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Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting Yakub Iqbal Mogul a,⇑, Irfan Nasir b, Dr. Peter Myler c a b c
School of Engineering and Computing, University of Bolton, RAK Academic Centre, United Arab Emirates Mechanical Engineering, University of Bolton, RAK Academic Centre, United Arab Emirates School of Engineering, University of Bolton, UK
a r t i c l e
i n f o
Article history: Received 11 September 2019 Received in revised form 17 October 2019 Accepted 23 December 2019 Available online xxxx Keywords: Control depth milling (CDM) Abrasive water jet cutting (AWJC) Titanium Ti6AL4V Grey relational analysis (GRA) Roughness
a b s t r a c t Abrasive water jet (AWJ) cutting has been widely used in industries because of its precise cutting technique. However, the effectiveness of AWJ cutting is dependent on machine operating parameters and the material properties. In this research AWJ cutting was applied on Titanium Ti6AL4V Grade 5 material to investigate the effects on depth of cut (DoC) and roughness. Taguchi L27 experiments were conducted at three levels with operating parameters such as water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size; a novel approach of ratio 3:1 was adopted with nozzle and orifice diameter to accommodate more parameters for investigation and to inspect the variation of these parameters in real time. Minitab 2017 software was used to simulate the influencing parameters with ANOVA. (GRA) Grey relational analysis linked with Taguchi technique represents a novel approach to optimization. It is a normalization estimation technique extended to elucidate the complex multi-performance characteristics. GRA is used for optimizing the process parameter which helped in determining the optimal parameters for roughness and depth of cut, ANOVA was used for analyzing the effect of independent variables on dependent variables, as per ANOVA the P values and the S/N ration rank indicated that water pressure was the most influencing parameter for surface roughness and transverse speed was the most influencing parameter for the depth of cut. The prediction of cutting depth through GRA optimization proved a valuable tool for the controlled depth milling (CDM) in Ti6Al4V material. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Recent Advances in Materials & Manufacturing Technologies.
1. Introduction Manufacturing industries are growing rapidly and becoming one of the most important factors in global economy for growth. In current era of industry 4.0 for smart manufacturing [18–20], various innovative machining processes have been introduced under the category of non-conventional machining providing improved superiority machining while being competitively costeffective and efficient, including those based on plasma, EDM, abrasive water jets (AWJ), lasers etc. AWJ machine is primarily used to cut materials through and that are hard to cut by conventional machining methods. These methods can be used to cut a
wide range of materials extending from soft materials like aluminium, and hard materials like Inconel, titanium, Kevlar, composites etc. [1] It is also used for (CDM) controlled depth milling of materials. Hard to cut materials like titanium, kelvar and composites require a high precision and good knowledge of controlled parameters of the machine because of the cost involved in the material cutting, very high accuracy is required and when it comes to control the depth of cut for these materials it becomes a challenge for the abrasive water jet machine to control the nonlinear parameters, very less research is available on the depth of cut for Titanium 6AL4V Grade 5 material and milling being an expensive and time consuming process in AWJ, there is a need to investigate the parameters which are influencing the depth of cut and roughness.
⇑ Corresponding author. E-mail address: [email protected] (Y.I. Mogul). https://doi.org/10.1016/j.matpr.2019.12.229 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Recent Advances in Materials & Manufacturing Technologies.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Y.I. Mogul et al. / Materials Today: Proceedings xxx (xxxx) xxx
making [12] on titanium were investigated and surface roughness and quality of hole and time of drilling were investigated showing a decrease in surface roughness by 25%. Predicting the optimal parameters for a depth of cut and roughness in any material with AWJ cutting is a complex process and a challenge. As the number of levels and parameters are increased for investigation the cost of experiments goes high and if the testing materials are expensive it becomes a real challenge to select the input parameters for investigation. Design of experiments using Taguchi method makes the life of the researcher bit easier [13,14]. Using Grey relational analysis (GRA) as an optimization technique and a part of grey system theory proposed by Deng [16] can solve problems with intricate interrelationships between several factors and variables [15]. The proposed Taguchi-GRA method has been used for optimization in many applications in wire EDM, process parameter for submerged arc welding and manufacturing. Very less research is seen with Titanium 6AL4V grade 5 materials for depth of cut with surface roughness and considering this fact the current research attempts to investigate a novel approach with Taguchi-GRA method involving six parameters with two parameters as a ratio and variation with 3 levels for obtaining the optimal parameters.
Fig. 1. Influencing process parameters in AWJ cutting.
2. Methodology In order to investigate the depth of cut and surface roughness on Titanium Ti-6AL4V, current study considered water pressure, transverse speed, abrasive flow rate, abrasive orifice size, nozzle/orifice diameter ratio varying at low, medium and high level considering the limitation of the machine operating parameters, understanding the fact that orifice diameter and nozzle diameter wear with respect to machine operating hours both the parameters were taken into consideration with a ratio of 3:1 considering the variation in their diameters, this technique tallows more parameters to be varied during the experiment phase.
Fig. 2. Taguchi-GRA method flow chart.
Table 1 Design of experiment parameters and levels. Factors
Level 1-Low
Level 2-Medium
Level 3-High
Water pressure, bar Transverse speed, mm/min Abrasive mass flow rate, kg/min Abrasive orifice size, mm Nozzle diameter ; mm Orifice diameter; mm (Ratio near 3:1)
2400 80 0.160 3.8 1:2 = 2.85 0:25
2900 100 0.180 4.0 0:77 = 3.04 0:35
3400 150 0.215 4.2 1:0 = 3.33 0:30
2.1. Phase I – planning Titanium Ti-6AL4V Grade 5 material 600 200 10 mm was considered for the experiment, for the design of experiment at
Constant parameters: Stand of distance, SOD = 3 mm, Abrasive material = garnet 80 mesh, Angle of cutting = 90 deg, Number of passes = 1.
AWJ cutting is swayed by numerous process parameters such as water pressure, abrasives, target work material and cutting parameters as shown in Fig. 1. Among them, certain parameters such as abrasive flow rate, jet traverse speed, water pressure and standoff distance are accurately controllable. Parameters such as the nozzle diameter and orifice diameter changes constantly due to wear and thus are uncontrollable [1–3]. Fig. 1 shows the influencing process parameters in AWJ cutting in which water jet orifice diameter and focusing nozzle bore diameter are nonlinear in their dimension during the cutting process [4]. Various studies on the depth of model [6,7] was carried out using nozzle oscillation techniques and dimensional technique to understand the effects of parameters on the depth of cut, it was establish that water pressure has the greatest effect on the depth of cut. New techniques involving modelling of incision profiles with rapid calibration and elementary passes [8–10] were studies to understand the depth of cut, but these techniques require lot of trials and require sophisticated measuring instruments which add to the cost of investigation at an initial stage itself. Studies on hole drilling [11] and deep hole
Fig. 3. Rectangular blocks through cut for roughness.
Fig. 4. Slot cut for depth analysis.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Y.I. Mogul et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 2 Design of experiment, Taguchi L27 Experiments with measured output for roughness and depth. Taguchi-L27 Experiment
Measured Output
SR No
Water Pressure (Bar)
Transverse speed (mm/ min)
Nozzle Diameter and orifice ratio (mm)
Abrasive flow rate (kg/ min)
Abrasive orifice size (mm)
Roughness (lm)
Depth (mm)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
2400 2400 2400 2400 2400 2400 2400 2400 2400 2900 2900 2900 2900 2900 2900 2900 2900 2900 3400 3400 3400 3400 3400 3400 3400 3400 3400
80 80 80 100 100 100 150 150 150 80 80 80 100 100 100 150 150 150 80 80 80 100 100 100 150 150 150
2.85 2.85 2.85 3.04 3.04 3.04 3.33 3.33 3.33 3.04 3.04 3.04 3.33 3.33 3.33 2.85 2.85 2.85 3.33 3.33 3.33 2.85 2.85 2.85 3.04 3.04 3.04
0.16 0.16 0.16 0.18 0.18 0.18 0.215 0.215 0.215 0.215 0.215 0.215 0.16 0.16 0.16 0.18 0.18 0.18 0.18 0.18 0.18 0.215 0.215 0.215 0.16 0.16 0.16
3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2
4.33 3.94 3.837 5.72 3.9 4.727 4.784 4.891 4.998 4.723 3.547 3.643 4.3 4.62 4.2 4.183 4.269 4.292 3.427 3.73 3.42 4.307 3.863 3.867 4.637 3.527 3.567
24 14 31 9 21 8 20 17 28 21 12 27 9 18 8 19 20 21 35 22 31 12 21 8 17 9 20
Fig. 6. Mitutoyo Surf test SJ-201P portable surface roughness tester.
Fig. 5. Experimental setup.
three levels and six factors were chosen with one as a combination of a ratio to accommodate more varying parameters for the investigation as shown in Table 1. Minitab 17 was chosen for the statistical analysis, Taguchi design of experiment with L27 half factorial was considered, 27 experiments were performed on the AWJ machine. In the initial experiments 27 rectangular blocks were through cut as shown in Fig. 3 to understand the roughness and the influencing parameters, More 27 experiments were conducted for slot cutting with the same set of parameters along the thickness to investigate the depth of cut as shown in the Fig. 4.
Fig. 7. Depth measurement with Vernier Caliper.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Table 3 Grey Relational Analysis Results (GRA). Input
Step 1: Normalization Data
Step 2: Deviation Sequence
Step 3: Grey Relational Coefficient
Step 4: Grey Relational Grade and Rank
Exp. No
Ra (mm)
Depth, m
Ra (mm)
Depth, m
Ra (mm)
Depth, m
Ra (mm)
Depth, m
GRG
Rank
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
4.33 3.94 3.837 5.72 3.9 4.727 4.784 4.891 4.998 4.723 3.547 3.643 4.3 4.62 4.2 4.183 4.269 4.292 3.427 3.73 3.42 4.307 3.863 3.867 4.637 3.527 3.567
0.024 0.014 0.031 0.009 0.021 0.008 0.02 0.017 0.028 0.021 0.012 0.027 0.009 0.018 0.008 0.019 0.01 0.021 0.035 0.022 0.031 0.012 0.021 0.008 0.017 0.009 0.02
0.604 0.774 0.819 0.000 0.791 0.432 0.407 0.360 0.314 0.433 0.945 0.903 0.617 0.478 0.661 0.668 0.631 0.621 0.997 0.865 1.000 0.614 0.807 0.806 0.471 0.953 0.936
0.593 0.222 0.852 0.037 0.481 0.000 0.444 0.333 0.741 0.481 0.148 0.704 0.037 0.370 0.000 0.407 0.074 0.481 1.000 0.519 0.852 0.148 0.481 0.000 0.333 0.037 0.444
0.396 0.226 0.181 1.000 0.209 0.568 0.593 0.640 0.686 0.567 0.055 0.097 0.383 0.522 0.339 0.332 0.369 0.379 0.003 0.135 0.000 0.386 0.193 0.194 0.529 0.047 0.064
0.407 0.778 0.148 0.963 0.519 1.000 0.556 0.667 0.259 0.519 0.852 0.296 0.963 0.630 1.000 0.593 0.926 0.519 0.000 0.481 0.148 0.852 0.519 1.000 0.667 0.963 0.556
0.558 0.689 0.734 0.333 0.706 0.468 0.457 0.439 0.422 0.469 0.901 0.838 0.567 0.489 0.596 0.601 0.575 0.569 0.994 0.788 1.000 0.565 0.722 0.720 0.486 0.915 0.887
0.551 0.391 0.771 0.342 0.491 0.333 0.474 0.429 0.659 0.491 0.370 0.628 0.342 0.443 0.333 0.458 0.351 0.491 1.000 0.509 0.771 0.370 0.491 0.333 0.429 0.342 0.474
0.555 0.540 0.753 0.338 0.598 0.401 0.466 0.434 0.540 0.480 0.635 0.733 0.454 0.466 0.465 0.529 0.463 0.530 0.997 0.649 0.886 0.467 0.606 0.527 0.457 0.628 0.680
11 13 3 27 10 26 20 25 12 17 7 4 24 19 21 15 22 14 1 6 2 18 9 16 23 8 5
Table 4 Optimal parameters obtained from GRA technique.
3. Experimental plan
Parameters
Values
Water pressure Transverse speed Orifice size Nozzle size Abrasive flow rate Abrasive orifice size Roughness Depth
3400 bar 80 mm/min 0.30 mm 1.0 mm 0.18 kg/min 3.8 mm 3.427 mm 35 mm
2.2. Phase II – measurement Roughness measurement was done with the Surf test SJ-201P Roughness tester as shown in Fig. 6. For the slot cut depth measurement was done with a Vernier caliper. Table 2 illustrates the L27 orthogonal array of the performed experiments along with the measured value of roughness and depth of cut 2.3. Phase III – optimization with grey relational analysis (GRA) (GRA) Grey Relational Analysis combined with Taguchi method represents a novel approach for optimization. It is a normalization estimation technique extended to elucidate the complex multi-performance characteristics. GRA is used in combination with orthogonal array to draw implications about the effect of the factor and their interaction on multiple responses. Grey relational analysis is a type of method is used for optimizing the process parameter and helps in determining the optimal parameters. Fig. 2 represents the methodology for Taguchi-GRA method adopted.
AWJ machine for the experiment shown in Fig. 5 can produce a pressure of 4200 bar with a rated discharge of 3.6 ltr/min with pump capacity 50 hp. The constant parameters were chosen as stand of distance 3 mm, angle of cut 90 deg with single pass, the abrasive material used was garnet with mesh size 80 for Titanium 6AL4V material for cutting, typically garnet signifies the highest running cost on a AWJ machine, in order to minimize the use of garnet reduction of orifice to focusing tube ratios are important [5], for this three abrasive orifice sizes of 3.8 mm, 4 mm, 4.2 mm is incorporated to understand the effect as shown in the Table 1. Understanding the fact that orifice diameter and nozzle diameter wear with respect to machine hours both the parameters were taken into consideration with a ratio of 3:1 considering the variation in their diameters [17]. Surf test SJ-201P portable surface roughness tester manufactured by Mitutoyo was used to determine the average surface roughness (Ra) on the cut material, Vernier caliper was used to measure the slot depth along the thickness of the material. Roughness measurements were taken along the length and breadth as sown in Figs. 6 and 7 with their mean values. Designs of Experiments (DOE) were developed with Minitab 17 along with Taguchi L27 orthogonal array provided in Table 2 with the measured values of roughness and depth for the 27 experiments. For optimization grey relational analysis (GRA) was performed, ANOVA was implemented for investigating the effect of different parameters on roughness and depth. Steps for Grey relational analysis GRA are shown below:
Step Step Step Step
1: 2: 3: 4:
Normalize the value of roughness and depth Calculate deviation sequence Calculate Grey relational coefficient Grey relational grade and ranking.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Fig. 8. a: ANOVA results from Minitab 17 software for depth of cut (DoC). b: S/N ratio results from Minitab 17 software for depth of cut (DoC).
Table 3 shows the detail of the above four steps for the grey relational analysis (GRA). Optimal parameters are obtained from the Table 3 where rank 1 correspond to experiment no 19 which is highlighted for the optimal parameters which shows lowest roughness and highest depth, the optimal parameters are indicated below (Table 4). 4. Results and discussion The Analysis of Variances (ANOVA) examines the implication of the control factors and assesses the degree of their effects on the response features by using Minitab as statistical modelling software. Values of ‘‘Probability (P) > F” less than 0.05 indicate the terms are significant with a confidence level of 95%.
4.2. Surface roughness, (Ra) analysis It is observed from Fig. 10a that water pressure has the highest effect on the surface roughness (P value = 0.003) followed by transverse speed (P value = 0.014) and it can be verified from Fig. 10b that rank 1 and 2 is associated with water pressure and transverse speed. Table 3 from experiment 19 indicates the highest roughness value as 3.425 mm. From the main effect plot analysis for roughness the parameters that are influencing most from highest to the lowest are water pressure, transverse speed, nozzle to orifice diameter ratio, abrasive orifice size and abrasive mass flow rate. The results verified that an improvement of the surface roughness can be achieved by increasing the water pressure at 3400 bar and low traverse speed at 80 mm/min (Fig. 11).
4.1. Depth of cut (DoC) analysis Fig. 8a indicate the responses for the depth of cut using Minitab 2017, As per ANOVA for S/N ratio it can be concluded that depth of cut is influenced by transverse speed (P value = 0.014), followed by abrasive mass flow rate (P value = 0.481), it can also be verified from the Fig. 8b that the highest rank 1 is for transverse speed followed by rank 2 for abrasive mass flow rate. From the main effect plot Fig. 9, the influencing parameters from highest to the lowest are transverse speed, abrasive mass flow rate, nozzle to orifice diameter ratio, abrasive orifice size and water pressure and is achieved from experiment 19 from Table 3 which confirms the highest depth of cut with GRA grade and rank 1. The general phenomena is that as the transverse speed increases the depth of cut decreases due to the reason the impingement of the abrasive will have very less time to erode the material at that particular instant, However, the interaction between abrasive rate and feed rate is important; to achieve a high depth of cut with lower feed rates higher abrasive rates are needed.
Fig. 9. Main effects plot for S/N ratio for depth of cut (DoC).
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Fig. 10. a: ANOVA results from Minitab 17 software for roughness. b: S/N ratio results from Minitab 17 software for roughness.
experiments with AWJ machine and add to cost for the researcher, variation considered in the nozzle and orifice diameter with a ratio of 3:1 showed a good study to evaluate for real time changes in these parameters. GRA optimization was used to convert a multiresponse problem into a single-response problem. It was seen from the simulation that the most influencing parameters for the depth of cut was transverse speed and for surface roughness is water pressure.
6. Future scope of work
Fig. 11. Main effects plot for S/N ratio for roughness.
5. Conclusion The investigation of this research proposed a combination of GRA with Taguchi based design of experiments (DOE) for depth of cut and roughness for Titanium 6AL4V Grade 5 materials which is extensively used in many applications. The current research successfully incorporated more parameters in the design of experiments (DOE) which if taken individually will lead to more
AWJ cutting being a complicated process and selecting the optimal parameters is a big challenge, under the current scenario if we have a look at the 3 levels of all parameters the possibility of levels between the 2 numbers are very high that means if any value of any parameters are little bit changed the researcher need to again perform experiments and it add to cost every time any parameters are changed for investigation. For the future work researcher can try to solve the above problem where any change in any parameters can be deal with some sort of simulation method and did not further require any more experiments to be performed, now a day’s artificial intelligence (AI) with deep learning techniques (Neural Nets) can be used with the help of high end software’s like Matlab and Python and applying various algorithms to get the optimized parameters.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References [1] M. Hashish, Wear in abrasive water jet cutting systems, in: International Conference on Wear of Materials, Houston, Texas, 5–9 April (1987), pp. 769– 776. [2] J. John Rozario Jegaraj, N. Ramesh Babu, A strategy for efficient and quality cutting of material with abrasive water jets considering the variation in orifice and focusing nozzle diameter, Int. J. Mach. Tools Manuf. 45 (12–13) (2005) 1443–1450. [3] J. John Rozario Jegaraj, N. Ramesh Babu, A soft computing approach for controlling the quality of cut with abrasive waterjet cutting system experiencing orifice and focusing tube wear, J. Mater. Process. Technol. 185 (1–3) (2007) 217–227. [4] M. Hashish, Waterjet machine tool of the future, Proceedings of Ninth American Waterjet Conference, Dearborn, 1997, pp. 769–778. [5] TECHNI Waterjet, How it Works | Water Jet Cutting | Water Jet Cutter – TECHNI Waterjet. [online] Available at: https://www.techniwaterjet.com/how-itworks/, 2019 (accessed 8 Sep. 2019). [6] J. Wang, Predictive depth of jet penetration models for abrasive water jet cutting of alumina ceramics, Int. J. Mech. Sci. 49 (3) (2007) 306–316. [7] M. Chithirai Pon Selvan, Selection of process parameters in abrasive water jet cutting of titanium, 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET’2014), London, UK, May 30-31, 2014. [8] T. Sultan, P. Gilles, G. Cohen, F. Cenac, W. Rubio, Modeling incision profile in AWJM of Titanium alloys Ti6Al4V, Mechan. Industry 17 (4) (2016) 403. [9] V.H. Bui, P. Gilles, G. Cohen, W. Rubio, A modeling of elementary passes taking into account the firing angle in abrasive water jet machining of titanium alloy,
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Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting Yakub Iqbal Mogul a,⇑, Irfan Nasir b, Dr. Peter Myler c a b c
School of Engineering and Computing, University of Bolton, RAK Academic Centre, United Arab Emirates Mechanical Engineering, University of Bolton, RAK Academic Centre, United Arab Emirates School of Engineering, University of Bolton, UK
a r t i c l e
i n f o
Article history: Received 11 September 2019 Received in revised form 17 October 2019 Accepted 23 December 2019 Available online xxxx Keywords: Control depth milling (CDM) Abrasive water jet cutting (AWJC) Titanium Ti6AL4V Grey relational analysis (GRA) Roughness
a b s t r a c t Abrasive water jet (AWJ) cutting has been widely used in industries because of its precise cutting technique. However, the effectiveness of AWJ cutting is dependent on machine operating parameters and the material properties. In this research AWJ cutting was applied on Titanium Ti6AL4V Grade 5 material to investigate the effects on depth of cut (DoC) and roughness. Taguchi L27 experiments were conducted at three levels with operating parameters such as water pressure, transverse speed, abrasive mass flow rate, abrasive orifice size; a novel approach of ratio 3:1 was adopted with nozzle and orifice diameter to accommodate more parameters for investigation and to inspect the variation of these parameters in real time. Minitab 2017 software was used to simulate the influencing parameters with ANOVA. (GRA) Grey relational analysis linked with Taguchi technique represents a novel approach to optimization. It is a normalization estimation technique extended to elucidate the complex multi-performance characteristics. GRA is used for optimizing the process parameter which helped in determining the optimal parameters for roughness and depth of cut, ANOVA was used for analyzing the effect of independent variables on dependent variables, as per ANOVA the P values and the S/N ration rank indicated that water pressure was the most influencing parameter for surface roughness and transverse speed was the most influencing parameter for the depth of cut. The prediction of cutting depth through GRA optimization proved a valuable tool for the controlled depth milling (CDM) in Ti6Al4V material. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Recent Advances in Materials & Manufacturing Technologies.
1. Introduction Manufacturing industries are growing rapidly and becoming one of the most important factors in global economy for growth. In current era of industry 4.0 for smart manufacturing [18–20], various innovative machining processes have been introduced under the category of non-conventional machining providing improved superiority machining while being competitively costeffective and efficient, including those based on plasma, EDM, abrasive water jets (AWJ), lasers etc. AWJ machine is primarily used to cut materials through and that are hard to cut by conventional machining methods. These methods can be used to cut a
wide range of materials extending from soft materials like aluminium, and hard materials like Inconel, titanium, Kevlar, composites etc. [1] It is also used for (CDM) controlled depth milling of materials. Hard to cut materials like titanium, kelvar and composites require a high precision and good knowledge of controlled parameters of the machine because of the cost involved in the material cutting, very high accuracy is required and when it comes to control the depth of cut for these materials it becomes a challenge for the abrasive water jet machine to control the nonlinear parameters, very less research is available on the depth of cut for Titanium 6AL4V Grade 5 material and milling being an expensive and time consuming process in AWJ, there is a need to investigate the parameters which are influencing the depth of cut and roughness.
⇑ Corresponding author. E-mail address: [email protected] (Y.I. Mogul). https://doi.org/10.1016/j.matpr.2019.12.229 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 2nd International Conference on Recent Advances in Materials & Manufacturing Technologies.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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making [12] on titanium were investigated and surface roughness and quality of hole and time of drilling were investigated showing a decrease in surface roughness by 25%. Predicting the optimal parameters for a depth of cut and roughness in any material with AWJ cutting is a complex process and a challenge. As the number of levels and parameters are increased for investigation the cost of experiments goes high and if the testing materials are expensive it becomes a real challenge to select the input parameters for investigation. Design of experiments using Taguchi method makes the life of the researcher bit easier [13,14]. Using Grey relational analysis (GRA) as an optimization technique and a part of grey system theory proposed by Deng [16] can solve problems with intricate interrelationships between several factors and variables [15]. The proposed Taguchi-GRA method has been used for optimization in many applications in wire EDM, process parameter for submerged arc welding and manufacturing. Very less research is seen with Titanium 6AL4V grade 5 materials for depth of cut with surface roughness and considering this fact the current research attempts to investigate a novel approach with Taguchi-GRA method involving six parameters with two parameters as a ratio and variation with 3 levels for obtaining the optimal parameters.
Fig. 1. Influencing process parameters in AWJ cutting.
2. Methodology In order to investigate the depth of cut and surface roughness on Titanium Ti-6AL4V, current study considered water pressure, transverse speed, abrasive flow rate, abrasive orifice size, nozzle/orifice diameter ratio varying at low, medium and high level considering the limitation of the machine operating parameters, understanding the fact that orifice diameter and nozzle diameter wear with respect to machine operating hours both the parameters were taken into consideration with a ratio of 3:1 considering the variation in their diameters, this technique tallows more parameters to be varied during the experiment phase.
Fig. 2. Taguchi-GRA method flow chart.
Table 1 Design of experiment parameters and levels. Factors
Level 1-Low
Level 2-Medium
Level 3-High
Water pressure, bar Transverse speed, mm/min Abrasive mass flow rate, kg/min Abrasive orifice size, mm Nozzle diameter ; mm Orifice diameter; mm (Ratio near 3:1)
2400 80 0.160 3.8 1:2 = 2.85 0:25
2900 100 0.180 4.0 0:77 = 3.04 0:35
3400 150 0.215 4.2 1:0 = 3.33 0:30
2.1. Phase I – planning Titanium Ti-6AL4V Grade 5 material 600 200 10 mm was considered for the experiment, for the design of experiment at
Constant parameters: Stand of distance, SOD = 3 mm, Abrasive material = garnet 80 mesh, Angle of cutting = 90 deg, Number of passes = 1.
AWJ cutting is swayed by numerous process parameters such as water pressure, abrasives, target work material and cutting parameters as shown in Fig. 1. Among them, certain parameters such as abrasive flow rate, jet traverse speed, water pressure and standoff distance are accurately controllable. Parameters such as the nozzle diameter and orifice diameter changes constantly due to wear and thus are uncontrollable [1–3]. Fig. 1 shows the influencing process parameters in AWJ cutting in which water jet orifice diameter and focusing nozzle bore diameter are nonlinear in their dimension during the cutting process [4]. Various studies on the depth of model [6,7] was carried out using nozzle oscillation techniques and dimensional technique to understand the effects of parameters on the depth of cut, it was establish that water pressure has the greatest effect on the depth of cut. New techniques involving modelling of incision profiles with rapid calibration and elementary passes [8–10] were studies to understand the depth of cut, but these techniques require lot of trials and require sophisticated measuring instruments which add to the cost of investigation at an initial stage itself. Studies on hole drilling [11] and deep hole
Fig. 3. Rectangular blocks through cut for roughness.
Fig. 4. Slot cut for depth analysis.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Y.I. Mogul et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 2 Design of experiment, Taguchi L27 Experiments with measured output for roughness and depth. Taguchi-L27 Experiment
Measured Output
SR No
Water Pressure (Bar)
Transverse speed (mm/ min)
Nozzle Diameter and orifice ratio (mm)
Abrasive flow rate (kg/ min)
Abrasive orifice size (mm)
Roughness (lm)
Depth (mm)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
2400 2400 2400 2400 2400 2400 2400 2400 2400 2900 2900 2900 2900 2900 2900 2900 2900 2900 3400 3400 3400 3400 3400 3400 3400 3400 3400
80 80 80 100 100 100 150 150 150 80 80 80 100 100 100 150 150 150 80 80 80 100 100 100 150 150 150
2.85 2.85 2.85 3.04 3.04 3.04 3.33 3.33 3.33 3.04 3.04 3.04 3.33 3.33 3.33 2.85 2.85 2.85 3.33 3.33 3.33 2.85 2.85 2.85 3.04 3.04 3.04
0.16 0.16 0.16 0.18 0.18 0.18 0.215 0.215 0.215 0.215 0.215 0.215 0.16 0.16 0.16 0.18 0.18 0.18 0.18 0.18 0.18 0.215 0.215 0.215 0.16 0.16 0.16
3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2 3.8 4 4.2
4.33 3.94 3.837 5.72 3.9 4.727 4.784 4.891 4.998 4.723 3.547 3.643 4.3 4.62 4.2 4.183 4.269 4.292 3.427 3.73 3.42 4.307 3.863 3.867 4.637 3.527 3.567
24 14 31 9 21 8 20 17 28 21 12 27 9 18 8 19 20 21 35 22 31 12 21 8 17 9 20
Fig. 6. Mitutoyo Surf test SJ-201P portable surface roughness tester.
Fig. 5. Experimental setup.
three levels and six factors were chosen with one as a combination of a ratio to accommodate more varying parameters for the investigation as shown in Table 1. Minitab 17 was chosen for the statistical analysis, Taguchi design of experiment with L27 half factorial was considered, 27 experiments were performed on the AWJ machine. In the initial experiments 27 rectangular blocks were through cut as shown in Fig. 3 to understand the roughness and the influencing parameters, More 27 experiments were conducted for slot cutting with the same set of parameters along the thickness to investigate the depth of cut as shown in the Fig. 4.
Fig. 7. Depth measurement with Vernier Caliper.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Table 3 Grey Relational Analysis Results (GRA). Input
Step 1: Normalization Data
Step 2: Deviation Sequence
Step 3: Grey Relational Coefficient
Step 4: Grey Relational Grade and Rank
Exp. No
Ra (mm)
Depth, m
Ra (mm)
Depth, m
Ra (mm)
Depth, m
Ra (mm)
Depth, m
GRG
Rank
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
4.33 3.94 3.837 5.72 3.9 4.727 4.784 4.891 4.998 4.723 3.547 3.643 4.3 4.62 4.2 4.183 4.269 4.292 3.427 3.73 3.42 4.307 3.863 3.867 4.637 3.527 3.567
0.024 0.014 0.031 0.009 0.021 0.008 0.02 0.017 0.028 0.021 0.012 0.027 0.009 0.018 0.008 0.019 0.01 0.021 0.035 0.022 0.031 0.012 0.021 0.008 0.017 0.009 0.02
0.604 0.774 0.819 0.000 0.791 0.432 0.407 0.360 0.314 0.433 0.945 0.903 0.617 0.478 0.661 0.668 0.631 0.621 0.997 0.865 1.000 0.614 0.807 0.806 0.471 0.953 0.936
0.593 0.222 0.852 0.037 0.481 0.000 0.444 0.333 0.741 0.481 0.148 0.704 0.037 0.370 0.000 0.407 0.074 0.481 1.000 0.519 0.852 0.148 0.481 0.000 0.333 0.037 0.444
0.396 0.226 0.181 1.000 0.209 0.568 0.593 0.640 0.686 0.567 0.055 0.097 0.383 0.522 0.339 0.332 0.369 0.379 0.003 0.135 0.000 0.386 0.193 0.194 0.529 0.047 0.064
0.407 0.778 0.148 0.963 0.519 1.000 0.556 0.667 0.259 0.519 0.852 0.296 0.963 0.630 1.000 0.593 0.926 0.519 0.000 0.481 0.148 0.852 0.519 1.000 0.667 0.963 0.556
0.558 0.689 0.734 0.333 0.706 0.468 0.457 0.439 0.422 0.469 0.901 0.838 0.567 0.489 0.596 0.601 0.575 0.569 0.994 0.788 1.000 0.565 0.722 0.720 0.486 0.915 0.887
0.551 0.391 0.771 0.342 0.491 0.333 0.474 0.429 0.659 0.491 0.370 0.628 0.342 0.443 0.333 0.458 0.351 0.491 1.000 0.509 0.771 0.370 0.491 0.333 0.429 0.342 0.474
0.555 0.540 0.753 0.338 0.598 0.401 0.466 0.434 0.540 0.480 0.635 0.733 0.454 0.466 0.465 0.529 0.463 0.530 0.997 0.649 0.886 0.467 0.606 0.527 0.457 0.628 0.680
11 13 3 27 10 26 20 25 12 17 7 4 24 19 21 15 22 14 1 6 2 18 9 16 23 8 5
Table 4 Optimal parameters obtained from GRA technique.
3. Experimental plan
Parameters
Values
Water pressure Transverse speed Orifice size Nozzle size Abrasive flow rate Abrasive orifice size Roughness Depth
3400 bar 80 mm/min 0.30 mm 1.0 mm 0.18 kg/min 3.8 mm 3.427 mm 35 mm
2.2. Phase II – measurement Roughness measurement was done with the Surf test SJ-201P Roughness tester as shown in Fig. 6. For the slot cut depth measurement was done with a Vernier caliper. Table 2 illustrates the L27 orthogonal array of the performed experiments along with the measured value of roughness and depth of cut 2.3. Phase III – optimization with grey relational analysis (GRA) (GRA) Grey Relational Analysis combined with Taguchi method represents a novel approach for optimization. It is a normalization estimation technique extended to elucidate the complex multi-performance characteristics. GRA is used in combination with orthogonal array to draw implications about the effect of the factor and their interaction on multiple responses. Grey relational analysis is a type of method is used for optimizing the process parameter and helps in determining the optimal parameters. Fig. 2 represents the methodology for Taguchi-GRA method adopted.
AWJ machine for the experiment shown in Fig. 5 can produce a pressure of 4200 bar with a rated discharge of 3.6 ltr/min with pump capacity 50 hp. The constant parameters were chosen as stand of distance 3 mm, angle of cut 90 deg with single pass, the abrasive material used was garnet with mesh size 80 for Titanium 6AL4V material for cutting, typically garnet signifies the highest running cost on a AWJ machine, in order to minimize the use of garnet reduction of orifice to focusing tube ratios are important [5], for this three abrasive orifice sizes of 3.8 mm, 4 mm, 4.2 mm is incorporated to understand the effect as shown in the Table 1. Understanding the fact that orifice diameter and nozzle diameter wear with respect to machine hours both the parameters were taken into consideration with a ratio of 3:1 considering the variation in their diameters [17]. Surf test SJ-201P portable surface roughness tester manufactured by Mitutoyo was used to determine the average surface roughness (Ra) on the cut material, Vernier caliper was used to measure the slot depth along the thickness of the material. Roughness measurements were taken along the length and breadth as sown in Figs. 6 and 7 with their mean values. Designs of Experiments (DOE) were developed with Minitab 17 along with Taguchi L27 orthogonal array provided in Table 2 with the measured values of roughness and depth for the 27 experiments. For optimization grey relational analysis (GRA) was performed, ANOVA was implemented for investigating the effect of different parameters on roughness and depth. Steps for Grey relational analysis GRA are shown below:
Step Step Step Step
1: 2: 3: 4:
Normalize the value of roughness and depth Calculate deviation sequence Calculate Grey relational coefficient Grey relational grade and ranking.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Fig. 8. a: ANOVA results from Minitab 17 software for depth of cut (DoC). b: S/N ratio results from Minitab 17 software for depth of cut (DoC).
Table 3 shows the detail of the above four steps for the grey relational analysis (GRA). Optimal parameters are obtained from the Table 3 where rank 1 correspond to experiment no 19 which is highlighted for the optimal parameters which shows lowest roughness and highest depth, the optimal parameters are indicated below (Table 4). 4. Results and discussion The Analysis of Variances (ANOVA) examines the implication of the control factors and assesses the degree of their effects on the response features by using Minitab as statistical modelling software. Values of ‘‘Probability (P) > F” less than 0.05 indicate the terms are significant with a confidence level of 95%.
4.2. Surface roughness, (Ra) analysis It is observed from Fig. 10a that water pressure has the highest effect on the surface roughness (P value = 0.003) followed by transverse speed (P value = 0.014) and it can be verified from Fig. 10b that rank 1 and 2 is associated with water pressure and transverse speed. Table 3 from experiment 19 indicates the highest roughness value as 3.425 mm. From the main effect plot analysis for roughness the parameters that are influencing most from highest to the lowest are water pressure, transverse speed, nozzle to orifice diameter ratio, abrasive orifice size and abrasive mass flow rate. The results verified that an improvement of the surface roughness can be achieved by increasing the water pressure at 3400 bar and low traverse speed at 80 mm/min (Fig. 11).
4.1. Depth of cut (DoC) analysis Fig. 8a indicate the responses for the depth of cut using Minitab 2017, As per ANOVA for S/N ratio it can be concluded that depth of cut is influenced by transverse speed (P value = 0.014), followed by abrasive mass flow rate (P value = 0.481), it can also be verified from the Fig. 8b that the highest rank 1 is for transverse speed followed by rank 2 for abrasive mass flow rate. From the main effect plot Fig. 9, the influencing parameters from highest to the lowest are transverse speed, abrasive mass flow rate, nozzle to orifice diameter ratio, abrasive orifice size and water pressure and is achieved from experiment 19 from Table 3 which confirms the highest depth of cut with GRA grade and rank 1. The general phenomena is that as the transverse speed increases the depth of cut decreases due to the reason the impingement of the abrasive will have very less time to erode the material at that particular instant, However, the interaction between abrasive rate and feed rate is important; to achieve a high depth of cut with lower feed rates higher abrasive rates are needed.
Fig. 9. Main effects plot for S/N ratio for depth of cut (DoC).
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Fig. 10. a: ANOVA results from Minitab 17 software for roughness. b: S/N ratio results from Minitab 17 software for roughness.
experiments with AWJ machine and add to cost for the researcher, variation considered in the nozzle and orifice diameter with a ratio of 3:1 showed a good study to evaluate for real time changes in these parameters. GRA optimization was used to convert a multiresponse problem into a single-response problem. It was seen from the simulation that the most influencing parameters for the depth of cut was transverse speed and for surface roughness is water pressure.
6. Future scope of work
Fig. 11. Main effects plot for S/N ratio for roughness.
5. Conclusion The investigation of this research proposed a combination of GRA with Taguchi based design of experiments (DOE) for depth of cut and roughness for Titanium 6AL4V Grade 5 materials which is extensively used in many applications. The current research successfully incorporated more parameters in the design of experiments (DOE) which if taken individually will lead to more
AWJ cutting being a complicated process and selecting the optimal parameters is a big challenge, under the current scenario if we have a look at the 3 levels of all parameters the possibility of levels between the 2 numbers are very high that means if any value of any parameters are little bit changed the researcher need to again perform experiments and it add to cost every time any parameters are changed for investigation. For the future work researcher can try to solve the above problem where any change in any parameters can be deal with some sort of simulation method and did not further require any more experiments to be performed, now a day’s artificial intelligence (AI) with deep learning techniques (Neural Nets) can be used with the help of high end software’s like Matlab and Python and applying various algorithms to get the optimized parameters.
Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229
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Please cite this article as: Y. I. Mogul, I. Nasir and P. Myler, Investigation and optimization for depth of cut and surface roughness for control depth milling in Titanium Ti6AL4V with abrasive water jet cutting, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.12.229