A Comprehensive Literature Review on Surface Roughness in Engineering

This literature review investigates the critical issue of surface roughness in engineering, particularly within modern machining sectors. It addresses the need for high-quality workpiece dimensional accuracy, reliability, and surface finish. The review explores the limitations of traditional trial-and-error approaches in milling machine cutting and advocates for mathematical modeling using statistical methods and artificial neural networks (ANN). It examines various research efforts focused on surface roughness modeling, including studies utilizing statistical approaches, ANN, and swarm intelligence. The review identifies key parameters such as spindle speed, feed rate, and depth of cut, while also noting the gap in considering uncontrolled parameters like chip formation and tool wear. Ultimately, the literature review aims to provide a foundation for more effective approaches to surface roughness approximation and prediction in engineering, and the full document is available on Desklib, a platform offering study tools and resources for students.

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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

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related to this research problem of modeling of milling process to predict surface roughness
utilizing artificial intelligent approach in engineering (Gupta, Krishna and Suresh, 2017). The
review try to address the issue of conventional try and error approach which has been time
consuming and costly (Gupta, Krishna and Suresh, 2017). There have been numerous researchers
on surface roughness in the end milling utilizing various materials, experiment design, cutting
tool along with other approaches to obtain the surface roughness model.
To be able to model surface roughness, various approaches had been utilized in the previous
research Hadad and Ramezani (2016) developed a surface area roughness prediction model for
the 6061-T6 Aluminum Alloy machining employing statistical approach. The reason behind this
literature is within the progression of the forecasting model of the surface area roughness, to
analyze on the vital predominant variables among the cutting speed, feed rate, radial deepness
and in optimizing of the surface Roughness forecast Model (Wiedenmann and Zaeh, 2015).
Adnan et al. (2015) carried study on the influence of the tools of geometry on surface roughness
in the widespread lathe. Out of this research ANN methods was utilized precisely to the turning
when it comes to predicting the surface roughness. The primary merit to ANN method is its
simpleness and the speed of computations (Mamedov and Lazoglu, 2016). The current work is
related to exploring on the possibility to predict the surface finish. It has been found that the
neutral network could possibly be applied to find effective approximations of the surface
roughness. Nevertheless, the proposed methodology provided by this author has been confirmed
by means of the experimental information to the dry transforming of the carbide tools (Mamedov
and Lazoglu, 2016). The methodology has been discovered to be effective and uses lesser
training and also testing data. Conversely, experimental data and system which was designed

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showed that ANN decreases the drawbacks for instance, time, economical losses and materials to
a minimum (Mamedov and Lazoglu, 2016).
Uros et al. (2004) had suggested that selection of the machining variables is essential step to the
process of planning. Thus, new evolutionary computation approach is designed in optimizing
machining process. Particle Swarm Optimization (PSO) is employed to effectively enhance on
the machining variables simultaneously to the milling processes in which several contradictory
objectives they are present (Tamilarasan, Rajamani and Renugambal, 2015). At first, Artificial
Neutral Network predictive model is issued to be able to forecast on the cutting forces
throughout machining and then the PSO algorithm is utilized in obtaining of the optimum cutting
feed rates and speed. The purpose of optimization is determining the objective function
maximum by consideration of the cutting constraints (Zhou et al. 2015).
Hazim et al. (2009) designed surface roughness model with regards to End Milling via
employing Swarm Intelligence. Out of this selected studies, information that was amassed from
the CNC chopping investigations utilizing Design of Tests approaches. The data that is achieved
was then employed when considering calibration and verification (Tian et al .2017). These kinds
of inputs to the model are comprised of the Feed, Depth and Speed of cut whilst the outcome
from the design is surface area roughness. The design is validated by means of evaluation to the
experimental values with their forecasted alternatives. There is certainly the ideal agreement that
is found from this specific research (Zhang, Yu and Wang, 2017). The proved approach has open
door for the new, simple as well as economical approaches which can be employed on
calibration of other empirical designs for the machining.

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Mandara et al. (2001) designed multilevel, in process surface roughness reputation system in
evaluating the surface roughness in the process and in the real time. The major aspects associated
with the surface roughness throughout the machining process were depth of the cut, feed rate,
vibration that have generated between tool and work piece (Wan, Feng, Ma and Zhang, 2017).
The over-all MR-M-ISRR system which shown 82% of precision of the forecast average, and
creating promising step to additional development in the in process surface area to recognizing
system (Wan and Altintas, 2014).
Wan and Altintas (2014) had searched on surface roughness of brass machined by the micro end
milling miniaturized equipment tool. The cutting variables consisted of spindle speed, depth of
the cut, feed rate as well as aspect tool (Wan and Altintas, 2014). These kinds of authors they
employed the statistical approach, for instance, ANONA and RSM in assessing the test data.
From their experiment, they discovered the evaluation on the surface roughness develop the
linearly with the increase of the tool dimension and spindle speed.
Conclusion
Based on this literature review, the most parameters which have been used hen looking into the
optimal surface roughness are the spindle speed, feed rate and depth of the cut. These kinds of
studies had not regarded as the uncontrolled parameters for example the chips formations, tool
wear, chip loads, tool wear and properties of the materials of the tool and work piece. In the
review it has identified the gap which is present in the surface roughness approximations and the
more effective approach that might be employed.

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References
Adnan, M.M., Sarkheyli, A., Zain, A.M. and Haron, H., 2015. Fuzzy logic for modeling
machining process: a review. Artificial Intelligence Review, 43(3), pp.345-379.
Bajpai, V., Lee, I. and Park, H.W., 2014. Finite element modeling of three-dimensional milling
process of Ti–6Al–4V. Materials and Manufacturing Processes, 29(5), pp.564-571.
Cao, C., Zhang, X.M. and Ding, H., 2018. An analytical method in modeling of milling process
damping considering cutting edge radius. Procedia CIRP, 77, pp.106-109.
Gupta, A., Krishna, C.M. and Suresh, S., 2017. Modeling and Analysis of CNC Milling Process
Parameters on Aluminium Silicate Alloy.
Hadad, M. and Ramezani, M., 2016. Modeling and analysis of a novel approach in machining
and structuring of flat surfaces using face milling process. International Journal of Machine
Tools and Manufacture, 105, pp.32-44.
Mamedov, A. and Lazoglu, I., 2016. An evaluation of micro milling chip thickness models for
the process mechanics. The International Journal of Advanced Manufacturing Technology, 87(5-
8), pp.1843-1849.
Subramanian, M., Sakthivel, M. and Sudhakaran, R., 2014. Modeling and analysis of surface
roughness of AL7075-T6 in end milling process using response surface methodology. Arabian
Journal for Science and Engineering, 39(10), pp.7299-7313.
Tamilarasan, A., Rajamani, D. and Renugambal, A., 2015. An Approach on Fuzzy and
Regression Modeling for Hard Milling Process. Applied Mechanics & Materials.
Tian, Y., Liu, Y., Wang, F., Jing, X., Zhang, D. and Liu, X., 2017. Modeling and analyses of
helical milling process. The International Journal of Advanced Manufacturing Technology, 90(1-
4), pp.1003-1022.
Vasile, G., Fetecau, C., Amarandei, D. and Serban, A., 2016. Experimental Research on the
Milling Process of Some Composite Materials. MATERIALE PLASTICE, 53(1), pp.157-165.
Wiedenmann, R. and Zaeh, M.F., 2015. Laser-assisted milling—Process modeling and
experimental validation. CIRP Journal of Manufacturing Science and Technology, 8, pp.70-77.
Wan, M. and Altintas, Y., 2014. Mechanics and dynamics of thread milling process.
International Journal of Machine Tools and Manufacture, 87, pp.16-26.
Wan, M., Feng, J., Ma, Y.C. and Zhang, W.H., 2017. Identification of milling process damping
using operational modal analysis. International Journal of Machine Tools and Manufacture, 122,
pp.120-131.

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Zhang, X., Yu, T. and Wang, W., 2017. Instantaneous uncut chip thickness modeling for micro-
end milling process. Machining Science and Technology, 21(4), pp.582-602.
Zhou, L., Peng, F.Y., Yan, R., Yao, P.F., Yang, C.C. and Li, B., 2015. Analytical modeling and
experimental validation of micro end-milling cutting forces considering edge radius and material
strengthening effects. International Journal of Machine Tools and Manufacture, 97, pp.29-41.