Literature Review on Surface Roughness in Engineering

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Literature review on surface roughness in engineering
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Introduction
The problem to the modern machining sectors has mainly accentuated achievement of high
quality, in relation to the work piece dimensional accuracy and reliability , surface finish , less
wear on the cutting tools, substantial production rate and economy of machining with regards to
cost saving and increase in performance ( Adnan et al. 2015). End milling is a typically
employed machining process in the industry. The capability to control this method is much better
quality to the final product. The surface roughness to the machined design specification that is
recognized is considerable impact to properties such as wear resistance and fatigue strength
(Adnan et al. 2015). Presently, the manufacturers in the manufacturing industry they are
specializing in providing quality and efficiency of the product (Tamilarasan, Rajamani and
Renugambal, 2015). To have the ability to increase on the productivity of the product, computer
numerically machine tools they have been utilized in the past decades (Cao, Zhang and Ding,
2018). Surface roughness is amongst the crucial parameters with regards to determining the
quality of the product. The mechanisms that are behind the formation of the surface roughness
are extremely dynamic; process reliant and complex (Cao, Zhang and Ding, 2018). There are
several factors which can affect the final surface roughness in the CNC milling operations for
example controllable components and ungovernable aspects.

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Figure 1: Profile of the asperities on the surface of solid.
Some of the machine operators are using trial and error approaches to establish milling machine
cutting situations. This approach is inefficient and effective and achievement of the desirable
value is repetitive and in addition empirical process which can be time consuming (Vasile,
Fetecau, Amarandei and Serban, 2016).
Figure 2: The diagram shows evaluation of the surface roughness
Therefore, the mathematical model utilizing statistical approach offers a much better solution.
Multiple regression evaluation happens to be ideal to finding the best combination for the
independent factors which is spindle speed, depth of cut and feed rate to attain desired surface
roughness (Subramanian, Sakthivel and Sudhakaran, 2014). It is unfortunate; multiple regression
models could be obtained from the statistical analysis which requires a large sample of the data.
Realizing on that particular matter, Artificial Neural Network is the state of the art artificial
intelligent approach which has some possibility in enhancing the prediction of the surface
roughness (Gupta, Krishna and Suresh, 2017). This review is from the previous research which is