## 1 Introduction  
Surface finishing and polishing are important quality assurance processes in many manufacturing industries. Effective quality control can be achieved by meeting the predefined specific standards of the final product. Surface polishing is a complex process depending on various input polishing parameters. The output of this process is a smooth surface which can be characterized by plenty of parameters such as lay, surface roughness, and waviness. Many industries are using surface roughness and glossiness as the judgment factor to achieve smooth surfaces [1; 2]. However, surface roughness is the most adopted parameter to quantify the surface finish [3; 4; 5; 6]. Presently, in most polishing factories, the understanding and the knowledge of the well-experienced workers is a gauge for setting the machining parameters. On the contrary, some use a trial and error method to achieve the approximated values, especially if they are dealing with a new workpiece or adopting a new polishing system. However, such methodsdo not lead the system towards the optimal setting for economical polishing. For economical surface polishing, it is necessary that all the input factors work on the optimum level. The design of experiments (DOE) is an organized approach to find out the effect of the input factors on the output of the process and help to optimize it. In other words, a DOE is an effective and economical method for experiment planning to achieve the desired results [7]. It offers statistical bases for analysis and helps to establish a conclusion. The idea of dealing with multiple factors of a process in an orderly manner is first presented by [PERSON] [7; 8; 9; 10]. This method encourages the full factorial DOE which uses all set of possible combinations of factors. However, processes with a large number of factors can rapidly increase the number of experiments in full factorial design. To reduce the number of experiments, a limited number of experimental runs can be select by using other DOE approach such as Taguchi method. [PERSON] has presented a systematic method that efficiently selects the set of experiments from all possible combinations which are useful to optimize the output economically in terms of performance and quality [11; 12; 13; 14; 15]. This method uses orthogonal arrays which use the minimum number of experiments to extract the complete information about input parameters of the process. These experimental arrays are generally selected depending on the tradeoff between the cost (time or resources) of the experiments and the accuracy of the results [16]. Many studies have been used the Taguchi method to optimize the surface quality parameters (such as roughness) for the polishing process. Usually, the automated polishing process consists of several steps using various polishing input parameters such as abrasive grain sizes, no. of polishing cycles, feed rate, polishing force, and others. [PERSON] and [PERSON] proposed the automatic polishing process step scheduling method with a compliant abrasive tool to select the optimal polishing parameters for different polishing steps [17]. An evaluation of the optimized parameters for the effective decrease in the surface roughness in each step is achieved by the Taguchi method. Another study [18] proposed the Taguchi DOE method to optimize the finishing conditions for the magnetic float polishing (MFP) technique to finish the silicon nitride balls for hybrid bearing applications. Reference [19] used orthogonal array \(L_{9}\) (\(3^{4}\)) for finishing advanced ceramic balls using a novel eccentric lapping machine. It used the standard three-level array to analyze the ice fixed abrasive polishing on the single crystal silicon wafer for polishing parameters (polishing pressure, table velocity, eccentricity, and polishing time). [PERSON] at el. investigated the parameters for chemical mechanical polishing (CMP) processes in wafer manufacturing by the Taguchi method and designs of experiments (DOE) approach to optimize it [20].