Optimization Of Quality Characteristics Research Articles

Application of integrated CRITIC and GRA-based Taguchi method for multiple quality characteristics optimization in laser-welded blanks

There is a need to study the multi-objective optimization technique as the appropriate method to enhance welding qualities under optimal process conditions. Therefore, this study investigated the application of the integrated Criteria Importance Through Intercriteria Correlation (CRITIC), which is an objective weighting technique, and the Grey Relational Analysis (GRA)-based Taguchi method to solve multiple criteria optimization problem in the Nd:YAG laser welding process. The Taguchi-based L36 orthogonal array table was employed in this study to optimize six process parameters, including the beam diameter, laser power, flow rate, welding speed, laser offset, and pulse shape, with the aim to simultaneously achieve maximum weld strength and minimum weld width. The base metal JIS G3141 SPCC steel with 0.5 and 1.0 mm thicknesses was used in the present experiment. Following the welding process and optimization, the weld strength was measured using a Cometech QC-506M1 universal testing machine, while the weld width was determined under a Nikon SMZ25 stereomicroscope. Based on the results, the weight fractions of the weld strength and weld width from the applied CRITIC method were equal to 0.4157 and 0.5843, respectively. Meanwhile, the GRA revealed that the process parameters recorded an optimal setting for beam diameter of 0.8 mm, flow rate of 8 L/min, laser power of 0.6 kW, welding speed of 2.5 mm/s, laser offset of 0.2 mm, and pulse shape I. Furthermore, the weld strength and the weld width were enhanced from 236 to 328 MPa and from 1.13 to 1.04 mm, respectively. Additionally, the Analysis of Variance (ANOVA) indicated that the laser power and welding speed were the most influential parameters on the welding qualities. Most importantly, the findings of the confirmation experiment showed that the proposed approach was able to effectively identify the optimal laser welding parameters, which ultimately improved the multiple quality characteristics.

Multi response optimization of quality characteristics in Offset printing industry using Taguchi approach and Grey relational analysis

Multi response optimization of quality characteristics in Offset printing industry using Taguchi approach and Grey relational analysis

Experimental investigation and optimization of quality characteristics during friction stir welding of Al- and Zn-based magnesium alloy using artificial intelligence

Friction stir welding is successfully used to weld different wrought magnesium alloys. This work investigated the mechanical and microstructural behavior of the friction stir-welded AZ31B magnesium alloy. The experiments were conducted as per experimental runs designed by response surface methodology. An artificial neural network model was developed to produce a relationship between process variables (tool rotational speed, welding speed, and tool shoulder diameter) and characteristics of the friction stir-welded joints (tensile strength, percentage elongation, impact strength, microhardness, and grain size). The acceptable range of statistical parameters validated the adequacy of the model. The multi-objective optimization technique, genetic algorithm was used to obtain a set of Pareto optimal solutions. The best-compromised optimum solution for maximum tensile strength (164.2 MPa), percentage elongation (8%) impact strength (3.5 J), microhardness (85 Hv), and minimum grain size (13.1 μm) was validated by confirmation test with <3 percent absolute error percentage. The fractographical analysis has been performed and dimples and torn edges observed in fracture zones.

Quality characteristics of semi-dried restructured jerky: combined effects of duck skin gelatin and carrageenan.

The present study investigated the effects of duck skin gelatin and carrageenan on the quality of semi-dried restructured jerky. Restructured jerky was prepared as follows: G0 (control, without duck skin gelatin and carrageenan), G0C (0.3% carrageenan), G0.5 (0.5% duck skin gelatin), G0.5C (0.5% duck skin gelatin and 0.3% carrageenan), G1 (1.0% duck skin gelatin), and G1C (1.0% duck skin gelatin and 0.3% carrageenan). The moisture content was the highest for the semi-dried restructured jerky from G0.5C and G1C groups, which showed the lowest for shear force value (p < 0.05). The processing yield of semi-dried restructured jerky with carrageenan was higher compared to that of the control group (p < 0.05). The rehydration capacities of G0.5, G0.5C, and G1C groups were significantly higher than the rehydration capacity of the control group (p < 0.05). Water activity, lightness, yellowness, flavor score, texture score, and overall acceptability were the highest (p < 0.05) for the semi-dried restructured jerky from the G1C group. No significant (p > 0.05) difference was observed in appearance score among restructured jerky prepared from duck skin gelatin and carrageenan. Thus, the addition of 1.0% duck skin gelatin and 0.3% carrageenan to semi-dried restructured jerky formulations results in the optimization of quality characteristics.

Multi-Objective Optimization of Pulsed Nd: YAG Laser Cutting Process Using Entropy-Based ANN-PSO Model

The paper investigated the efficacy of entropy-based ANN-PSO model combining Artificial Neural Networks (ANN) and Particle Swarm Optimization (PSO) for estimation and optimization of quality characteristics associated with pulsed Nd:YAG laser cutting of aluminium alloy. In the ANN-PSO model, ANN trained using backpropagation with the Bayesian regularization algorithm is employed for estimation and computation of objective function value during optimization with PSO. The entropy method is used to compute the real weight of different output quality characteristics during formulation of the combined objective function. An experiment has been conducted based on full factorial experimental design, where cutting speed, pulse energy, and pulse width are considered as controllable input parameters while kerf width, kerf deviation, surface roughness, and material removal rate are measured as output parameters. Further, the experimental dataset has been used in the ANN-PSO model for prediction and optimization. The prediction accuracy of the ANN module is indicated by a low mean absolute error of 1.74%. Experimental validation of optimized output also results in less than 2% error only. ANOVA study suggests cutting speed as the most influencing factor.

On the multi-parametric optimization of quality characteristics of the hybrid Al-6061 composites fabricated through powder metallurgy

Aluminium matrix composites (AMCs) have gained enormous attention of almost all types of manufacturing sectors owing to their excellent strength to weight ratio, ease of processing, good thermal conductivity, corrosion resistance, and desirable mechanical properties. Amongst the various processing routes available for AMCs, mechanical alloying assisted powder metallurgy (MA2PM) is particularly efficient in achieving the required physical, mechanical, and metallurgical characteristics. Therefore, in this present work, an effort has been made to develop the hybrid Al-6061 composites via MA2PM route under the influence of controllable and significant process variables. A specified proportion of graphite, silicon carbide (SiC), magnesium (Mg), zinc (Zn), and fly ash were selected and the process parameters (such as compaction pressure, sintering temperature, and sintering time) were varied as per Taguchi L9 orthogonal array. It has been found that the hardness and wear responses of the developed samples were significantly influenced by the processing parameters at 95% confidence level. Overall, the study conducted indicated that for hardness and wear rate only the compaction pressure found as significant parameter with 89.40 and 72.52% contribution.

Hybrid neural network–particle swarm optimization algorithm and neural network–genetic algorithm for the optimization of quality characteristics during CO2 laser cutting of aluminium alloy

Aerospace and automobile industries employ aluminium alloys due to its lightweight and excellent resistance to corrosion. As aluminium is highly reflective and highly thermally conductive, it is difficult-to-cut material by laser processing. The quality characteristics of the cut predominantly depend upon the combination of laser processing parameters. The main quality indices for evaluating CO2 laser cutting were surface roughness, kerf width and kerf taper; and the machining parameters considered were laser power, speed and gas pressure. This work suggests hybrid artificial neural network (ANN)–particle swarm optimization (PSO) algorithm and artificial neural network (ANN)–genetic algorithm (GA) to optimize the associated multi-response characteristics during CO2 laser cutting of aluminium 6061 alloys. The results illustrate that the hybrid ANN–GA and ANN–PSO model is an efficient tool for the optimization of process parameters in CO2 laser cutting of difficult-to-cut material—aluminium. From the optimization results, it can be concluded that the proposed ANN–GA approach can be efficiently utilized to optimize the parameters for obtaining minimum roughness, kerf width and kerf taper.

Quality characteristics optimization in CNC end milling of A36 K02600 using Taguchi’s approach coupled with artificial neural network and genetic algorithm

This study investigates surface roughness and energy consumption in a CNC end milling of plain low carbon steel (mild steel) A36 K02600 using a carbide end mill cutter. Taguchi’s L9 orthogonal array was adopted for designing the experimental runs considering several process parameters viz. cutting velocity, Feed rate, spindle speed and cutting depth in order to study their influence on the quality characteristics. Optimal control parameter combinations for minimizing surface roughness and energy consumption were evaluated from signal to noise ratio. Analysis of variance revealed the contribution of control factors on the quality characteristics. Numerical predictive models using linear regression and artificial neural network were developed to envisage the responses accurately. Multi-objective Genetic Algorithm optimization was exploited in order to obtain a specific set of control parameter which would optimize both the responses simultaneously. This study concludes that spindle speed (68.24% contribution) and feed rate (92% contribution) are the most responsible variables for surface quality and energy consumption respectively. The outcome of artificial neural network model and genetic algorithm confirm that both quality characteristics can be optimized simultaneously and Taguchi’s robust design approach is a successful tactic for optimizing machining parameters to achieve desired surface quality at low energy consumption. Improvement in surface quality and reduction in energy consumption were found to be 27.79% and 30% respectively. Low carbon steel is extensively accepted by the industries for its wide variety of which make this study physically viable.

A Solution Framework for Response Surface-Based Multiple Quality Characteristics Optimization

A common problem generally encountered during manufacturing process improvement involves simultaneous optimization of multiple ‘quality characteristics’ or so-called ‘responses’ and determining the best process operating conditions. Such a problem is also referred to as ‘multiple response optimization (MRO) problem’. The presence of interaction between the responses calls for trade-off solution. The term ‘trade-off’ is an explicit compromised solution considering the bias and variability of the responses around the specified targets. The global exact solution in such types of nonlinear optimization problems is usually unknown, and various trade-off solution approaches (based on process response surface (RS) models or without using process RS models) had been proposed by researchers over the years. Considering the prevalent and preferred solution approaches, the scope of this paper is limited to RS-based solution approaches and similar closely related solution framework for MRO problems. This paper contributes by providing a detailed step-by-step RS-based MRO solution framework. The applicability and steps of the solution framework are also illustrated using a real life in-house pin-on-disc design of experiment study. A critical review on solution approaches with details on inherent characteristic features, assumptions, limitations, application potential in manufacturing and selection norms (indicative of the application potential) of suggested techniques/methods to be adopted for implementation of framework is also provided. To instigate research in this field, scopes for future work are also highlighted at the end.

Modeling and optimization for fly ash reinforced bronze-based composite materials using multi objective Taguchi technique and regression analysis

PurposeThis paper aims to investigate the effect of fly ash reinforcement ratio (Rr) and sintering temperature (T) on the transverse rupture strength (TRS), hardness and density of fly ash reinforced bronze-based composite materials by using multi-objective Taguchi technique, analysis of variance (ANOVA) and regression analysis.Design/methodology/approachThe bronze-based composite materials containing 5, 10 and 15 Wt.% fly ashes were prepared by using spark plasma sintering carried out under a pressure of 35 MPa, at 750, 800 and 850 °C for 3 min. Sintering temperature and fly ash reinforcement ratio were considered as input parameters; the TRS, hardness and density were considered as output parameters. Experiments were designed according to Taguchi L9 orthogonal array. Multi signal-to-noise ratio (MSNR) was computed to define the optimal process parameters. ANOVA was conducted to detect the importance of the input parameters for the process performance. Moreover, the linear model was developed for predicting the performance parameters by using regression analysis.FindingsFly ash can be a good alternative as reinforcement to reduce the cost for composite materials. Optimal process parameters had obtained 850°C sintering temperature and 5 per cent reinforcement ratio by using multi-objective Taguchi technique. The per cent contributions of the control factors on the performance parameters had obtained sintering temperature (95.78 per cent) and fly ash reinforcement ratio (3.00 per cent) with ANOVA. The obtained results indicate that the sintering temperature was found to be the dominant factor among controllable factors. However, the reinforcement ratio showed an insignificant effect.Originality/valueIt has been indicated that multi-objective Taguchi technique and regression analysis are effective and powerful tools in modeling and simultaneous optimization of quality characteristics for composite materials.

Multi-response optimization of T300/epoxy prepreg tape-wound cylinder by grey relational analysis coupled with the response surface method

This study investigates the multi-objective optimization of quality characteristics for a T300/epoxy prepreg tape-wound cylinder. The method integrates the Taguchi method, grey relational analysis (GRA) and response surface methodology, and is adopted to improve tensile strength and reduce residual stress. In the winding process, the main process parameters involving winding tension, pressure, temperature and speed are selected to evaluate the parametric influences on tensile strength and residual stress. Experiments are conducted using the Box–Behnken design. Based on principal component analysis, the grey relational grades are properly established to convert multi-responses into an individual objective problem. Then the response surface method is used to build a second-order model of grey relational grade and predict the optimum parameters. The predictive accuracy of the developed model is proved by two test experiments with a low prediction error of less than 7%. The following process parameters, namely winding tension 124.29 N, pressure 2000 N, temperature 40 °C and speed 10.65 rpm, have the highest grey relational grade and give better quality characteristics in terms of tensile strength and residual stress. The confirmation experiment shows that better results are obtained with GRA improved by the proposed method than with ordinary GRA. The proposed method is proved to be feasible and can be applied to optimize the multi-objective problem in the filament winding process.

Modeling and Multiresponse Optimization of Quality Characteristics for the Micro-EDM Drilling Process

Micro parts of various kinds are now in demand for fabricating high-tech products such as micro-electronic parts and MEMS applications. In order to supply these micro parts, the development of micromachining technology becomes increasingly important. Among the various stages of micromachining, the fabrication of holes and pins of precise nature are essential for many miniature components. Micro-hole drilling, is used in various precision industries, such as the production of automotive fuel injection nozzles, watch and camera parts to generate deeper holes with better straightness, better roundness and smoother surfaces. Electrical discharge machining (EDM) is a non-contact machining process for electrically conductive materials which can produce high aspect ratio holes, particularly useful in the tool making industry. The objective of the study on micro-EDM drilling process parameters is to analyse the effect of drilling parameters such as the feed rate (A), capacitance (B) and voltage (V) on machining stainless steel shim using a tungsten electrode of 300 µm diameter. The performance measures are surface roughness (Ra), metal removal rate and tool wear ratio. Response surface methodology has been used and experiments have been conducted using central composite design. Statistical models of the output responses have been developed using three level full factorial design of experiment. The developed models have been used for multi response optimization by desirability function approach to determine the optimum machining parameters. These optimal machining parameters are then validated experimentally, and it is observed that the response values are very close to the predicted values with less error.

Multiple Quality Characteristics Optimization of Ball Grid Array Wire Bonding Process

This paper presents an integrated approach combining principal component analysis (PCA) and Taguchi methods to develop a ball grid array (BGA), gold (Au) wire bonding process with multiple quality characteristics optimization. Eight main process factors of BGA wire bonding technology are selected as the control factors for parameter design. They are the factor A (seating ultrasonic generator (USG)), factor B (TIP height), factor C (C/V), factor D (USG current), factor E (USG bond time), factor F (bond force), factor G (FS threshold), and factor H (FAB size). The quality characteristics of the process in the study, including the wire pull strength, the ball shear strength, the ball thickness difference, the ball size difference, and the percentage of the Au–Al intermetallic compound (IMC) are measured. The optimal process parameters that meet the requirements for multiple quality characteristics are A1B3C1D3E3F1G1H2. They are then used to be tested for verification. Experimental results confirm that the optimal process design indeed enhances the quality characteristics investigated. The analysis of variance (ANOVA) results also show that the most important control factors affecting the quality characteristics are factor B (TIP height), factor C (C/V), and factor G (FS threshold), which accounts for 72.34% of total process variance. Thus, they must be strictly monitored during processing.

Multi-objective optimisation of pulsed Nd:YAG laser cutting process using integrated ANN–NSGAII model

The paper presents an integrated model of artificial neural networks (ANNs) and non-dominated sorting genetic algorithm (NSGAII) for prediction and optimization of quality characteristics during pulsed Nd:YAG laser cutting of aluminium alloy. A full factorial experiment has been conducted where cutting speed, pulse energy and pulse width are considered as controllable input parameters with surface roughness and material removal rate as output to generate the dataset for the model. In ANN–NSGAII model, back propagation ANN trained with Bayesian regularization algorithm is used for prediction and computation of fitness value during NSGAII optimization. NSGAII generates complete set of optimal solution with pareto-optimal front for outputs. Prediction accuracy of ANN module is indicated by around 1.5 % low mean absolute % error. Experimental validation of optimized output results less than 1 % error only. Characterization of the process parameters in pareto-optimal region has been explained in detail. Significance of controllable parameters of laser on outputs is also discussed.

Multi-Objective Optimization of Quality in Laser Cutting Based on Response Surface Model

Prediction and optimization of quality characteristics is an important means to improve the quality of laser cutting. Kerf width (KW) and material removal rate (MRR) are selected as the quality characteristics in this paper. The fitting response surface models (RSM) of KW and MRR are considered as the optimization objective function in pulsed Nd: YAG laser cutting of alloy steel for multi-objective optimization. An improved Pareto genetic algorithm is used in the optimization, and the significant factors have been found. The predicted results are basically consistent with the experimental. Therefore, the method used in this paper can be used for optimization of KW and MRR in pulse Nd: YAG laser cutting. The study can provide theoretical basis for the prediction and optimization of quality in laser cutting.

A Study of Hybrid Optimization of Injection Moulding Process Parameters for Plastic Gear

This paper presents a hybrid optimization method for optimizing the injection moulding process parameters for plastic gear. This proposed method combines the Taguchi robust design, grey relational analysis (GRA) and principal component analysis (PCA) in designing the experiments and analyzing the behaviour of shrinkage in addendum circles (AC), dedendum circles (DC) and tooth thickness (TT) of the moulded gear. The proposed hybrid optimization method appears to be a necessity to overcome the shortcomings in multiple quality characteristics optimization as well as provides a statistical solution in defining a weight for each quality characteristic.

Parameter optimization of etching process for a LGP stamper

This study proposes a two-stage system to optimize the etching process parameter for making a light guide plate (LGP) stamper. The multi-quality characteristics of the parameter settings include depth and uniformity of the microstructures formed in the LGP stamper. The control factors to conduct the process are etching temperature, specific gravity, spray pressure, transfer speed, and oscillating rate. The first stage is to conduct signal-to-noise (S/N) ratio optimization using Taguchi orthogonal array experiments. After conducting the etching process in microstructure, the experimental data can be translated and tested by back-propagation neural networks in order to create S/N ratio and the other quality characteristics predictors. In addition, the S/N ratio predictor and genetic algorithms are used together to obtain combinations of settings and to find the maximized process parameters on S/N ratios. As a result, the quality variance could be minimized. The second stage demonstrates quality characteristics optimization by pushing the process qualities to the targeted specifications. The analysis of variance (ANOVA) is employed to determine the significant control factors. Then, a statistical analysis using the aforementioned quality predictor, S/N ratios predictor, and particle swarm optimization is implemented to simulate the targeted specifications and then find a suitable specifications combination and the most stable and qualified process.

Modelization and Optimization of Quality Characteristics of Pork Treated Various Hydrostatic Pressure Conditions

In this study, the effects of physical parameters (30-270MPa of pressure, 3-57 min of time, and 1-49oC of temperature) on pork quality were investigated. Response surface methodology was used in order to monitor and model the changes in pork quality under varied pressure conditions. As quality characteristics, shear force, water holding capacity (WHC) and the CIE color of pork were measured, and optimum pressure conditions were evaluated by statistical modeling. Pressure improved the WHC of pork at relatively low temperature (<25oC); however, the opposite occurred with increasing temperature. Although pressure and temperature affected the tenderness of the meat, interaction effects among variations were not observed. At pressure levels higher than 200MPa, the color of pork differed markedly from that of the untreated controls. In particular, differential scanning calorimetry (DSC) revealed marked evidence of myosin denaturation. The present study demonstrates that pork quality varies depending on pressure conditions.

Multi-Objective Process Optimization of Pulsed Plasma Arc Welding SS400 Steel Pipe with Foamed Aluminum Liner

Principal component analysis (PCA) coupled with Taguchi methods are employed in the study for developing multiple quality characteristics optimization of pulsed plasma arc welding SS400 steel pipe with foamed aluminum liner (SPFAL). The quality characteristics investigated are the micro-hardness, the compression strength, and the bending strength of the weldments. Eight control factors selected are the tip aperture (Factor A), plasma base current (Factor B), plasma pulse current (Factor C), duty cycle (Factor D), pulse frequency (Factor E), shielding gas (Factor F), plasma gas (Factor G), and welding velocity (Factor H), respectively. It is shown by the experimental results that the optimal parameter combination of the pulsed plasma arc welding process is A1 (tip aperture: O1.5mm), B3 (plasma base current: 30A), C3 (plasma pulse current: 100A), D2 (duty cycle: 50%), E3 (pulse frequency: 300Hz), F2 (shielding gas: 14L/min), G3 (plasma gas: 0.4L/min), and H2 (welding velocity: 4RPM). Moreover, it is ascertained from the analysis of variance (ANOVA) results that plasma base current (B), plasma pulse current (C), duty cycle (D), and welding velocity (H) are the most important control factors in the process design, and thus strict control must be applied to them. They account for 75.02% of the total variance. The experimental results likewise show that the best process design could indeed enhance the multiple quality characteristics of the pulsed plasma arc welded SPFAL as 3020kgf of the bending strength, 13650kgf of the compression strength, and 180.4Hv of the hardness, respectively.

Principal component analysis for multiple quality characteristics optimization of metal inert gas welding aluminum foam plate

Principal component analysis (PCA) coupled with Taguchi methods are employed in the study for multiple quality characteristics optimization of metal inert gas (MIG) arc welding aluminum foam plates. The quality characteristics investigated are the micro-hardness and the bending strength of the weldments. Eight control factors selected are the type of filler material, MIG current, welding speed, MIG gas flow rate, workpiece gap, MIG arcing angle, groove angle, and electrode extension length. It is shown by the experimental results that the optimal parameter combination of the MIG welding process is A 2 (filler material: Type No. 5356), B 3 (MIG current: 100 A), C 1 (welding speed: 80 mm/min), D (MIG gas flow rate: 13 L/min), E 2 (workpiece gap: 1.7 mm), F 3 (MIG arcing angle: 50°), G 3 (groove angle: 20°), and H 1 (electrode extension length: 15 mm). Moreover, it is ascertained from the analysis of variance (ANOVA) results that B (MIG current), C (welding speed), and E (workpiece gap) are the most important control factors in the process design, and thus strict control must be applied to the three factors. The experimental results likewise show that the optimal process design could indeed improve the multiple quality characteristic values of the MIG-welded aluminum foam plates.