun to provide customers with a high-quality service experience, in order to attract more customers and achieve higher customer satisfaction. Although customer service and satisfaction have been discussed by other researchers, to the knowledge, there has been no dynamic and intelligent way to model and optimize customer support systems for product and service providers. The purpose of this paper is to develop a modeling method for customer support optimization.

Design/methodology/approach

In this study, a system dynamics (SD) model has been formulated to investigate the dynamic characteristics of customer support in an IT service provider. The proposed simulation model considers the dynamic, non-linear, and asymmetric interactions among its components, and allows study of the behavior of the customer support system under controlled conditions. Furthermore, a particle swarm optimization method was developed to investigate the proper combination of parameters and strategy development of the support center.

Findings

This paper proposes a novel modeling, simulation, and optimization approach for complex customer support systems of information and communications technology (ICT) service providers. This method helps managers improve their customer support systems. Moreover, the simulation results of the case study show that ICT service providers can gain benefit by managing their customer service dynamically over time using the proposed artificial intelligent multi-parameter modeling and optimization method.

Research limitations/implications

The proposed holistic modeling approach and multi-parameter optimization method will greatly help managers and researchers understand the factors influencing customer support. Moreover, it facilitates the process of making new improvement strategies based on provided insights.

Originality/value

The paper shows how SD simulation and multi-parameter optimization can provide insights into the field of customer support. However, the existing literature lacks a holistic view of these kinds of simulation systems, as well as a multi-parameter optimization method for SD methodology.

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Article
Publication date: 16 January 2017

Heping Chen, Jing Xu, Biao Zhang and Thomas Fuhlbrigge

High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time…

Abstract

Purpose

High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process.

Design/methodology/approach

In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed.

Findings

Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes.

Research limitations/implications

The proposed method is practical without any limitation.

Practical implications

The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production.

Originality/value

This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.

Details

Industrial Robot: An International Journal, vol. 44 no. 1
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 24 August 2020

Brijesh Upadhaya, Paavo Rasilo, Lauri Perkkiö, Paul Handgruber, Anouar Belahcen and Antero Arkkio

Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be…

Abstract

Purpose

Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be remedied by including a proper physical constraint in the parameter-fitting optimization algorithm. This paper aims to implement the constraint in the meta-heuristic simulated annealing (SA) optimization and Nelder–Mead simplex (NMS) algorithms to find JA model parameters that yield a physical hysteresis loop. The quasi-static B(H)-characteristics of a non-oriented (NO) silicon steel sheet are simulated, using existing measurements from a single sheet tester. Hysteresis loops received from the JA model under modified logistic function and piecewise cubic spline fitted to the average M(H) curve are compared against the measured minor and major hysteresis loops.

Design/methodology/approach

A physical constraint takes into account the anhysteretic susceptibility at the origin. This helps in the optimization decision-making, whether to accept or reject randomly generated parameters at a given iteration step. A combination of global and local heuristic optimization methods is used to determine the parameters of the JA hysteresis model. First, the SA method is applied and after that the NMS method is used in the process.

Findings

The implementation of a physical constraint improves the robustness of the parameter fitting and leads to more physical hysteresis loops. Modeling the anhysteretic magnetization by a spline fitted to the average of a measured major hysteresis loop provides a significantly better fit with the data than using analytical functions for the purpose. The results show that a modified logistic function can be considered a suitable anhysteretic (analytical) function for the NO silicon steel used in this paper. At high magnitude excitations, the average M(H) curve yields the proper fitting with the measured hysteresis loop. However, the parameters valid for the major hysteresis loop do not produce proper fitting for minor hysteresis loops.

Originality/value

The physical constraint is added in the SA and NMS optimization algorithms. The optimization algorithms are taken from the GNU Scientific Library, which is available from the GNU project. The methods described in this paper can be applied to estimate the physical parameters of the JA hysteresis model, particularly for the unidirectional alternating B(H) characteristics of NO silicon steel.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 39 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 25 January 2013

Jianghong Yu, Daping Wang and Chengwu Hu

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

Abstract

Purpose

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

Design/methodology/approach

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Findings

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

Practical implications

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Originality/value

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

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Article
Publication date: 14 June 2019

Xianwei Liu, Huacong Li, Xinxing Shi and Jiangfeng Fu

The purpose of this paper is to improve the hydraulic efficiency without changing the overall dimension. The blade profile optimization design of the aero-centrifugal pump…

Abstract

Purpose

The purpose of this paper is to improve the hydraulic efficiency without changing the overall dimension. The blade profile optimization design of the aero-centrifugal pump based on the biharmonic equation surrogate model has been studied.

Design/methodology/approach

First of all, Bezier curves and linear function are used to control the annular angle distribution and the stacking angle of blade profile under the MATLAB platform. Grid independence analysis has been studied to find the finest mesh scheme. After the precision comparison of test data and computation fluid dynamics 15 sets of design parameters are carried out as the boundary condition of the biharmonic equation. The efficiency surrogate model of the biharmonic equation is constructed via iteratively solving of a discrete difference equation. The other two surrogate models of response surface model (RSM) and radial basis function neural network surrogate model (RBFNNSM) are compared with the biharmonic equation surrogate model by the standard of modified complex correlation coefficient R2 and root mean square deviation (RSME). Finally, the artificial fish swarm algorithm has been used to find the global optimal design parameters with the objective function of highest efficiency.

Findings

The results show that the design parameters code conversion method can reduce the number of optimization parameters from five to three, makes the design space become a cube, and compared with RSM and RBFNNSM, the biharmonic equation surrogate model has higher precision with R2 is 0.8958, RSME is 0.1382. The final optimum result of AFSA is at the point of [1 −1 −1]. The internal flow field analysis shows that after optimization the outlet relative velocity becomes more uniform and the wake effect has been significantly decreased. The hydraulic efficiency of the optimized pump is about 59.45 per cent increasing 5.4 per cent compared with a prototype pump.

Originality/value

This study developed a new method to optimize the design parameters of aero-centrifugal pump impeller based on biharmonic equation surrogate model, which had a good agreement with experimental values within just 15 sets of the original design. The optimization results shows that the method can improve the hydraulic efficiency significantly.

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Article
Publication date: 12 May 2020

Hanmant Virbhadra Shete and Madhav S. Sohani

This paper aims to examine an investigation of high-pressure coolant (HPC) drilling process with regard to experimental models of output parameters, effect of input…

Abstract

Purpose

This paper aims to examine an investigation of high-pressure coolant (HPC) drilling process with regard to experimental models of output parameters, effect of input parameters on output parameters and simultaneous optimization of the output parameters.

Design/methodology/approach

Experimental plan was designed using response surface method and experiments were conducted on HPC drilling set up. Measurements for output parameters were carried out and mathematical models were obtained. Multi response optimization using a composite desirability function approach was used to obtain optimum values of input parameters for simultaneous optimization of output parameters.

Findings

Optimal value of input parameters for optimization of HPC drilling process were obtained as; coolant pressure: 21 bar, spindle speed: 3,970 rpm, feed rate: 0.084 mm/rev and peck depth: 5.50 mm. The composite desirability obtained is 0.9412, which indicates that the performance of HPC drilling process was significantly optimized. Developed mathematical models of the output parameters accurately represent the entire design space under investigation.

Originality/value

This is the first study that involves variation of higher coolant pressure and investigation of HPC drilling process using response surface methodology and multi response optimization technique with desirability function.

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Article
Publication date: 12 August 2020

Ngoc Le Chau, Ngoc Thoai Tran and Thanh-Phong Dao

Compliant mechanism has been receiving a great interest in precision engineering. However, analytical methods involving their behavior analysis is still a challenge…

Abstract

Purpose

Compliant mechanism has been receiving a great interest in precision engineering. However, analytical methods involving their behavior analysis is still a challenge because there are unclear kinematic behaviors. Especially, design optimization for compliant mechanisms becomes an important task when the problem is more and more complex. Therefore, the purpose of this study is to design a new hybrid computational method. The hybridized method is an integration of statistics, numerical method, computational intelligence and optimization.

Design/methodology/approach

A tensural bistable compliant mechanism is used to clarify the efficiency of the developed method. A pseudo model of the mechanism is designed and simulations are planned to retrieve the data sets. Main contributions of design variables are analyzed by analysis of variance to initialize several new populations. Next, objective functions are transformed into the desirability, which are inputs of the fuzzy inference system (FIS). The FIS modeling is aimed to initialize a single-combined objective function (SCOF). Subsequently, adaptive neuro-fuzzy inference system is developed to modeling a relation of the main geometrical parameters and the SCOF. Finally, the SCOF is maximized by lightning attachment procedure optimization algorithm to yield a global optimality.

Findings

The results prove that the present method is better than a combination of fuzzy logic and Taguchi. The present method is also superior to other algorithms by conducting non-parameter tests. The proposed computational method is a usefully systematic method that can be applied to compliant mechanisms with complex structures and multiple-constrained optimization problems.

Originality/value

The novelty of this work is to make a new approach by combining statistical techniques, numerical method, computational intelligence and metaheuristic algorithm. The feasibility of the method is capable of solving a multi-objective optimization problem for compliant mechanisms with nonlinear complexity.

Details

Engineering Computations, vol. 38 no. 4
Type: Research Article
ISSN: 0264-4401

Keywords

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