Surface Finish Comparison of Dry and Coolant Fluid High-Speed Milling of JIS SDK61 Mould Steel

This paper investigates the influence of dry high-speed milling on the surface quality of JIS SKD61 hard steel, compared to the conventional coolant fluid method. This research was conducted in a Super MC 500 high-speed CNC milling machine with a Hitachi coated carbide 20mm in diameter. High-speed cutting parameters such as cutting speed V, cutting depth t, and spindle speed S were considered as variants. The experiment was designed based on Taguchi's L9. Surface quality, including Ra and Rq, was measured using the Mitutoyo Surftest SV-210. A mathematical regression model was found for the average values of surface roughness through regression analysis for dry and coolant fluid conditions. The chosen high-speed milling parameters and the respective Ra and Rq values were obtained by ANOVA. The grey relation scores for wet and dry milling surface quality for cut depth, feed rate, and cutting speed were 0.7527, 0.7869, 0.6302, and 0.8167, 0.7199, 0.6040, respectively. The results showed that the feed rate had the greatest influence on the surface quality during the high-speed coolant milling of hardened steel, while the depth of the cut had the greatest influence on the surface quality during the high-speed dry milling process. Keywords-SKD61; FGRA; surface roughness; high-speed milling


INTRODUCTION
JIS SKD61 (or AISI H13) alloy steel is a hot work mold steel, popular for its remarkable toughness, sufficient resistance to high-temperature fatigue, and high rigidity. In general, the manufacturing processes of extrusion die sets, hot forging, and casting dies include roughing, heat treatment, and machine finishing. This process is costly and time-consuming. The increasing pressure in the global economy forces the manufacturers to increase machining productivity while maintaining or improving product quality. The improvement of the cutting productivity and quality of high-speed cutting processes have attracted the attention of many companies and researchers in recent decades.
The coolant fluid is essential when milling hardened steel or other difficult-to-machine materials, especially when machining at high speeds. The main effect of a coolant in milling is to reduce temperature and friction, prolong the life of the cutting tool, and improve the machining efficiency and surface quality [1][2][3]. Moreover, a coolant also has anticorrosion and cleaning effects [4,5]. Nowadays, with the continuous development of precision and ultra-precision machining, machined surface integrity is more important than cutting efficiency and tool life, especially for important components such as mold parts working in harsh environments. However, the effect of the coolant fluid on machined surface quality and its performance in the high-speed machining of hardened steels has not been given attention and compared with dry milling under the same machining conditions. The cost of the coolant fluid can increase the total production cost by up to 15% [6]. Dry, near-dry, and MQL machining processes are viable alternatives to reduce this cost. Dry machining is now considered an excellent solution to minimize the negative impacts on human health and the environment. Therefore, dry or near-dry machining has attracted considerable interest among manufacturers and researchers [7]. Dry milling can offer significant advantages such as reducing pollutants in the air, water, or the earth, having less harmful effects on human health [20], and a significant cost reduction as there is no need to invest in cooling equipment, maintenance, operation, and handling coolant after machining [8,9]. Dry milling processes can be applied to a wide range of materials, such as cast iron, carbon steel, alloy steel, and difficult-to-machine steels [10,11]. However, in dry milling conditions, due to the large friction between the tool and the workpiece, the cutting temperature increases and causes increased wear of the cutting tool. Build of edge also appears more frequently [9], adversely affecting the quality of the machined surface. Although the further improvement of surface quality and the reduction of tool wear during dry milling has attracted the interest of many researchers, most of them focused on measures to offset the positive effects of lubricants through the optimal selection of cutting parameters, changing the geometry of the cutting tool, choosing the right tool material, improving the machine, etc. However, these studies have not mentioned or compared the main factors that affect surface quality between the wet and dry milling process.
This study examined the factors influencing the dry and wet high-speed milling of hardened steel on surface quality under the same machining conditions. Fuzzy Grey Relationship Analysis (FGRA) was applied to determine the influence of each factor on surface quality in the two different milling environments. Based on the experimental results obtained by the Taguchi L9 method, the FGRA processes were used to evaluate the main factors affecting surface quality. The dry and wet milling comparison provides excellent reference values to be applied to the machining processes and further improve the surface quality of high-speed milling of hardened steel.

A. The Workpiece
The SKD61 hard steel, used as the workpiece specimen, was prepared in square blocks of 70×40×15mm dimensions. The chemical composition and mechanical properties of JIS SKD61 mold steel are demonstrated in Tables I and II.

B. Machine and Cutting Tool
An MC50 high-speed machine, made by Foxconn Corporation and a Hitachi cutting tool with coated inserts were used for the experiment process ( Figure 1).

C. Measurement Equipment
The surface roughness value for all experiments was determined as the average of three measurements at separate locations in the workpiece specimens using a Mitutoyo Surftest SV-210. These average values are shown in Table III. III. APPLYING GREY FUZZY RELATIONSHIP ANALYSIS GRA is an effective statistical method to measure the approximation between objects using the grey relational level. The model has been successfully applied in many different fields [12][13][14]. The level of information in a GRA assists decision-making in difficult situations [15,16]. An improved version of this method is the Fuzzy Grey Relationship Analysis (FGRA). This is an approach based on a fuzzy system developed on coefficients and grey relation levels. This model considers all the different criteria, including their importance and the uncertainty about the weights. In this work, FGRA was used to evaluate the effects of various factors from the cutting mode parameters on the machined surface quality in both dry and wet milling environments. The steps of the algorithm are described as follows: • Step 1: When applying FGRA to determine the factors affecting surface quality in wet and dry milling, the reference matrix Y is established based on the surface roughness parameters obtained. Y(1), Y(2),..., Y(n) correspond to the surface roughness values obtained by the experiments 1, 2, ..., n. The reference matrix with the surface quality in high-speed milling is described by: The factors investigated in the high-speed milling process include inputs such as cutting speed, feed, wet milling, dry milling, etc. in the experiments. If the number of the investigated factors is m and the number of experiments is n, the comparison matrix is: • Step 2: Nondimensionalize original sequences. As the investigated factors (surface roughness) and the reference variables (depth of cut t, feed rate S, cutting speed V) have different dimensions, they must be nondimensionalized by [17]: Step 3: Calculate the cosine value of the fuzzy membership. In this study, the included-angle cosine method was adopted, which is not affected by the linear proportional relationship of the data.
This variant must be suitable for working conditions and resist any noise signal, as the values of dispersion coefficients may not accurately reflect the relationship between the investigated variants. The dispersion coefficient was calculated by [14]: where ∆ is the absolute error's mean. As ‫ܥ‬ ൌ The grey Euclidean R2 levels were calculated using [17]: • Step 6: Determine the fuzzy grey points. According to the fuzzy association coefficient, the grey relationship's level and grey relationship with the fuzzy correlation coefficient of the research variants were calculated by: • Step 7: Ranking. Based on the magnitude of the grey relational grade, the impact of the investigated factors was ranked.
IV. EXPERIMENTAL PROCEDURE The experiment was designed based on the Taguchi orthogonal array [19] to reduce the number of experiments. Two responses, R a and R q , were analyzed simultaneously in both dry and coolant fluid environments. Cutting speed V, spindle speed S, and depth of cut t were chosen. Table III shows the high-speed milling parameters and results. Figures 2 and 3 depict a quality comparison of the workpiece surface in wet and dry milling, according to R a and R q , respectively. The results show that the surface quality in dry is lower than in coolant fluid milling with the same cutting parameters.  The ANOVA results of the surface quality in wet milling conditions are shown in Figure 4. The results show that the smallest S/N ratios were in level 3 for all variables. Feed rate S had the smallest S/N ratio (0.31), followed by cutting speed (0.99) and depth of cut (2.15). The ANOVA results in dry milling conditions are shown in Figure 5. The results show that feed rate S and cutting speed V had the smallest S/N ratios in level 3 (-3.36, -2.92, respectively). Depth of cut t had its smallest S/N ratio in level 1 (-0.78). Therefore, there is a significant difference in the ANOVA results between the wet and dry milling processes. The FGRA was applied to determine the main factors affecting the surface quality through the following steps: • Step 1: The surface quality of different machining conditions had the Y(x) and Z(y) reference arrays for wet and dry milling, respectively. The depth of cut, feed rate, and cutting speed were Y1(x), Y2(x), Y3(x), and Z1(x), Z2(x), Z3(x) for conventional and dry milling, respectively. The reference and the comparison matrixes are shown in (11) and (12). 1  1  1  2  2  2  3  3  3  1  2  3  1  2  3  1  2  3  1  2  3  2  3  1  3  1 1  1  1  2  2  2  3  3  3  1  2  3  1  2  3  1  2  3  1  2  3  2  3  1  3   • Step 2: The angular cosine of the fuzzy membership function was obtained by (4). Figures 6 and 7 depict the fuzzy membership classes of four surface quality parameters in wet and dry environments. The results showed that in the wet machining process, the change of feed rate was the highest (0.7705), followed by the depth of cut (0.7525) and cutting speed (0. 5413). The analysis results in dry high-speed milling depicted a clear difference, as the depth of cut was kept at the most considerable value (0.8468), followed by the feed rate (0.6748) and cutting speed (0.487). Hence, in comparison with wet milling, the feed rate dramatically influenced the surface quality. Cutting speed had an almost negligible impact on the surface quality, just as in wet milling. Meanwhile, in dry milling, the depth of cut had the most influence on surface quality.

V. RESULTS AND DISCUSSION
• Step 3: The grey Euclidean relationship levels were calculated using (5) - (8) and are depicted in Figures 6 and  7. The Euclidean grey equivalent ratios of different elements were not much for the wet milling process. But it was still found that the feed rate most influenced the surface quality in wet milling. In dry milling, the depth of cut had the most effect on surface quality.  Step 4: The fuzzy grey relationship scores of the input variants were obtained by (12) and are illustrated in Figures  6 and 7. The results showed a comprehensive way to evaluate the impact of experimental variants on the surface quality in both convenient and dry milling conditions. Therefore, the matt grey related scores for wet and dry milling surface quality of the three factors depth of cut t, feed rate S, and cutting speed v were 0.7527, 0.7869, 0.6302, and 0.8167, 0.7199, 0.6040, respectively.
Hence, the influence of feed rate on the surface quality in the high-speed machining of a difficult-to-cut material in wet milling is the most. By contrast, the depth of cut has the most VI. CONCLUSION This study investigated the high-speed cutting parameters optimization of milling processes in dry and coolant fluid conditions to ensure a satisfactory surface quality. FGRA was obtained to analyze the relation among the surface roughness parameters R a , R q , and the cutting parameters cutting speed v, spindle speed S, and depth of cut t. ANOVA was used to determine the model's adequacy and factor significance. The main results can be summarized as: • The surface quality in high-speed milling of mold steel JIS SKD61 in dry conditions is lower than in coolant fluid conditions with the same cutting parameters.
• The effects of different cutting parameters on the surface roughness in dry and coolant fluid environments differ. Cutting speed v had the smallest S/N value at 0.31 and 0.21, respectively. By contrast, the S/N value of the depth of cut t was highest, at 2.51 and -0.78, respectively, followed by the S/N of spindle speed S.
• The FGRA confirmed that the influence of spindle speed was the most in the coolant fluid high-speed milling of hardened steel. By contrast, the effect of depth of cut on the surface roughness in the dry machining was the most significant. At the same time, both dry and wet conditions showed that the influence of cutting speed was insignificant.