Showing papers on "Orthogonal array published in 2020"

Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

Abstract: Many engineering optimization problems are typically multi-objective in their natures and multidisciplinary with a large number of decision variables. Furthermore, Pareto dominance loses its effectiveness in such situations. Thus, developing a robust optimization algorithm undoubtedly becomes a true challenge. This paper proposes a multi-objective orthogonal opposition-based crow search algorithm (M2O-CSA) for solving large-scale multi-objective optimization problems (LSMOPs). In the M2O-CSA, a multi-orthogonal opposition strategy is employed to mitigate the conflicts among the convergence and distribution of solutions. First, two individuals are randomly chosen to undergo the crossover stage and then orthogonal array is presented to obtain nine individuals. Then individuals are used in the opposition stage to improve the diversity of solutions. The effectiveness of the proposed M2O-CSA is investigated by implementing it on different dimensions of multi-objective optimization problems (MOPs). The Pareto front solutions of these MOPs have various characteristics such as convex, non-convex and discrete. It is also applied to solve multi-objective design applications with distinctive features such as four bar truss (FBT) design, welded beam (WB) deign, disk brake (DB) design, and speed reduced (SR) design, where they involve different characteristics. In this context, a new decision making tool based on multi-objective optimization on the basis of ratio analysis (MOORA) technique is employed to help the designer for extracting the operating point as the best compromise or satisfactory solution to execute the candidate engineering design. Simulation results affirm that the proposed M2O-CSA works efficiently and effectively.

WEDM Parameter Optimization for Silicon@r-GO/Magneisum Composite Using Taguchi Based GRA Coupled PCA

Abstract: A combination of Taguchi methodology and Grey Relational Analysis (GRA) inturn coupled with principal component analysis (PCA) has been proposed through this paper. This methodology is adopted in order to evaluate and estimate the effect of machining parameters over the output responses of Wire Electrical-Discharge Machining (WEDM) performed on Magnesium based metal matrix composite. In this research, an optimal combination of process parameter was expected to be finalized so as to attain a state of maximum Material Removal Rate (MRR) that too with minimal surface roughness (Ra) value. WEDM of developed composite specimens were confirmed to be of L27 orthogonal array(OA) using Taguchi’s method, based mainly on control factors namely reinforcement weight percentage (wt.%), Doping (DP %), Pulse ON time (T-ON), Pulse OFF time (T-OFF) and wire feed rate (WF). ANOVA outcome reveals that wt.% and DP% are the most influencing parameter for MRR and Ra. Multiobjective responses were normalized using GRA; further PCA was applied to evaluate the weighting values corresponding to each performance. The optimial parameter was set and the final results obtained depending on the optimal combination was found to be with a maximum MRR of 14.9 mm3/min and a minimum Ra of 2.04 μm.

On the application of grey Taguchi method for benchmarking the dimensional accuracy of the PLA fused filament fabrication process

Abstract: The dimensional accuracy of a simple benchmark specimen fabricated with fused filament fabrication (FFF) route is discussed in the present study. FFF is a low-cost 3D-printing process that builds complicated parts by extruding molten plastic. Experimental method was designed according to Taguchi robust design based on an orthogonal array with nine experiments (L9 orthogonal array). The printing material was the polylactic acid (PLA). First, Grey–Taguchi method was used for the identification of the optimal printing parameter levels which result in the best dimensional accuracy for the PLA FFF parts. The printing parameters selected included number of shells, printing temperature, infill rate and printing pattern; they were selected in accordance with relevant studies already published. Then, in the second phase, nine specimens were fabricated using the same optimal printing parameter values determined in the first phase. The tolerance of these specimens was characterized according to international tolerance grades (IT grades). Data analysis showed that nozzle temperature is the dominant parameter. Additionally, the parts printed using the optimized process parameter levels possess good dimensional accuracy, which is compatible with the IT grades specification.

Optimization of powder metallurgy processing parameters of Al2O3/Cu composite through Taguchi method with Grey relational analysis

Abstract: This article investigates the effect of powder metallurgy processing parameters on multi-responses: mass density and hardness of Al2O3/Cu composite based on Taguchi method coupled with Grey relational analysis. Based on Taguchi’s L18 orthogonal array, the experimental trial runs were conducted with four controllable powder processing parameters: milling time, compaction pressure, sintering temperature and holding time. The Grey relational analysis (GRA) was performed to find the optimal powder processing condition with Grey relational grade (GRG) as a measure of performance index. Analysis of variance (ANOVA), 3D surface plots and contour plots were used to express the most influential parameter that affects mass density and hardness of Al2O3/Cu composite. Confirmation tests were conducted to make sure the validity of test results. The scanning electron microscope (SEM) images show the presence of Al2O3 particle in the composite. The Energy Dispersive X-Ray (EDX) spectrum indicates the presence of chemical elements: Cu, Al, O in the composite. The results show that the optimal powder metallurgy parameter combination is A2B3C3D1. The mean Grey relational coefficient (GRC) analysis shows that the mass density was more easily influenced by factors considered compare to hardness. The mean GRG result shows that the compaction pressure has the strongest correlation to responses. The result of 3D surface plots, contour plots and ANOVA indicate that the compaction pressure has most influential parameter that affect GRG. The % contribution of parameters shows that the % contribution of compaction pressure is significant (80.22%) compare to other main effects and interaction effects. The confirmation tests revealed that the performance characteristic of Al2O3/Cu composite was improved by GRA based Taguchi technique. The Monte Carlo simulated model summery show that based on Taguchi L18 Orthogonal Array design, one can easily find the reality of what will be happening in a process. Therefore, optimization of processes parameter could be simplified by using the Grey-based Taguchi method.

Application of hybrid nature-inspired algorithm: Single and bi-objective constrained optimization of magnetic abrasive finishing process parameters

Abstract: The manufacturing quality generated by the magnetic abrasive finishing (MAF) of brass depends of some critical process variables. Therefore, in this survey were investigated the optimization of this process taking into account the material removal rate and surface characteristics using a hybrid nature inspired algorithm (particle swarm optimization (PSO) coupled with firefly algorithm (FA)). In the initial step, the design matrix was generated using the Taguchi L16 orthogonal array, thereafter, obtaining the experimental protocol for developing the MAF process. The regression analysis was confronted with the analysis of variance (ANOVA) simulations to facilitate determination of the percentage contribution of each input parameters. The influence of individual and combined process parameters investigated via standard PSO, FA, and hybrid HPSO-FA were used for mono and bi-objective constrained optimization. This innovative way of processing the brass plate revealed that machining time was the most dominant parameter which contributed maximum towards variation in response variables determined.

Optimization of backpropagation neural network-based models in EDM process using particle swarm optimization and simulated annealing algorithms

Abstract: In the present study, artificial neural network (ANN) along with heuristic algorithms, namely particle swarm optimization (PSO) and simulated annealing (SA), has been employed to carry out the modeling and optimization procedure of electrical discharge machining (EDM) process on AISI2312 hot worked steel parts. Surface roughness (SR), tool wear rate (TWR) and material removal rate (MRR) are the process quality measures considered as process output characteristics. Determination of a process variables (pulse on and off time, current, voltage and duty factor) combination to minimize TWR and SR and maximize MRR independently (as single objective) and also simultaneously (as multi-criteria) optimization is the main objective of this study. The experimental data are gathered using Taguchi L36 orthogonal array based on design of experiments approach. Next, the output measures are used to develop the ANN model. Furthermore, the architecture of the ANN has been modified using PSO algorithm. At the last step, in order to determine the best set of process output variables values for a desired set of process quality measures, the developed ANN model is embedded into proposed heuristic algorithms (SA and PSO) with which their derived results have been compared. It is evident that the proposed optimization procedure is quite efficient in modeling (with less than 1% error) and optimization (less than 4 and 7 percent error for single- and multi-objective optimizations, respectively) of EDM process variables.

Modeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy

Abstract: Laser-based powder-bed fusion (L-PBF) is a widely used additive manufacturing technology that contains several variables (processing parameters), which makes it challenging to correlate them with the desired properties (responses) when optimizing the responses. In this study, the influence of the five most influential L-PBF processing parameters of Ti-6Al-4V alloy—laser power, scanning speed, hatch spacing, layer thickness, and stripe width—on the relative density, microhardness, and various line and surface roughness parameters for the top, upskin, and downskin surfaces are thoroughly investigated. Two design of experiment (DoE) methods, including Taguchi L25 orthogonal arrays and fractional factorial DoE for the response surface method (RSM), are employed to account for the five L-PBF processing parameters at five levels each. The significance and contribution of the individual processing parameters on each response are analyzed using the Taguchi method. Then, the simultaneous contribution of two processing parameters on various responses is presented using RSM quadratic modeling. A multi-objective RSM model is developed to optimize the L-PBF processing parameters considering all the responses with equal weights. Furthermore, an artificial neural network (ANN) model is designed and trained based on the samples used for the Taguchi method and validated based on the samples used for the RSM. The Taguchi, RSM, and ANN models are used to predict the responses of unseen data. The results show that with the same amount of available experimental data, the proposed ANN model can most accurately predict the response of various properties of L-PBF components.

Comparative study of metamodeling and sampling design for expensive and semi-expensive simulation models under uncertainty:

Abstract: In spite of the wide improvements in computer simulation packages, many complex simulation models, particularly under uncertainty, may be inefficient to run in terms of time, computation, and resou...

Multi-response Optimization of Ni55.8Ti Shape Memory Alloy Using Taguchi–Grey Relational Analysis Approach

Abstract: Wire electrical discharge machining process is largely applicable to any conductive material regardless of its hardness. The present study aims to investigate the influence of input process parameters such as pulse on time, pulse off time and current on output responses of MRR and SR for Ni55.8Ti shape memory alloy. Taguchi’s L9 orthogonal array design has been utilized to conduct the experiments. ANOVA is used to study significance and non-significance of the response variables. All the process parameters are found to be significant for both the output responses. Grey relational analysis have been used to obtain an optimal combination of WEDM parameters to maximize the cutting rate while minimizing surface roughness for shape memory alloys, which is the most preferred material for aerospace and biomedical application because of its high resistance to corrosion and a non-toxic nature. Predicted values obtained at an optimal condition using GRA have been validated by experimental trail and show a very close relation.

Shrinkage prediction of injection molded high density polyethylene parts with taguchi/artificial neural network hybrid experimental design

Abstract: Injection molding is classified as one of the economical manufacturing processes for high volume production of plastic parts. However, it is a complex process, as there are many factors that could lead to process variations and thus the quality issues of final products. One common quality issue is the presence of shrinkage and its associated warpage. Part shrinkage is largely affected by molding conditions, as well as mold design and material properties. The main objective of this paper is to predict the shrinkage of injection molded parts under different processing parameters. The second objective is to facilitate the setup of injection molding machine and reduce the need for trial and error. To meet these objectives, an artificial neural network (ANN) model was presented in this study, to predict the part shrinkage from the optimal molding parameters. Molding parameters studied include injection speed, holding time, and cooling time. A Taguchi-based experimental study was conducted, to identify the optimal molding condition which can lead to the minimum shrinkages in the length and width directions. A L27 (33) orthogonal array (OA) was applied in the Taguchi experimental design, with three controllable factors and one non-controllable noise factor. The feedforward neural network model, trained in back propagation, was validated by comparing the predicted shrinkage with the actual shrinkage obtained from Taguchi-based experimental results. It demonstrates that the ANN model has a high prediction accuracy, and can be used as a quality control tool for part shrinkage in injection molding.

Optimizing the machining variables in CNC turning of aluminum based hybrid metal matrix composites

Abstract: Aluminum based hybrid metal matrix composites (HMMC) are being employed nowadays in automobile, aeronautical, sports equipment etc. In this study, three different Al6063 based hybrid metal matrix composites (HMMC) samples having reinforcement with 3%, 6% and 9% by weight respectively are fabricated using stir casting method. Reinforcements used are waste products, namely, jute ash, groundnut shell and sugarcane. Surface roughness of these fabricated composite are is tested by varying machining parameters. The design of experiment is constructed by Taguchi method using three factors and two level, with L8 orthogonal array in which surface roughness is the output response parameter. These recorded results are analysed using analysis of variance and optimization of process parameters is done using response surface methodology and genetic algorithm. The Genetic algorithm optimization is achieved within 102 generations which is quite fast. On comparing it was found that results obtained from GA closely agreed with those obtained from the Response surface methodology.

Multi-objective Optimization of Woven Fabric Parameters Using Taguchi–Grey Relational Analysis

Abstract: Optimization technique is mainly used to find out the optimal value of control factors which yield the best response variables. In the case of more than one response variable, the optimization process using the Taguchi approach will give a different set of optimal level for each response variable. Consolidating Taguchi method with grey relation analysis will give optimal levels of control factors for all response variables. In the present study, multi-response optimization based on Taguchi-grey relational analysis was conducted to maximize tensile strength, breaking extension and air permeability of cotton woven fabrics. Cotton woven fabric parameters such as weft yarn count, weave structure, weft yarn density with three levels, and twist factor of the weft yarn with two levels were used as control factors. Using full factorial design, 81 experiments will be conducted. Whereas, using the Taguchi approach and L18 orthogonal array in particular, these experiments will be reduced to 16 experiments. U...

Automated multi-objective optimization for thin-walled plastic products using Taguchi, ANOVA, and hybrid ANN-MOGA

Abstract: In this paper, an automated two-staged multi-objective optimization tool for plastic injection molding (PIM) is designed, implemented, and applied to industrial product. In the first stage, Taguchi method is utilized for design of experiments (DOE). Process parameters include mold temperature, melt temperature, flow rate, cooling time, and four parameters related to variable pressure profile. Quality characteristics of plastic product containing warpage, weld line, and clamp force are obtained by simulation experiments. ANOVA and S/N ratio are utilized for data analysis to identify the importance of factors, provide an initial optimal combination of process parameters, and obtain quantitative factor’s contribution percentage. The number of process parameters is compressed from 8 to 5 considering factors’ contribution percentage for reduction in time and computation cost. In the second stage, hybrid artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) are utilized as further study to search for optimal solutions for synchronous reduction in three quality characteristics within compressed design space. Orthogonal array with Latin hypercube sampling (OA-LHS) is adopted for sampling with uniformity and stratification. ANN is utilized as a surrogate model to fit the surface between parameters and quality characteristics. MOGA is adopted to search the optimal solutions along the Pareto frontier. With the help of python, visual basic scripts, application programming interface (API) of Moldflow and DAKOTA, the two-stage optimization process is automated, which allows designers to study optimization for PIM, accurately and efficiently, without deep understanding of complicated optimization algorithms. The automated two-stage system is applied to an industrial plastic products and the result verifies that it is accurate and efficient in quality improvement.

Optimization of Abrasive Water Jet Machining for Green Composites Using Multi-variant Hybrid Techniques

Abstract: Traditional machining of polymer matrix composites (PMCs) possesses difficulties as they exhibit excellent specific strength and stiffness. Superior properties led PMCs parts were extensively used in structural, aviation, construction and automotive applications. The advanced machining process abrasive water jet machining (AWJM) has been explored to machine PMCs. The AWJM factors namely abrasive grain size, working pressure, standoff distance, nozzle speed, and abrasive mass flow rate affect the final outcome of surface quality (i.e. surface roughness, SR) and productivity (i.e. material removal rate ‘MRR’ and process time ‘PT’) are studied. Taguchi L27 orthogonal array of experimental design is employed for conducting practical experiments. Taguchi method limit to optimize multiple conflicting outputs (maximize: MRR, and minimize: PT and SR), simultaneously. In general, multiple outputs may have many solutions and are dependent on the tradeoff (relative importance or weights) assigned to each output. Traditional practices such as engineer judgement, expert suggestion and customer requirements may lead to local solutions (i.e. superior quality for one output, while compromising with the rest). Principal component analysis (PCA) method overcomes the said shortcomings of traditional practices and determines weight fractions for each output based on the experimental data. Multi-objective optimization on the basis of ratio analysis (MOORA), Grey relational analysis (GRA), Technique for order preference by similarity to ideal solution (TOPSIS) and Data Envelopment Analysis based Ranking (DEAR) are the four methods employed for the purpose of multi-objective optimization. MOORA, GRA and TOPSIS methodologies require assigning weight fractions for each output by the problem solver. Note that, solution accuracies vary with the weight fractions assigned to each output. The aggregate (composite values of all responses) values determined by PCA-MOORA, PCA-TOPSIS, PCA-GRA and DEAR method were used for determining optimal factor levels and their contributions. DEAR method determined optimal levels resulted in better machining quality characteristics.

Optimization of Congo red removal from aqueous solution using Taguchi experimental design

Abstract: Taguchi method was applied to optimize process parameters for the removal of Congo red (CR) dye from aqueous solutions by batch adsorption experiments. The L18 (35) orthogonal array was cho...

Design of T-shaped tube hydroforming using finite element and artificial neural network modeling

Abstract: Tube hydroforming (THF) is a frequently used manufacturing method in the industry, especially on automotive and aircraft industries. Compared with other manufacturing processes, THF provides parts with better quality and lower production costs. This paper proposes a design approach to estimate the T-shaped THF parameters, such as counter force, axial feed, and internal pressure, through finite element (FE) and artificial neural network (ANN) modeling. A numerical database is built through Taguchi’s L27 orthogonal array of experiments to train the ANN. The micromechanical damage model of Gurson-Tvergaard-Needleman is used with an elastoplastic approach to describe the material behavior. This study aims to find the combinations of THF parameters that maximize the bulge ratio and minimize the thinning ratio and wrinkling. The numerical results obtained by the FE model show good correlation with the results predicted by the ANN.

An Integrated Approach of GRA Coupled with Principal Component Analysis for Multi-Optimization of Shielded Metal Arc Welding (SMAW) Process.

Abstract: Welding distortion is a critical issue as it leads to severe deterioration of structural integrity of welded work piece and dimensional precision. This study aims at studying the effects of shielded metal arc welding (SMAW) parameters on the evolution of mechanical properties, including tensile strength, impact toughness, and hardness, along with angular distortion on a welded joint from SA 516 grade 70. Such parameters are analyzed and optimized by employing the Taguchi method and Grey relational analysis. SA 516 grade 70 is commercially used for fabrication of storage tanks, boilers and pressure vessels. SMAW is investigated with three levels of root gap, groove angle, electrode diameter, and pre-heat temperature, which were varied on a butt joint in flat (1 G) position to determine their effects on response variables at room temperature. Nine experiments were designed using a Taguchi L9 orthogonal array, welded according to American Society of Mechanical Engineers (ASME) section IX, and samples were prepared and tested as per ASTM A 370. The Taguchi method and Grey relational analysis were employed to observe the most significant parameters and optimal levels that synergically yield improved responses. Results are validated by conducting confirmatory experiments that show good agreement with optimum results.