“Effect of Hydroxyapatite Nanoparticles On Compressive strength and Flexural strength of Dental Composites Using Taguchi Method”

Several composite materials are being used in biomedical and dental field with their immense applications to repair and transform various organs in human body. Recent advances suggest that Hydroxyapatite is one of the most reliable and widely used inorganic composite in dentistry. Desirable applications of Hydroxyapatite are achieved by utilizing variety of hydroxyapatite and their composites. This study was conducted to evaluate the compressive & flexural strength. Cylindrical specimens (n=9) for compressive strength & rectangular shaped specimens (n=9) for flexural strength were made according to manufacturer’s recommendations. Dental composite is using quartz, silica, and alumina glass as filler for a long time. Taguchi optimization technique keeps the experimentation within limit giving valid product in the calculating of compressive and flexural strength optimization. The goal of the work is to detect the best combination of composite materials. Keywords: Hydroxyapatite, Compressive Strength, Flexural Strength, Taguchi’s optimization method.

Investigation on the Effect of Impeller Design Parameters on Performance of a Low Specific Speed Centrifugal Pump Using Taguchi Optimization Method

In order to investigate the effect of blade design on pump performance, a CFD analysis was carried out, and the results were compared with experimental performance data of a low specific speed radial pump, which presents a good agreement. After model verification, the effect of impeller geometrical parameters includes blade outlet angle, wrap angle, and width at the exit, was investigated on the pump’s performance. Moreover, these parameters were chosen on three levels using an L9 orthogonal standard array of the Taguchi optimization method. The efficient levels of variables were calculated using the analysis of variance (ANOVA) method. The results revealed that impeller width at exit and blade outlet angle is the most effective pump shaft power and efficiency parameters. To minimize power, the optimal levels are the outlet angle of 27∘∘, wrap angle of 150∘∘, and width at the exit of 9 mm. Further, an outlet angle of 23∘∘, a wrap angle of 155∘∘, and a width at the exit of 9 mm lead to maximum pump efficiency. According to the validation simulations, an increase of 2.4% inefficiency and a minimum power of 3.9KW were achieved. The Overall Evaluation Criteria (OEC) technique revealed that considering 23∘∘, 160∘∘, and 9 mm for outlet angle, wrap angle, and width at the exit, minimum shaft power, and maximum pump efficiency will be achieved. ANOVA introduced width at the exit as the most governing parameter of pump performance characteristics.

Optimization of Three-Phase Hybrid Stepper Motors for Noise Reduction

For the optimization of three-phase hybrid stepper motors with complex electromagnetic structures, an optimization method is presented in this paper. The method is a combination of 3D-FEM and the Taguchi optimization method intended to reduce the dependence on FEM results during the optimization calculation. In this paper, the optimization method is used in the optimization of the tooth shape of the three-phase hybrid stepper motor, and the objective is to reduce the noise caused by harmonics in the “torque-angle characteristic” of the motor. It is clear that traditional optimization methods make it very difficult to carry out such an optimization calculation as a large number of finite element calculations have to be used in the optimization process, and the required computation time is extremely long. Using the optimization method presented in the paper, the optimization becomes feasible because the number of finite element calculations is greatly reduced and the computation time is thus greatly reduced. In order to check the effectiveness of the optimization, the waterfall diagram for noise analysis and its application to check torque ripple are also presented in the paper. Both simulation and test results show that the optimized structure can significantly reduce the motor noise caused by torque ripple. Therefore, the optimization method proposed in this paper can be an effective tool for the optimal design of high-performance motors, including stepper motors.

Investigation of the effect of cutting parameters on the milling process of cryogenically treated aluminum alloy with cryogenically treated and untreated inserts, using the Taguchi and Gray Relational Analysis methods

The aluminum 6061 alloy is commonly employed in the automotive industry in the manufacture of rims, panels and even the chasses of vehicles and has excellent machinability. In this study, the surface of the cryogenically processed aluminum 6061-T651 alloy was milled using both untreated and cryogenically treated TiN-TiCN-Al2O3-coated cutting inserts. The Taguchi L18 orthogonal array was chosen as the experimental design. As the cutting parameters in the experiments, two different cutting inserts (untreated and cryogenically treated, TiN-TiCN-Al2O3-coated), three different cutting speeds (250, 350 and 450 m/min) and three different feed rates (0.15, 0.30 and 0.45 mm/rev) were used. After each experiment, the surface roughness and wear values of the cutting inserts were measured, the latter after repeating the experiment five times. Wear and roughness values were optimized using the Taguchi method. Additionally, Gray Relational Analysis (GRA) was used for the combined optimization of wear and roughness values. The optimized findings determined using Taguchi optimization for minimum surface roughness were the cryogenically treated cutting insert, 250 m/min cutting speed and 0.45 mm/rev feed rate. The optimized findings for wear were the cryogenically treated cutting insert, 350 m/min cutting speed and 0.30 mm/rev feed rate. In the optimization with GRA, the common optimum parameters for surface roughness and wear were the cryogenically treated cutting insert, 250 m/min cutting speed and 0.15 mm/rev feed rate. According to the Taguchi and GRA results, the cryogenically treated cutting inserts performed the best in terms of minimum wear and surface roughness. The Gray-based Taguchi methodology proposed in this study was found to be effective in solving the decision-making problem in multi-specific results as wear and surface roughness.

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

Parameter design of PLA/Wood Fused Filament Fabrication using Taguchi optimization methodology

This study investigates the effects of four variables during fused filament fabrication of organic biocompatible composite material, PLA with coconut flour, at the ultimate tensile strength and elasticity module of the printed parts. The parameter optimization uses Taguchi L18 design and regression models. The examined deposition variables are the layer thickness, the nozzle temperature, the raster deposition angle, and filament printing speed. The effects of the above variables on the strength of the parts are essential to enhance the mechanical response of the printed parts. The experimental outcomes are investigated using the ANOM and ANOVA analysis and modeled utilizing linear regression models. In addition, an independent experiment was repeated three times at optimum parameters' levels to evaluate the methodology, giving predictions errors less than 3%.