A Correlative Analysis of Machining Parameters with Surface Roughness for Ferrous and Non- Ferrous Alloy Materials
Abstract
Average Surface Roughness (Ra) is one of the most frequently used texture parameters to define the quality of turned components. The roughness values of a turned surface much depends on cutting parameters such as cutting speed, feed rate and depth of cut. Optimization of these parameters is very important in relation to surface roughness as they reveal the best suitable conditions for the turning operation. In this project, a correlative study of machining parameters and the surface roughness for ferrous (stainless steel 304) and non–ferrous alloy (Aluminium) material is carried out. Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) techniques are employed in this analysis to explain the influence of different cutting parameters on surface roughness values. The combination of optimum experimental parameters can be found by machining these ferrous and non-ferrous materials in CNC turning center and finding the least surface roughness parameters. ANOVA analysis, integrated with Design Expert software, is used to determine effective ratios of the parameters and subsequently the relationships between input parameters and their responses relationship are established. The minimum surface roughness results in reference to spindle rpm, feed rate, and depth of cut are determined and estimation of the optimal surface roughness values (Ra) for least surface roughness are the results obtained in the study. In case of stainless steel 304, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 1.33 microns are found to be 220 m. min-1 , 0.2 mm. rev-1 and 0.3 mm respectively. In case of Aluminium, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 2.8 microns are 200 m. min-1 , 0.2 mm. rev-1 and 1.15 mm respectively. These results reaffirm that RSM and ANOVA techniques are useful and efficient in achieving optimal set of machining parameters for select ferrous and non-ferrous materials in correlating the surface finish values.
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Introduction
Surface roughness is defined as the shorter frequency of real surfaces relative to the troughs. When machined parts are looked at carefully, their surfaces are found to embody a complex shape made of a series of peaks and troughs of varying heights, depths, and spacing. Surface roughness is greatly affected by the microscopic asperity of the surface of each part. Surface roughness for a machined component depends on the following factors:
cutting speed selected
depth of cut set
feed rate set
spindle speed set
type of coolant used
type of cutting tool used
Type of material being machined etc
In recent times, industrial manufacturing process has become more advanced and they are trying to produce the products at lesser cost and enhanced quality. Surface roughness is the most important factor which can increase the quality of machine components. Influence of each factor aforementioned on surface roughness can be experimentally predicted by using Response Surface Methodology (RSM) method supported by Analysis of Variance (ANOVA) technique. This experimental work is carried out on a CNC turning center in Caledonian College of Engineering. The experimental results help to predict the most optimal cutting parameters to achieve the least surface roughness values so that reliability, quality and longevity of the product are ensured at minimum product cost. This paper aims at analyzing and investigating the different machining parameters such as cutting speed, feed rate and depth of cut and their effect on surface roughness values for different ferrous alloy and non-ferrous alloys using RSM and ANOVA techniques and to make a comparative study on the same. Investigation results in proposing a new scope of improvement for industrial applications.
Conclusion
This study presents the findings of an experimental investigation into the effect of turning parameters like cutting speed, feed rate and depth of cut by turning ferrous (stainless steel 304) and non- ferrous material (Aluminium) in the CNC turning center and then checked the surface roughness values with Mitutoyo SJ-301 instrument. The effects of parameters and their correlation with the surface roughness and the optimal values have been analysed.
In case of stainless steel 304, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 1.33 microns are found to be 220 m. min-1 , 0.2 mm. rev-1 and 0.3 mm respectively. In case of Aluminium, optimal values of cutting speed, feed and depth of cut against the least surface roughness value of 2.8 microns are 200 m. min-1 , 0.2 mm rev-1 and 1.15 mm respectively.
These results reaffirm that RSM and ANOVA techniques are capable of achieving optimal set of machining parameters for select ferrous and non-ferrous materials machining and to effectively correlate the surface finish values obtained. Therefore, it is concluded that for stainless steel 304 feed and depth of cut are major influencing factors while for Aluminium, the feed rate is the major influencing factor in order to get minimum surface finish values. The study and associated results/outcomes of the analysis would add value to the current literature available in this research domain.
Effect of machining parameters on the tribology of the machined components can be a vast area to study and investigate. In this attempt, the effects of cutting parameters such as speed, feed and depth of cut are considered and their correlation with the surface roughness are analysed and presented. Although the current approach which is used to find the optimal parameters is Response surface method and ANOVA. The optimal results are varying for surface finish and therefore there should be improved unique values for the model to get even better surface finish values in the future work. Furthermore investigation and analysis can be extended to include other mechanical properties such as fatigue and torsion etc in order to enhance the level of investigation into this effect. Finally, a number of different materials such as alloys (ferrous and nonferrous) can be subject to this kind of study in order to draw a wider analysis and conclusion by taking this work as a reference work. This can open up new fields of research for young researchers to go beyond in this field and can benefit the industry.