Taguchi Method Research Articles

Structure factor design of a liquid crystal beam steering device for augmented reality applications

The applications of AR and VR devices have been increasingly important in our daily life. There are some disadvantages remaining to be improved for real time application. In this paper, we demonstrated the design of an experiment based on the Taguchi method targeting a beam steering liquid crystal device possessing a 6 degree steering angle with fast switching (>60Hz) ability for viewing angle expansion in AR/VR devices. The approach of the best structure design was suggested from the recall table that concluded the optical phenomenon was affected by the selected parameters from simulation. The resulting design rule was a tape-out design to prepare the pattern ITO glass. The beam steering device was assembled according to the suggested cell gap. The device evaluation showed a 6.1 degree steering angle as predicted. The response time was measured as 10.5 ms, which is able to be driven well above 60 Hz.

Application of integrated entropy-COPRAS and ANN approaches for maximizing wire EDM machinability attributes of Al6082-T6/GNP/TiB2 composites

Wire Electrical Discharge Machining (WEDM) utilizes electrical sparks to cut conductive materials precisely. It employs a thin, electrically charged wire to erode the material, creating complex shapes with high accuracy, tight tolerance, and minimal material waste. The current study investigates the effect of WEDM process parameters on the various output properties of machining a hybrid Aluminium metal matrix nanocomposite (HAMMNC). A series of experiments were conducted on a composite constructed using Taguchi’s standard L18 Orthogonal Array (OA) design. The acquired responses of Material-Removal-Rate (MRR), Surface-Roughness (SR), and Kerf-Width (KfW) were optimized using integrated Entropy-COPRAS and artificial neural network (ANN) algorithms respectively. The relative significance ( Qi )values obtained for the alternatives are analyzed with the Taguchi method. The outcomes showed that the optimal setting of WEDM process variables is achieved at Servo Voltage: high volts; Pulse-on-time: 60 μs; Pulse-off-time: 16 μs; Peak Current: 6 Amp; Variable frequency: 18 Hz; Speed: 50 RPM; Sim Speed: 30 RPM respectively. The models developed from ANN were adequate and accurate from the results of error histogram and regression plots and they are best suited for the prophecy of future responses. The surface morphology studies confirmed the appearance of pockmarks, voids, and globules of debris on the Wire EDMed surfaces.

Tailoring the vibration characteristics of carbon fiber-reinforced polylactic acid in fused filament fabrication process

The Fused Filament Fabrication (FFF) process has gained significant attention for its ability to manufacture parts using advanced polymeric materials, such as carbon fiber-reinforced polylactic acid (CF-PLA). By adjusting the printing process parameters of the CF-PLA, the optimized mechanical properties can be achieved with less chances of flaws. Structural integrity of the components plays an important role in analyzing the durability of the product which can be analyzed using modal analysis. This research study focuses on analyzing the effect of process parameters on the vibration damping characteristics of CF-PLA components produced by the FFF process. The Taguchi method is used to model the relationship between process parameters and vibration performance metrics. Based on the vibration study, the optimal process parameters were determined as 60% infill density, 0.08 mm slice thickness, and hexagonal pattern. This study shows that the natural frequency and damping increases with increase in infill percent but decreases with increase in slice thickness. The closely aligned values between the predicted and experimental results suggest that the developed model is effective in predicting the vibration characteristics of CF-PLA composites. By accurately adjusting the process parameters, the study’s findings—such as frequencies and damping ratio—provide manufacturers with important and dependable CF-PLA Fused Deposition Modeling components.

Copper Ion Removal by Adsorption Using Fly Ash-Based Geopolymers: Process Optimization Insights from Taguchi and ANOVA Statistical Methods.

The present study aimed to use geopolymer materials synthesized from different fly ashes, which are promising for the adsorption of copper ions from aqueous solutions. The characterization of fly ashes and prepared adsorbents was performed by energy-dispersive X-ray spectroscopy (EDS) analysis, Brunauer-Emmett-Teller (BET) surface area analysis, and Scanning Electron Microscopy (SEM). Taguchi and ANOVA methods were used to predict the effect of different working parameters on copper ion removal by prepared geopolymers. Based on data obtained by the Taguchi method, it was found that the factor most influencing the adsorption process is the type of adsorbent used, followed by the solution pH, the reaction time, the adsorbent dose, and the initial copper ion concentration. The ANOVA results agree with the Taguchi method. The optimal conditions of the adsorption process were: fly ash C modified by direct activation with 2 M NaOH, at 70 °C for 4 h, solution pH of 5, initial pollutant concentration of 300 mg/L, 40 g/L adsorbent dose, and 120 min of reaction time. Copper ion removal efficiency was determined experimentally under optimal conditions, achieving a value of 99.71%.

UAV propeller fault diagnosis using deep learning of non-traditional χ2-selected Taguchi method-tested Lempel–Ziv complexity and Teager–Kaiser energy features

Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel–Ziv Complexity (LZC), and Teager–Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations. The extracted features were subjected to a Chi-Square (χ2) feature selection process to identify the most significant features for input into a Deep Neural Network. The Taguchi method was utilized to test the performance of the recorded features, correspondingly. Performance metrics, including Accuracy, F1-Score, Precision, and Recall, were employed to evaluate the model’s effectiveness before and after the feature selection. The achieved accuracy has increased by 0.9% when compared with results utilizing traditional statistical methods. Comparative analysis with prior research reveals that the proposed untraditional features surpass traditional methods in diagnosing UAV propeller faults. It resulted in improved performance metrics with Accuracy, F1-Score, Precision, and Recall reaching 99.6%, 99.5%, 99.5%, and 99.5%, respectively. The results suggest promising directions for future research in UAV maintenance and safety protocols.

Studies on parametric optimization of PLA/graphene composites synthesized by fused filament fabrication for thermal applications

The objective of this study is to examine the impact of various material and processing factors on the hardness (Shore D) and tensile strength of PLA/graphene composites. The study employed the Taguchi method to systematically optimize and statistically analyze various process variables, including the content of graphene (0 %, 0.5 %, and 1.0 % by weight), infill density (80 %, 90 %, and 100 %), and print speed (160 mm/s, 170 mm/s, and 180 mm/s). The composite material was fabricated utilizing the fused filament fabrication (FFF) technique, employing polylactic acid (PLA) and graphene as the primary feedstock materials. A standardized experiment was conducted in accordance with ASTM standards to assess the hardness and strength of samples that were prepared based on the experimental design. Based on the S/N ratio response table, it can be concluded that the reinforcement content is the most significant factor affecting both hardness and tensile strength. The optimal combination of process variables for achieving high hardness and tensile strength was found to be a graphene content of 1 %, an infill density of 100 %, and a print speed of 180 mm/s. The analysis of variance (ANOVA) indicated that the variable with the greatest statistical significance and substantial impact on the properties is the content of reinforcement. The analysis of fractures indicated a transition from ductile to brittle behavior with an increase in infill density. Furthermore, both composites demonstrated a brittle mode of failure irrespective of the infill density. Thus this composite material synergistically combines the inherent biodegradability of PLA with the improved thermal conductivity of graphene.

Chitosan/Arabic gum /ZnO bionanocomposite as a novel antibacterial agent

The synthesis conditions of chitosan/Arabic gum /zinc oxide nanocomposite were optimized using the Taguchi method to obtain antibacterial properties. FT-IR, XRD, FESEM, EDX, TEM, UV/VIS and TGA techniques were used to characterize the nanocomposite. Nanocomposite C3 (1 mg/mL chitosan, 4.5 mg/mL Arabic gum and 8 mg/mL zinc oxide), C7 (3 mg/mL chitosan, 5.1 mg/mL Arabic gum and 8 mg/mL zinc oxide) and C9 (3 mg/mL chitosan, 4.5 mg/mL Arabic gum and 4 mg/mL zinc oxide) had the best antibacterial properties against S. mutans. TGA showed that ZnO improved the thermal stability of the nanocomposite. Such materials can be used as antibacterial agents.

Multi-Objective Optimization of Injection Molding Parameters for Manufacturing Thin-Walled Composite Connector Terminals.

The rapid development of new energy vehicles demands significant improvements in connector structures and performance standards. Wire harness connectors, crucial for linking various electrical components, face challenges due to their small size and thin-walled structure, which can lead to dimensional shrinkage and warping during injection molding. To address these issues, this study optimizes the injection molding process by fine-tuning parameters such as melt temperature, mold temperature, injection time, holding pressure/time, and cooling time. By integrating the Taguchi method with grey relational analysis, the study enhances the molding process for thin-walled composite connectors. This combined approach provides a comprehensive framework for optimizing multiple quality objectives and improving the overall performance of injection-molded composite components.

Utilization of RESOLV with polymer to produce prazosin hydrochloride nanoparticles and optimization of the process parameters

In this study, rapid expansion of a supercritical solution into a Liquid Solvent (RESOLV) was used for the first time to produce pharmaceutical nanoparticles of Prazosin hydrochloride (PRH). The Taguchi method (robust design) was utilized to design the experiments and ensure obtaining the optimal process conditions. The pressure (15–25 MPa), temperature (308–328 K) and nozzle diameter (300–700 μm) effects on the morphology and size distribution of the resulting particles were also examined. The size of the particles decreased from about 40 μm to the range of (252–418 nm). FTIR, DLS, FESEM, XRD, DSC were used to characterize the primary and processed PRH particles. According to DSC investigations, RESOLV-produced PRH showed lower crystallinity than original PRH.

Optimization of Lipid Production of Rhodosporidium toruloides from Various Substrates

Background and Aim: In recent years, bioenergy has received a lot of attention from a technical point of view due to its ability to use reversible resources and its applicability worldwide. Biodiesel is a non-toxic, safe, renewable, and degradable fuel that is obtained from natural sources, such as vegetable oils, food waste oils, animal fats, algae, and microorganisms. This study aimed to optimize the lipid production of Rhodosporidium toruloides as a biofuel from different substrates. Materials and Methods: In this experimental laboratory study, after lipid extraction from Rhodosporidium toruloides CECT1137, cell biomass, nitrogen, and sugar consumption were measured according to the Bly and Dyer method. Then, using gas chromatography, the produced lipid compounds were identified, and lipid production was optimized by the one-factor method (type of nitrogen source and KH2PO4) and the Taguchi method (carbon and nitrogen source, temperature, pH, centrifuge, and time). Finally, the feasibility of mutation was investigated using UV to optimize lipid production. Results: The amount of lipid produced by Rhodosporidium toruloides was 4.95 g/l, which, after optimization by the Taguchi method, reached 8.54 g/l. Cell biomass, residual nitrogen, and consumed sugar were measured as 14.05, 0.19, and 28.85 g/l, respectively. Optimal production conditions were estimated by Taguchi, including 80 g/l carbon source, 1 g/l nitrogen source, 25 ° C, pH = 5.5, centrifuge at 180 rpm, and 96 hours. Most lipids produced were oleic acid and palmitic acid. Lipid production increased after UV exposure. Conclusion: The results showed that Rhodosporidium toruloides can accumulate a significant amount of lipid in its cells and can be a good alternative to plants and other sources of fuel production.

Removal of volatile organic compounds using fixed bed adsorption: optimization using Taguchi methodology and grey relational analysis

Removal of volatile organic compounds using fixed bed adsorption: optimization using Taguchi methodology and grey relational analysis

Parametric study and optimization of H-type finned tube heat exchangers with honeycomb arrangement using Taguchi method

Enhancing the efficiency of flue gas waste heat utilization is an important way to achieve carbon peaking and carbon neutrality goals. In this paper, a three-dimensional numerical model of an H-type finned tube heat exchanger with honeycomb arrangement is developed. Firstly, the effects of five parameters are analyzed using the control variable method, including the oblique tube pitch, the ratio of fin height to oblique tube pitch, fin pitch, fin thickness and slit width. Then, the Taguchi method was used to investigate the interaction effects of the five geometric parameters. Numerical simulations of 27 cases with different parameter combinations were carried out and the heat transfer and flow friction characteristics and their overall performance were discussed in detail. The results show that oblique tube pitch has the most significant effect on heat transfer capacity, fin pitch has the most significant effect on overall performance, and slit width has the least effect on both. Finally, the optimum parameter combinations were obtained. When inlet velocity ranges from 5 m/s to 10 m/s, heat transfer performance is increased by 46.3 %-52.1 %, pressure drop is increased by 5.7 % − 11.3 %, and the overall performance PEC is improved by 63.5 %-105.9 %. The results of the study contribute to the development and optimization of new flue gas heat exchangers.

Overall aerodynamic performance of the airfoils with different amplitudes via a fuzzy decision making based Taguchi methodology

In this study, the overall aerodynamic performance of the airfoils with variable amplitude geometric structure for lift-type vertical axis wind turbine has been analyzed and optimized using Fuzzy Analytic Hierarchy Process (AHP)-Fuzzy COmbinative Distance-based Assessment (CODAS) based on Taguchi Method. In the design of the experiment, leading edge (LE) tubercle airfoil models, Reynolds number, and angle of attack are utilized as the control factors. Drag, lift, and lift to drag ratio performances by comparing the airfoils according to the baseline model are calculated and the combination of these performances is considered as the overall aerodynamic performance response. The experiments are carried out according to the orthogonal matrix L18. Since the importance of each response is different on lift-type vertical axis wind turbines, the importance weights of these responses were obtained by Fuzzy AHP according to the evaluation of the decision maker. Multiple responses are converted into a single response (the overall aerodynamic performance) via Fuzzy CODAS which enables to model uncertainty in order to overcome the physical effects that may arise in experiments. Thereby, a multi objective Taguchi methodology was applied for the overall aerodynamic performance with a novel Fuzzy logic based multi criteria decision analysis approach. As a result, Model-4, having tubercle design parameters of amplitude of 0.025c and 0.0125c and wavelength of 0.5c and 0.125c, was found to have the best performance with the novel Taguchi approach, which saves time and cost. In addition, it has been determined that the airfoils, Reynolds number and angle of attack are significant and what level of contribution to the overall aerodynamic performance. At this point, the most important control factor has emerged as the angle of attack.

Process Optimization for Biosurfactant Production by Bacillus aryabhattai SPS1001 using Taguchi Method.

This study employs Taguchi design of experiments (DOE) to optimize biosurfactant yield by analyzing the impact of various input parameters. Signal-to-noise ratio analysis was utilized for optimization, corroborated by ANOVA findings. Regression equations depicted response behaviour and are validated through a confirmation test. Taguchi methodology identified optimal conditions for maximum biosurfactant yield: agitation (180rpm), inoculum size (2%), beef extract (5g/L), diesel (20ml/L), peptone (5g/L), NaCl (7g/L), incubation time (4days), pH (7.9), and yeast extract (6g/L). This yielded an 8.33% increase to 1.53g/L, with initial optimum parameters projecting 1.41g/L. ANOVA ranked and quantified control factor contributions, revealing agitation's significant (31.41%) impact on yield. The study underscores the viability of Taguchi's optimal conditions for substantial yield improvement within specific ranges. The strong alignment between expected and experimental yields affirmed the reliability of developed models for optimal yield selection. This study underscores the power of statistical techniques like Taguchi DOE and ANOVA in systematically enhancing biosurfactant production by Bacillus aryabhattai SPS1001 and paves the way for future advancements in bioprocess optimization.

A Self-Training-Based System for Die Defect Classification

With increasing wafer sizes and diversifying die patterns, automated optical inspection (AOI) is progressively replacing traditional visual inspection (VI) for wafer defect detection. Yet, the defect classification efficacy of current AOI systems in our case company is not optimal. This limitation is due to the algorithms’ reliance on expertly designed features, reducing adaptability across various product models. Additionally, the limited time available for operators to annotate defect samples restricts learning potential. Our study introduces a novel hybrid self-training algorithm, leveraging semi-supervised learning that integrates pseudo-labeling, noisy student, curriculum labeling, and the Taguchi method. This approach enables classifiers to autonomously integrate information from unlabeled data, bypassing the need for feature extraction, even with scarcely labeled data. Our experiments on a small-scale set show that with 25% and 50% labeled data, the method achieves over 92% accuracy. Remarkably, with only 10% labeled data, our hybrid method surpasses the supervised DenseNet classifier by over 20%, achieving more than 82% accuracy. On a large-scale set, the hybrid method consistently outperforms other approaches, achieving up to 88.75%, 86.31%, and 83.61% accuracy with 50%, 25%, and 10% labeled data. Further experiments confirm our method’s consistent superiority, highlighting its potential for high classification accuracy in limited-data scenarios.

Modeling of hydrogen separation through Pd membrane with vacuum pressure using Taguchi and machine learning methods

The performance of hydrogen purification using palladium (Pd) membrane is analyzed by Taguchi and machine learning (ML) methods. Three system factors are considered: the feed gas mixture composition (i.e., H2, CO2, and H2O), retentate-side total pressure, and vacuum pressure. The Taguchi method designs the experimental cases based on the constructed orthogonal array. In addition, the hydrogen flux is investigated by the analyses of variance (ANOVA) and artificial neural networks (ANN) methods. The results show that the effects of the considered factors on the hydrogen permeation performance can be ranked as feed gas mixture composition > retentate-side total pressure > vacuum pressure. Both the retentate-side pressure and vacuum pressure present a positive effect on hydrogen flux. The average relative error of hydrogen flux between the experimental results and predictions by ANN is 2.1%, which proves that the ANN method is an effective technique for predicting the hydrogen flux through the Pd membrane. The ML classification test is then performed for hydrogen purity by three machine learning methods: decision tree (DT), support vector machine (SVM), and ensemble method. Among the various classification models, the Quadratic SVM model exhibits a relatively high average training accuracy but shows overfitting. However, the bagged model of the ensemble method can achieve an impressive training accuracy of 91.4% and a prediction accuracy of 85.7%.

Responses analysis and structural optimization of a semisubmersible platform based on the Taguchi experiment method

Responses analysis and structural optimization of a semisubmersible platform based on the Taguchi experiment method

Achieving superior Laplace pressure in electrospun PVDF/PAN Janus membranes: A morphological approach

Janus membranes have asymmetric wettability on each side. This contrasting structure gives them an internal driving force for spontaneous fluid transport (caused by Laplace pressure). The objective of this research is to attain the maximum Laplace pressure for electrospun PVDF/PAN Janus membranes by adjusting the fiber diameter and, consequently, the pore size and contact angle in each layer of the membrane. To achieve this goal, the Taguchi method was employed, and the parameters of the electrospinning process were modified to determine the optimal conditions for obtaining the smallest and largest fiber diameters for PAN and PVDF polymers. Accordingly, four distinct types of Janus membranes with different morphologies in each layer were synthesized. SEM analysis was employed to characterize the structural and morphological properties of the fibers in each layer. Additionally, the contact angle (CA) was measured to assess the hydrophilicity of each layer and determine the droplet penetration rate across the membrane. According to the results, the maximum Laplace pressure (205.60 kPa calculated by the analysis of SEM images and 197.4 kPa calculated by the bubble point test) and highest droplet penetration rate (0.83 µL/s) were attributed to the Janus membrane with the lowest pore size in the hydrophilic layer (0.65 µm) and the highest pore size in the hydrophobic layer (3.44 µm). The performance of the optimized membrane was evaluated with respect to unidirectional water transport and water–oil mixture separation. The results showed a rectification ratio of3.92, a permeate flux of7.9 × 104 L·m−2·h−1·bar−1, and a separation efficiency of96.8 %. Finally, it was demonstrated that the Laplace pressure of the optimal Janus membrane in this study is 2.66 times superior to that of the best similar one in previous studies. The appropriate and allowable range for pore size and contact angle, necessary to attain a satisfactory Laplace pressure (exceeding 100 kPa), has been explored, determined, and recommended for future researchers to foster inspiration in the field of Janus membranes.

DEVELOPMENT AND PERFORMANCE EVALUATION OF A MODEL CUTTING FLUID DEVELOPED FROM SHEA BUTTER OIL

This research was carried out to develop a model cutting fluid from Shea Butter oil and evaluate its performance in relation to temperature and surface roughness when compared to a commercial cutting fluid. The effectiveness of the cutting fluid was evaluated on a mild steel using a turning operation on the lathe machine. Temperature variations during the turning operation were measured using an infra-red gun thermometer while surface roughness was evaluated by employing a surface roughness tester. In this work Taguchi method was used to guide the selection of machine parameters such as cutting speed (rev/min), feed rate (mm/rev), and depth of cut (mm). Using Minitab, the cutting temperature and resulting surface roughness were measured for both the developed cutting and commercial fluids. The results obtained for the average cutting temperature of ~ 30oC and overall surface roughness value of 0.32μm for shea butter oil show a significant difference from that of ~ 35oC and 0.47 μm respectively for commercial grade cutting fluids. These results show that the shea butter oil-based cutting fluid not only exhibits great potential but also surpasses the current capabilities of commercial cutting fluids in terms of its cooling and surface finishing performances.

Dynamic Robust Parameter Design Using Response Surface Methodology based on Generalized Linear Model

Purpose: When designing an input-output system susceptible to noise, engineers assume a functional relation between the input and the output. The Taguchi method, which uses a dynamic, robust parameter design (RPD) to evaluate the robustness of the input-output relation against noise, is employed. This study aims to address extending the scope of use of a dynamic RPD. Methodology/Approach: A target system in a typical dynamic RPD can be interpreted as one in which the relation between the input and the output is a linear model, and the output error follows a normal distribution. However, an actual system often does not conform to this premise. Therefore, we propose a new analysis approach that can realize a more flexible system design by applying a response surface methodology (RSM) based on a generalized linear model (GLM) to dynamic RPD. Findings: The results demonstrate that 1) a robust solution can be obtained using the proposed method even for a typical dynamic RPD system or an actual system, and 2) the target function can be evaluated using an adjustment parameter. Research Limitation/implication: Further analysis is required to determine which factor(s) in the estimated process model largely contribute(s) to changes in the adjustment parameter. Originality/Value of paper: The applicability of typical dynamic RPD is limited. Hence, this study’s analytical process provides engineers with greater design flexibility and deeper insights into dynamic systems across various contexts. Category: Research paper Keywords: robust parameter design; dynamic system; generalized linear model; response surface methodology; Taguchi method