• International Journal of Technology (IJTech)
  • Vol 13, No 1 (2022)

Dry Milling Machining: Optimization of Cutting Parameters Affecting Surface Roughness of Aluminum 6061 using the Taguchi Method

Dry Milling Machining: Optimization of Cutting Parameters Affecting Surface Roughness of Aluminum 6061 using the Taguchi Method

Title: Dry Milling Machining: Optimization of Cutting Parameters Affecting Surface Roughness of Aluminum 6061 using the Taguchi Method
Shamsuddin Sulaiman, Mohammad Sh Alajmi, Wan Norizawati Wan Isahak, Muhammad Yusuf, Muhammad Sayuti

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Cite this article as:
Sulaiman, S., Alajmi, .M.S., Wan Isahak, W.N., Yusuf, M., Sayuti, M., 2022. Dry Milling Machining: Optimization of Cutting Parameters Affecting Surface Roughness of Aluminum 6061 using the Taguchi Method. International Journal of Technology. Volume 13(1), pp. 58-68

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Shamsuddin Sulaiman Faculty of Engineering, Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Mohammad Sh Alajmi Faculty of Engineering, Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Wan Norizawati Wan Isahak Faculty of Engineering, Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
Muhammad Yusuf Department of Mechanical Engineering, Faculty of Engineering, Universitas Malikussaleh, 24351 Aceh Indonesia
Muhammad Sayuti Department of Industrial Engineering, Faculty of Engineering, Universitas Malikussaleh, 24351 Aceh Indonesia
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Abstract
Dry Milling Machining: Optimization of Cutting Parameters Affecting Surface Roughness of Aluminum 6061 using the Taguchi Method

In this paper, the application of dry machining as part of contributions for development of a sustainable environment in the machining industry is explained. Achieving a good surface roughness product is one the most important factors that must be considered in the metal machining process. Surface quality control is a complicated process, and a reliable technique is required during machining operation. Currently, an appropriate cutting conditions in most of machining cases are determined by trial and error, which leads to time increased, energy consumption, and manufacturing costs. Most of the previous studies have investigated factors that affect surface roughness, but different machining conditions require the control of different factors. In this study, experiments were conducted to optimize cutting parameters and determine the factors significant for the surface roughness quality. Machining experiments were conducted on a vertical milling machine using square non-coated two flutes HSS Co end mill with selected cutting parameters on aluminum 6061. This study focused on the surface roughness in one direction and combined with the Taguchi design method. Signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were employed to examine and reveal the factors that are significant in affecting surface roughness quality. The analysis result revealed that cutting speed exerts the highest effect on surface roughness, followed by feed rate and depth of cut. Finally, the combination of dry machining performance and an eco-friendly environment would result in competitive sustainable growth of the machining industry.

ANOVA; Machining process; Signal-to-noise; Surface roughness; Taguchi method

Introduction

In the machining industry, a liquid coolant, also called a cutting fluid, is used to remove the heat produced during machining. However, the use of cutting fluids incurs considerable economic and ecological burden, which is continuously increasing (Wickramasinghe et al., 2020). Therefore, researchers have attempted to utilize machining components without using cutting fluids, which is referred to dry machining.

In the last few years, the monitoring of surface roughness has been developed, and a few different measuring systems are also available to measure surface roughness (Saif and Tiwari, 2021). To obtain the desired surface quality of parts by end-milling machining, few cutting parameters should be selected and controlled appropriately during machining, including cutting speed, feed rate, and depth of cut. Poor selection of machining parameters leads to the rapid wear and breakage of cutting tools.

A few number of processes that can be utilized to produce raw materials of any desired shape from its initial stage to its final stage are available. Among various machining processes, end milling is one of the most widely employed material removal processes in industries (Nisar et al., 2021). Cutting operations by end mills can be as simple as face milling on the top of a flat surface using a rigid cutter or the milling of extremely complex parts (Hoang et al., 2019). Aluminum is mainly used in industries to produce various parts, especially for the assembly of machine parts (Daghfas et al., 2017).

Typically, surface roughness is utilized as an extremely good performance predictor of mechanical components of machined materials as irregular surface properties may lead to cracks or corrosion (Dinesh et al., 2014). Sometimes, although surface roughness is undesirable for certain manufacturing products, it is quite difficult to control and may incur higher machining costs (Singh et al., 2020).

Elmunafi (2015) has reported that cutting speed is the most significant cutting parameter, followed by feed rate and depth of cut (Elmunafi et al., 2015; Tapadar et al., 2017). On the other hand, Liu (2016) has reported that feed rate is the most significant factor that affects surface roughness, followed by depth of cut and cutting velocity, for minimizing energy consumption (Liu et al., 2016). Shah and Bhavsar (2020) have investigated four machining parameters, namely cutting speed, feed rate, depth of cut, and nose radius, respectively, to examine the maximum tool life and minimum power consumption (Shah and Bhavsar, 2020). Their study results revealed that cutting speed is the most significant parameter to achieve the maximum tool life with minimum power consumption, followed by depth of cut, feed rate, and nose radius.

In studies on surface roughness, various methodologies and practices have been employed and applied for the prediction of quality surface roughness, such as artificial intelligence or soft computing techniques, the Taguchi method, response surface methodology (RSM), machining theory, classical experimental design, and artificial neural network (Qehaja et al., 2015; Kilickap et al., 2017). Recently, the design of experiments (DOE) method has been widely used in various industries for several years to improve the product and manufacturing process (Razavykia et al., 2015). Vishnu Vardhan (2017) has applied the Taguchi method to investigate the effects of feed rate, cutting speed, nose radius, depth of cut, and cutting environment of AISI P20 tool steel machining on power consumption. The results revealed that cutting speed is the most significant factor, followed by feed rate and depth of cut (Vishnu Vardhan et al., 2017). Meanwhile, Qasim (2015) has employed the Taguchi design and analysis of variance (ANOVA) to investigate the effect of cutting parameters on surface finish and power consumption during the high-speed machining of AISI 1045 steel with a coated carbide tool (Qasim et al., 2015). The result revealed that cutting speed is the most significant factor, followed by depth of cut, to reduce power consumption. Therefore, generally, it is complicated to determine the relationship between cutting parameter as a machining control parameter and response characteristics due to various factors that affect surface finish (Singh et al., 2020).

    ANOVA has been used predominantly in experiments for statistical analysis and to reveal cutting parameters that significantly affect response variables as well as performance characteristics (Rathod et al., 2021). In addition, ANOVA can determine the effect of machining parameters on various responses, including power consumption, cutting force, and material removal rate (MRR), etc. (Khentout et al., 2019). In the analysis, the sum of squares (SS) and variance of square are calculated, and the F-test ratio at the 95% confidence level is employed to determine the significant factors that affect machining (Ahmed et al., 2015). Usually, at a high F-ratio, the machining factor significantly affects the response variable (Maiyar et al., 2013). Thus, according to ANOVA, a high F-ratio value indicates a large significant of control factors. In addition, the probability value (P) also revealed the level of significance of each control factor. Low P values indicated that control factor values exhibit a high probability of falling within the ranges, thereby impacting the experiment results (Qasim et al., 2015).

Conclusion

In conclusion, dry machining was demonstrated to contribute to an eco-friendly environment in the machining industry. In this study, an experimental study for the prediction and optimization of cutting parameter is discussed, where a minimum surface roughness of aluminum 6061 using an HSS-Co Helical Shank Insert is subjected to a dry milling cutting condition. The Taguchi method combined with the design of experiment is applied for the response characteristic optimization. The relationship between control parameters (e.g., cutting speed, feed rate, and depth of cut) and surface roughness on the basis of different levels by the DOE method is determined. Analysis in terms of the significant effect clearly revealed that the three control parameters exhibit an important correlation between each other. In terms of the three control parameters, surface roughness quality mainly depends on cutting speed, followed by feed rate and depth of cut. A high cutting speed affords high surface roughness quality.  However, surface roughness quality decreases with a low feed rate and depth of cut.

The S/N ratio analysis revealed that the cutting speed is the most significant factor affecting surface roughness, followed by feed rate (moderately significant) and depth of cut (least significant factor), of a machined surface. The optimal combination of the control parameters in minimizing surface roughness is A3B1C1, indicating a cutting speed of 300 m/min (level 3), a feed rate of 150 mm/rev (level 1), and a depth of cut of 0.5 mm (level 1). Similarly, ANOVA results also revealed the same result as that of S/N ratio analysis.

SEM analysis is employed to observe the morphology of the machined surface on the workpiece material. SEM images revealed that surface roughness is uneven when machining is conducted under a lower cutting speed and the surface is covered by a thin film, and it is less uneven when machining is conducted under a higher cutting speed, which affords a better surface roughness quality. Besides, a low feed rate and depth of cut contribute to a better surface finish quality compared with a higher feed rate and depth of cut, leading to disruption in the metal removal process.

Finally, positive outcomes of the dry machining process should be implemented in the machining industry worldwide, especially in Euro-Mediterranean countries. The combination of dry machining performance and an eco-friendly environment would lead to competitive sustainable growth in the machining industry.

Acknowledgement

    The authors would like to thank the staff at the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia UPM Serdang, Selangor Malaysia and Faculty of Engineering Universitas Malikussaleh for their supports and laboratory facility. This study was financially supported by the Fundamental Research Grant Scheme (FRGS) with contract number FRGS/1/2015/TK03/UPM/01/1 from Ministry of Education (MOHE), Malaysia.

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