Surface hardness improvement in surface grinding process using combined Taguchi method and regression analysis

Shahri, Hamid Reza Fazli; Akbari, Ali Akbar; Mahdavinejad, Ramezanali; Solati, Ali

doi:10.1299/jamdsm.2018jamdsm0049

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…(3.1) Shahri et al, (2018) Where NTaguchi is the minimum number of experiments to be carried out. L is the number of levels while N is the number of parameters, and from Table 3.2 above, N = 3, L = 4.…”

Section: Taguchi Approachmentioning

confidence: 99%

Modeling and Optimization of the Thermophysical Properties of a Natural Ester Based Nano Fluid for a High Voltage Transformer

Edamaku,

Dan-asabe,

Abdullahi

2022

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In recent times, due to environmentally friendly issues, sustainable resources, and higher concerns regarding safety, research in the area of alternative liquid insulation in high voltage apparatus has been necessary. To actualize this goal, vegetable oil has been studied extensively and it has the potential to replace mineral oil in power transformers. In addition, the incorporation of nanoparticles has been remarkable in producing improved characteristics of insulating oil. In this study optimization of the Thermophysical properties which include viscosity, and the flash point was done using the Taguchi approach of design of experiment. The concentration of the nanoparticles, mixing, and drying time were varied and an optimal combination of parameters was observed and recorded. The optimum parameter combination for flash point is 0.9wt% concentration, 45 minutes mixing time, and 3hrs drying time. Viscosity has its optimum parameter combination at zero concentration of nanoparticles, mixing, and drying time. The optimized values of the thermophysical properties of the nanofluid reveal that the natural ester-based nanofluid is a suitable replacement for mineral oil in transformer cooling and insulation because of its improved properties. A mathematical model for the combination of concentration of nanoparticles, mixing and drying time for viscosity and flash point was developed and statistically validated. Hence can be used to navigate and predict the viscosity and flash point of the nanofluid within and outside the experimental design space. Keywords: Methyl ester, Viscosity, Flash Point, Optimization

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Section: Taguchi Approachmentioning

confidence: 99%

Modeling and Optimization of the Thermophysical Properties of a Natural Ester Based Nano Fluid for a High Voltage Transformer

Edamaku,

Dan-asabe,

Abdullahi

2022

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“…% contribution: It is the ratio of the sum of the squares (SS M ) of the variable to the total sum of the square (SS T ) of all the variables [34]. It shows the influence of the particular variable (factor) in terms of percentage on the response of the product/process as:…”

Section: Analysis Of Variance (Anova)mentioning

confidence: 99%

Optimization of the Polishing Efficiency and Torque by Using Taguchi Method and ANOVA in Robotic Polishing

Mohsin

et al. 2020

Applied Sciences

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Surface finishing and polishing are important quality assurance processes in many manufacturing industries. A polished surface (low surface roughness) is linked with many useful properties other than providing an appealing gloss to the product, such as surface friction, electrical and chemical resistance, thermal conductivity, reflection, and product life. All these properties require an efficient polishing system working with the best machining parameters. This study analyzed the effects of the different input polishing parameters on the polishing efficiency and torque in the robotic polishing system for the circular-shaped workpieces (such as ring, cylinder, sphere, cone, etc.) by using the Taguchi method and analysis of variance (ANOVA). A customized rotatory passive gripper is designed to hold the watch bezel during polishing. Under the design of experiments (DOE) technique, Taguchi’s L 18 array is selected to find the optimized process parameters for polishing efficiency (based on surface roughness) and torque. Experimental results with the statistical analysis by signal-to-noise ratio and ANOVA (95% confidence level) confirms that the polishing force and tool speed are the most influencing parameter for polishing efficiency in the system. Linear regression equations are modeled for the polishing efficiency and torque. Finally, a confirmation test is conducted for the validation of the experimentation results against actual results.

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“…1, this section is done to compare the results of surface hardness which is calculated by using this model with the results determined from the experiment. To accomplish this, the experimental results in [24] were selected for comparison. This is because in [24] a determination of surface hardness was obtained when grinding ASIS 1045 steel -the same material used in equation (1).…”

Section: Verifying Surface Hardness Modelmentioning

confidence: 99%

“…To accomplish this, the experimental results in [24] were selected for comparison. This is because in [24] a determination of surface hardness was obtained when grinding ASIS 1045 steel -the same material used in equation (1). To make this comparison, the data used in the experiment in [24] was used to calculate (see Table 1).…”

Section: Verifying Surface Hardness Modelmentioning

confidence: 99%

Calculation of Surface Hardness when Surface Grinding ASIS 1045 Steel

Trung

Tung

Sơn

et al. 2020

KEM

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This study presents a prediction study of the surface hardness in surface grinding ASIS 1045 steel. Base on the experimental data on the changes in characteristics of steel in the heat treatment processes, the relationship between the surface hardness and the impacted temperature in surface was found. Besides, the relationship between the cutting temperature and the technology parameters in the grinding was created. The surface hardness was then formulated as a function of the technology parameters in the grinding processes such as the depth of cut, the grinding wheel speed, the workpiece speed, etc. The proposed model was verified by comparing the predicted and measured results of surface hardness. This model can be used to predict the surface hardness when surface grinding.

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Surface hardness improvement in surface grinding process using combined Taguchi method and regression analysis

Cited by 6 publications

References 17 publications

Modeling and Optimization of the Thermophysical Properties of a Natural Ester Based Nano Fluid for a High Voltage Transformer

Modeling and Optimization of the Thermophysical Properties of a Natural Ester Based Nano Fluid for a High Voltage Transformer

Optimization of the Polishing Efficiency and Torque by Using Taguchi Method and ANOVA in Robotic Polishing

Calculation of Surface Hardness when Surface Grinding ASIS 1045 Steel

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