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Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network Chithirai Pon Selvan a,⇑, Divya Midhunchakkaravarthy b, Swaroop Ramaswamy Pillai c, Sahith Reddy Madara d a
School of Science and Engineering, Curtin University Dubai, Dubai, United Arab Emirates Centre of Postgraduate Studies, Lincoln University College, Malaysia Department of Electronics and Telecommunication Engineering, Amity University Dubai, Dubai, United Arab Emirates d Sharjah Academy for Astronomy, Space Sciences and Technology, University of Sharjah, Sharjah, United Arab Emirates b c
a r t i c l e
i n f o
Article history: Received 15 June 2019 Accepted 30 June 2019 Available online xxxx Keywords: Abrasive waterjet machining (AWJM) Mass flow rate Optimization Mild steel Standoff distance Traverse speed Water pressure Artificial neural network (ANN)
a b s t r a c t Abrasive waterjet machining (AWJM) is one of the unconventional machining practices and is used widely in industries. The key output performance measures in this technique are depth of cut and surface roughness. This paper presents the experimental investigation on machining conditions of abrasive waterjet process in machining mild steel. Taguchi’s method of design of experiments was used to select input process parameters by varying water pressure, traverse speed, abrasive mass flow rate and standoff distance. The depth of cut and surface roughness were measured using Sigma Scope 500 profile projector and surface roughness tester respectively. After getting the required data from the experimental setup the same data is fed to the artificial neural networks and back propagation algorithm procedure is used to train the data using MATLAB programming package. It is observed that the artificial neural network (ANN) model was able to foresee the new set of data. This mathematical modelling tool can be used to predict dynamic varying different types of input to identify the stability of the system. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
1. Introduction At present, abrasive waterjet machining (AWJM) frameworks are presently broadly utilized in various mechanical and machining applications considering their demonstrated capacity to cut wide scope of metals and non-metals, for example, cast iron, treated steel, aluminium, copper, titanium and its compounds, high carbon steels, fibre-strengthened composites, etc [1] than ordinary machining system. A framework has been created and tried tentatively to cut these harder materials by methods for a surge of grating particles entrained in a high-speed waterjet. The working standard of these grating waterjets is the increasing speed of rough particles by blending them with a flood of water driven by weights of up to 44,000 psi [2]. This blending and speeding up procedure happen in a blending chamber made of a hard material, for example, boron carbide. Adding dry grating to the water jet in a unique blending chamber expands machining effectiveness [3]. Accord⇑ Corresponding author. E-mail address: [email protected] (C.P. Selvan).
ingly, it ends up conceivable to cut practically any material. Common pressure levels utilized by the AWJ framework runs from 400 MPa to 600 MPa. The most ordinarily utilized abrasive particles is garnet. As on account of each machining procedure, the nature of Abrasive waterjet machining procedure is altogether influenced by the fine-tuning parameters such as the water jet pressure, mass flow rate, standoff distance, traverse speed and measurement width of centering spout are of extraordinary significance yet accurately controllable [4]. The procedure has a few constraints and disadvantages. It might produce active noise and an untidy workplace, the machining isn’t appropriate for machining too thick pieces, set number of materials can be cut economically, narrow machining is additionally an issue with AWJ in exceptionally thick materials because it produces an V shaped gap between the cut [5]. The streamlining of process variable is a noteworthy region of research in abrasive waterjet machining technology [6]. Mathematical Modelling demonstrations in abrasive waterjet technology will be helpful in understanding the entire complex machining process. Modelling studies are the logical approaches to think
https://doi.org/10.1016/j.matpr.2019.06.757 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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C.P. Selvan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. (a) Diagram of an AWJM process; (b) AWJM head (1) Water Inlet, (2) Orifice, (3) Abrasive inlet, (4) Mixing Chamber, (5) Abrasive Nozzle).
and study about the whole complex system behaviour in detail. A scientific mathematical model of a framework is the connection between the I/P and O/P parameters in terms of numerical conditions [7]. As per the previous literature’s identified related to modelling and optimization of abrasive waterjet machining is basically founded on factual structure of tests DOE, for example, Taguchi strategy [8–10]. The multi-layer system with the backpropagation calculation is furthermost generally utilized in upgrading useful non-linear issues [11]. Neural network system has been effectively utilized in expectation of AWJ de-painting in mostly civil engineering complex concrete structures [12]. Scientists have prohibited numerous significant factors, for example, spout size & orifice distance during study which generally would influence the performance features in an unexpected way [13]. Hardly few scientists focused on demonstrating and streamlining of AWJM through different methods, for example, Fuzzy logic, neural network, and so on [14]. The back-propagation algorithm is utilized to build up an adequately precise and insightful mathematical model of AWJM. Prepared through test catalogue for a few industrial constituent materials, the system with positive structure and constraints can define a decent estimate to multifaceted nonlinear connections amongst the machining rate besides the sources of I/P parameters, for example, water pressure, material, machining quality. 2. Experimental work 2.1. Material choice The mildest grade of mild steel contains an extremely low measure of carbon from 0.06% to 0.26% by weight, and its high measure of carbon starting from 0.31% to 2.2%. Mild steel has a Manganese content of 0.69 to 0.89%, Silicon maximum of 0.41%, Sulfur maximum of 0.04%, and Phosphorous maximum of 0.04%. Mild steel grade 350 plates were utilized as the examples in this experimental study. The elements of these mild steel plates were 160 110 70 mm. It has the modulus of elasticity which is equivalent to 190,000 MPa. 2.2. Equipment details The apparatus used for machining operations of the samples was KMT Waterjet Cutting Pumps STREAMLINEÒ SL-VI, it has the capacity to cut most of the material including steel, glass, plastic, and many more. It has a Nominal Power Rate of about 15 hp (11 kW), Pressure of about 60,000 psi, Water Flow Rate of about 0.30 gpm (1.14 L/min), Control Voltage & Power Supply of about
24 V DC; 10 Amps DC, Operating Temperature of about Min. 5°Celsius, Max. 40°Celsius, Hydraulic Reservoir Capacity of about 12 Gal (45 L), Cooling Water Flow at 24°Celsius Water Temperature of about 2 gpm (7.6 L/min), Attenuator Volume of about 0.11 Gal (0.41L), Length of about 5600 (1.4 m), Width of about 2800 (711 mm), Height of about 3300 (838 mm), Weight of about 1800 lbs [15]. The diagram of an AWJM process is shown in Fig. 1. The AWJM head is appeared in Fig. 2. 2.3. Design of experiments (DOE) and AWJM procedure The choice of parameters which can deliver ideal outcomes is normally a complex process. Regularly this requires completing various tests, and along these lines the impact of innovative technological parameters on the properties of the last item can be resolved Table 1. The arranging of such studies is an interdisciplinary science, which lies at the convergence of metrology, connected arithmetic, measurements, and software engineering, enabling analysts to utilize the data the program has given them to decrease both the expense and time exhausted in getting the applicable data. This DOE empowers specialists to choose the info factors which altogether influence the procedure and can likewise construct a numerical model of the procedure and the scientific connections among information. Further, it can decide the estimation of the information amounts which influence the most wanted result of the procedure and decide the impact of variety in the extent of the contribution on the changeability of the entire procedure. The Taguchi Method is a technique that gives an orderly and proficient technique for procedure streamlining and is a helpful device for the plan of great frameworks. The Taguchi way to deal with DOE is simple for clients with less experience of factual techniques to apply and has in this manner developed wide fame in the core engineering industry. The Taguchi Method is utilized to create a S/N proportion g to decide the present disperse of qualities. The sign (S) is gotten from variables which are customizable or under the control of the client, yet noise (N) alludes to those components which influence the sign, however which are outside the ability to control of the client. AWJM normally aims for accomplishing the maximal profundity of cut and low surface roughness of the cut surface. Particularly during accuracy machining low surface roughness of the cut is of vital significance. For this situation the unpleasantness of the kerf’s surface ought to be as little as could reasonably be expected. This is depicted by the littler is better condition: n 1X g ¼ 10log y2 n i¼1 1
!
ð1Þ
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
C.P. Selvan et al. / Materials Today: Proceedings xxx (xxxx) xxx
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Fig. 2. (a) Raw Image of Garnet 80 Mesh – Abrasive particles; (b) SEM Image of Garnet 80 Mesh – Abrasive particles.
3. Experimental results and discussions
Table 1 Three levels of input parameters. Parameters
Units
Level 1
Level 2
Level 3
Water Pressure Traverse Speed Standoff Distance Mass Flow Rate
MPa mm/s mm g/s
270 0.42 1.8 8
335 1.46 3.4 11.5
400 2.5 5 15
where n is the quantity of redundancies of estimation, y the present estimation of the estimation, and I the quantity of factors/variables. Surface roughness of materials is a proportion of the mechanical nature of an item and a factor that incredibly impacts the assembling cost. It portrays the geometry and surface textures of the machined parts.
Ra ¼
1 L
Z
L
jyðxÞjjdxj
ð2Þ
0
where L is the testing length, y is profile bend and x is the profile course direction. The normal surface roughness is indicated by Ra. Some of the parameters which have been kept constant during conducting the experiment which are work piece thickness – 10 mm, Focusing tube diameter – 0.8 mm, Impact angle – 900 (neutral nozzle position), Nozzle diameter – 1 mm, Orifice diameter – 0.25 mm, Water flow rate – 5.6 L/minute, Abrasive size – 80 Mesh (0.180 mm), Abrasive type – Garnet. Fig. 2(a) indicates the raw image of garnet 80 mesh abrasive particles and Fig. 2(b) indicates the SEM image of the garnet 80 mesh abrasive particles.
3.1. Water pressure on depth of cut & abrasive mass flow rate on depth of cut In Fig. 3(a), the complete graph was plotted between depth of cut (mm) versus water pressure (MPa) by keeping three process parameters as constant, which are mass flow rate is considered as 8 g/s, standoff distance is considered as 5 mm, and traverse speed is considered as 0.42 mm/s. By the plot results we can confirm that depth of cut (mm) of the mild steel is directly proportional (/) to water pressure (MPa). This is mainly because, at high water pressure there will be high jet kinetic energy. In Fig. 3(b), the complete graph was plotted between depth of cut (mm) versus mass flow rate (g/s) by keeping three process parameters as constant, which are water pressure is considered as 270 MPa, standoff distance is considered as 5 mm, and traverse speed is considered as 0.42 mm/s. By the plot results we can confirm that depth of cut (mm) of the mild steel is directly proportional (/) to mass flow rate (g/s).
3.2. Traverse speed on depth of cut & standoff distance on depth of cut In Fig. 4(a), the complete graph was plotted between depth of cut (mm) versus traverse speed (mm/s) by keeping three process parameters as constant, which are water pressure is considered as 270 MPa, standoff distance is considered as 5 mm, and mass flow rate is considered as 8 g/s. By the plot results we can confirm
Fig. 3. (a) Depth of cut (mm) on water pressure (MPa); (b) Depth of cut (mm) on mass flow rate (g/s).
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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C.P. Selvan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 4. (a) Depth of cut (mm) on traverse speed (mm/s); (b) Depth of cut (mm) on standoff distance (mm).
that depth of cut (mm) of the mild steel is inversely proportional (/) to traverse speed (mm/s). In Fig. 4(b), the complete graph was plotted between depth of cut (mm) versus standoff distance (mm) by keeping three process parameters as constant, which are water pressure is considered as 270 MPa, traverse speed is considered as 0.42 mm/s, and mass flow rate is considered as 8 g/s. By the plot results we can confirm that depth of cut (mm) of the mild steel is inversely proportional (/) to standoff distance (mm). 3.3. Water pressure on surface roughness & abrasive mass flow rate on surface roughness In Fig. 5(a), the complete graph was plotted between Mean surface roughness (mm) versus water pressure (MPa) by keeping three process parameters as constant, which are standoff distance is considered as 5 mm, traverse speed is considered as 0.42 mm/s, and
mass flow rate is considered as 8 g/s. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is inversely proportional (/) to water pressure (MPa). In Fig. 5(b), the complete graph was plotted between Mean surface roughness (mm) versus Mass flow rate (g/s) by keeping three process parameters as constant, which are standoff distance is considered as 5 mm, traverse speed is considered as 0.42 mm/s, and water pressure is considered as 270 MPa. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is inversely proportional (/) to Mass flow rate (g/s). 3.4. Traverse speed on surface roughness & standoff distance on surface roughness In Fig. 6(a), the complete graph was plotted between Mean surface roughness (mm) versus Traverse speed (mm/s) by keeping three process parameters as constant, which are standoff distance
Fig. 5. (a) Mean surface roughness (mm) on water pressure (MPa); (b) Mean surface roughness (mm) on mass flow rate (g/s).
Fig. 6. (a) Mean surface roughness (mm) on traverse speed (mm/s); (b) Mean surface roughness (mm) on standoff distance (mm).
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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C.P. Selvan et al. / Materials Today: Proceedings xxx (xxxx) xxx
is considered as 5 mm, mass flow rate is considered as 8 g/s, and water pressure is considered as 270 MPa. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is directly proportional (/) to Traverse speed (mm/s). In Fig. 6(b), the complete graph was plotted between Mean surface roughness (mm) versus Standoff distance (mm) by keeping three process parameters as constant, which are traverse speed is considered as 0.42 mm/s, mass flow rate is considered as 8 g/s, and water pressure is considered as 270 MPa. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is directly proportional (/) to Standoff distance (mm).
From the chain rule,
@E2 @E2 @IB ¼ ¼ OA @W AB @IB @W AB From equation (4) and (2) the new weight is
W ABðnewÞ ¼ W ABðoldÞ g
@E2 OA @IB
Neth1 ¼ W 1 I1 þ W 2 I2 þ ðb 1Þ 1 1 þ eNeth1
Oh1 ¼
Table 2 shows the deviation in the values of depth of cut when the selected input parameters are varied. The mathematical model is determined to see the variation with the actual value. Table 2 shows input and output experimental data for depth of cut in AWJM of mild steel used for ANN modelling.
Neto1 ¼ W 5 Oh1 þ W 6 Oh2 þ b 1
ð3Þ
where represents the weight matrix from one node to other. Η indicates the learning rate and E is the error
W ABðnewÞ ¼ W ABðoldÞ g
@E2 @W AB
ð8Þ ð9Þ ð10Þ
NET indicates the hidden layer and b is the bias.
Oo1 ¼
1 1 þ eNet01
5. Back propagation algorithm (ANN model training)
Network Error ¼ Pred Req ¼ E
ð7Þ
I and represents the input and the output of nodes.
4. Artificial neural network (ANN) modelling
The following equation shows the algorithm used in the backpropagation algorithm
ð6Þ
Etotal ¼
1X ðt oÞ2 2
ð11Þ
ð12Þ
E indicates the final error in the back-propagation algorithm. The figure below indicates how the errors are propagated. As per the industrial necessities, 2 fundamental parameters considered work-piece material type, abrasive water-jet pressure are kept as I/P information parameters of the system, while machining
ð4Þ
@E2 @W AB
ð5Þ
Table 2 Input and output process parameters. Pressure (MPa)
Traverse Speed (mm/s)
Mass Flow Rate (g/s)
Standoff Distance (mm)
Depth of Cut (mm)
270 275 280 285 290 295 300 305 310 315 320 325 330 335 340 345 350 355 360 365 370 375 380 385 390 395 400
2.5 2.42 2.34 2.26 2.18 2.1 2.02 1.94 1.86 1.78 1.7 1.62 1.54 1.46 1.38 1.3 1.22 1.14 1.06 0.98 0.9 0.82 0.74 0.66 0.58 0.5 0.42
8 8.27 8.54 8.81 9.08 9.35 9.62 9.89 10.16 10.43 10.7 10.97 11.24 11.51 11.78 12.05 12.32 12.59 12.86 13.13 13.4 13.67 13.94 14.21 14.48 14.75 15
5 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3 2.3 2.2 2.1 2 1.9 1.8
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
Fig. 7. Artificial Neural Network Topological Structure.
Fig. 8. Weights Modification in Artificial Neural Network.
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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Fig. 9. (a) Artificial Neural Network Learning; (b) Actual and predicted depth of cut; (c) Three-dimensional view for depth of cut.
Fig. 10. (a) Actual and predicted depth of cut; (b) Error curve from Artificial Neural Network; (c) Three-dimensional view for depth of cut.
velocity is set as yield O/P of the system. The first Fig. 7 indicates the topological structure and the second Fig. 8 shows the weight matrix are updated. Here the MATLAB is used to program the back-propagation algorithm and to update the weights. 6. Simulation results and discussion The following Fig. 9(a) below shows that the iteration with respect to the error. It can be seen that the error keeps reducing and is stable between the 0 and 1.5 stable. In this case the number of iterations considered is 1000. The Fig. 9(b) shows the depth of cut based on the experimental set up highlighted in red colour and the other one indicates the prediction based on the artificial neural networks. The Fig. 9(c) indicates the three-dimensional view which shows the depth of cut based on the two parameters. Similarly, the depth of cut also depends upon two more parameters which are the stand of distance and the transverse speed. The Fig. 10(a) shows that two curve one is the predicted depth of cut for all the data and the red line indicates the actual depth of cut from the experimental set up. The Fig. 10(b) shows the learning curve where it is observed that the error decreases with number of iterations. It reaches less that 0.5 after 1000 iteration. The Fig. 10(c) shows the three-dimensional view for the depth of cut for the traverse speed and the standoff distance. 7. Conclusions The abrasive waterjet machining conditions between input and output parameters were studied for machining mild steel. Depth of
cut and surface roughness were considered as output parameters and water pressure, traverse speed, abrasive mass flow rate and standoff distance were considered as input parameters. The following conclusions were derived while machining mild steel using abrasive waterjet machining technique. Both water pressure and mass flow rate are directly proportional to depth of cut and surface smoothness. Depth of cut and surface smoothness values are decreased, when traverse speed and standoff distances values are increased. These results are also observed with the artificial neural network modelling. It can be seen from the graphs that the proposed prediction of artificial neural network model of certain AWJM system can be utilized for any further theoretical studies of AWJM processes.
References [1] A. Hascalik, U. Caydas, H. Gurun, Effect of traverse speed on abrasive waterjet machining of Ti-6Al-4V alloy, Meter. Des. 28 (2007) 1953–1957. [2] A. Momber, R. Kovacevic, Principles of Abrasive Waterjet Machining, SpringerVerlag, London, 1998. [3] M. Hashish, A model for abrasive waterjet (AWJ) machining, Trans. ASME J. Eng. Mater. Technol. III (1989) 154–162. [4] Chithirai Pon Selvan, Sahith Reddy Madara, S.S. Sampath, N.S. Sarath Raj, Effects of process parameters on depth of cut in abrasive waterjet cutting of phosphate glass, in: IEEE Xplore, Advances in Science and Engineering Technology International Conferences (ASET), Abu-Dhabi, pp. 1–6, 2018. [5] J. Wang, Abrasive Waterjet Machining of Engineering Materials, UetikonZuerich [Swizerland]: Trans Tech Publications, 2003. [6] C. Ma, R.T. Deam, A correlation for predicting the kerf profile from abrasive waterjet cutting, Exp. Thermal Fluid Sci. 30 (2006) 337–343. [7] K. Babets, E.S Geskin, B. Chaudhuri, Neural Network Model of Waterjet Depainting Process, in: Proceedings of the10th American Waterjet Conference, WJTA, Houston, Texas, USA, 1999. [8] Chithirai Pon Selvan, Sahith Reddy Madara, Swaroop Ramaswamy Pillai, Ramesh Vandanapu, In-depth evaluation of process variables in abrasive waterjet cutting of alumina ceramics, in: IEEE Explore, Advances in Science and
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
C.P. Selvan et al. / Materials Today: Proceedings xxx (xxxx) xxx Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, pp. 1–4, 2019. [9] R. Kovacevic, Monitoring the depth of abrasive waterjet penetration, Int. J. Machine Tools Manufacture 32 (5) (1992) 725–736. [10] Sahith Reddy Madara, N.S. Sarath Raj, Jerrin Thadathil Varghese, Chithirai Pon Selvan, Experimental investigations on abrasive waterjet machining of hybridized Kevlar with jute fiber reinforced epoxy composite using Taguchi & ANOVA approach, in: IEEE Explore, Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, pp. 1–9, 2019. [11] Yiyu Lu, Xiaohong Li, Binquan Jiao, Yong Liao, Application of artificial neural networks in abrasive waterjet cutting process, Springer-Verlag, Berlin Heidelberg, 2005, pp. 877–882.
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[12] R. Hecht-Nielsen, Theory of the backpropagation neural network, in: Proceedings of the International Joint Conference on Neural Networks, pp. 593–605, 1989. [13] M. Hashish, Optimization factors in abrasive waterjet machining, Trans. ASME J. Eng. Ind. 113 (1991) 29–37. [14] John Rozario J. 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. Processing Technol., 185 (1–3) 217–227, 2007. [15] Sahith Reddy Madara, Chithirai Pon Selvan, Swaroop Ramaswamy, Impact of process parameters on surface roughness of hastelloy using abrasive waterjet machining technology, Int. J. Recent Technol. Eng. 7 (5S2) (2019) 419–425.
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network Chithirai Pon Selvan a,⇑, Divya Midhunchakkaravarthy b, Swaroop Ramaswamy Pillai c, Sahith Reddy Madara d a
School of Science and Engineering, Curtin University Dubai, Dubai, United Arab Emirates Centre of Postgraduate Studies, Lincoln University College, Malaysia Department of Electronics and Telecommunication Engineering, Amity University Dubai, Dubai, United Arab Emirates d Sharjah Academy for Astronomy, Space Sciences and Technology, University of Sharjah, Sharjah, United Arab Emirates b c
a r t i c l e
i n f o
Article history: Received 15 June 2019 Accepted 30 June 2019 Available online xxxx Keywords: Abrasive waterjet machining (AWJM) Mass flow rate Optimization Mild steel Standoff distance Traverse speed Water pressure Artificial neural network (ANN)
a b s t r a c t Abrasive waterjet machining (AWJM) is one of the unconventional machining practices and is used widely in industries. The key output performance measures in this technique are depth of cut and surface roughness. This paper presents the experimental investigation on machining conditions of abrasive waterjet process in machining mild steel. Taguchi’s method of design of experiments was used to select input process parameters by varying water pressure, traverse speed, abrasive mass flow rate and standoff distance. The depth of cut and surface roughness were measured using Sigma Scope 500 profile projector and surface roughness tester respectively. After getting the required data from the experimental setup the same data is fed to the artificial neural networks and back propagation algorithm procedure is used to train the data using MATLAB programming package. It is observed that the artificial neural network (ANN) model was able to foresee the new set of data. This mathematical modelling tool can be used to predict dynamic varying different types of input to identify the stability of the system. Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
1. Introduction At present, abrasive waterjet machining (AWJM) frameworks are presently broadly utilized in various mechanical and machining applications considering their demonstrated capacity to cut wide scope of metals and non-metals, for example, cast iron, treated steel, aluminium, copper, titanium and its compounds, high carbon steels, fibre-strengthened composites, etc [1] than ordinary machining system. A framework has been created and tried tentatively to cut these harder materials by methods for a surge of grating particles entrained in a high-speed waterjet. The working standard of these grating waterjets is the increasing speed of rough particles by blending them with a flood of water driven by weights of up to 44,000 psi [2]. This blending and speeding up procedure happen in a blending chamber made of a hard material, for example, boron carbide. Adding dry grating to the water jet in a unique blending chamber expands machining effectiveness [3]. Accord⇑ Corresponding author. E-mail address: [email protected] (C.P. Selvan).
ingly, it ends up conceivable to cut practically any material. Common pressure levels utilized by the AWJ framework runs from 400 MPa to 600 MPa. The most ordinarily utilized abrasive particles is garnet. As on account of each machining procedure, the nature of Abrasive waterjet machining procedure is altogether influenced by the fine-tuning parameters such as the water jet pressure, mass flow rate, standoff distance, traverse speed and measurement width of centering spout are of extraordinary significance yet accurately controllable [4]. The procedure has a few constraints and disadvantages. It might produce active noise and an untidy workplace, the machining isn’t appropriate for machining too thick pieces, set number of materials can be cut economically, narrow machining is additionally an issue with AWJ in exceptionally thick materials because it produces an V shaped gap between the cut [5]. The streamlining of process variable is a noteworthy region of research in abrasive waterjet machining technology [6]. Mathematical Modelling demonstrations in abrasive waterjet technology will be helpful in understanding the entire complex machining process. Modelling studies are the logical approaches to think
https://doi.org/10.1016/j.matpr.2019.06.757 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Manufacturing, Material Science and Engineering.
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
2
C.P. Selvan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. (a) Diagram of an AWJM process; (b) AWJM head (1) Water Inlet, (2) Orifice, (3) Abrasive inlet, (4) Mixing Chamber, (5) Abrasive Nozzle).
and study about the whole complex system behaviour in detail. A scientific mathematical model of a framework is the connection between the I/P and O/P parameters in terms of numerical conditions [7]. As per the previous literature’s identified related to modelling and optimization of abrasive waterjet machining is basically founded on factual structure of tests DOE, for example, Taguchi strategy [8–10]. The multi-layer system with the backpropagation calculation is furthermost generally utilized in upgrading useful non-linear issues [11]. Neural network system has been effectively utilized in expectation of AWJ de-painting in mostly civil engineering complex concrete structures [12]. Scientists have prohibited numerous significant factors, for example, spout size & orifice distance during study which generally would influence the performance features in an unexpected way [13]. Hardly few scientists focused on demonstrating and streamlining of AWJM through different methods, for example, Fuzzy logic, neural network, and so on [14]. The back-propagation algorithm is utilized to build up an adequately precise and insightful mathematical model of AWJM. Prepared through test catalogue for a few industrial constituent materials, the system with positive structure and constraints can define a decent estimate to multifaceted nonlinear connections amongst the machining rate besides the sources of I/P parameters, for example, water pressure, material, machining quality. 2. Experimental work 2.1. Material choice The mildest grade of mild steel contains an extremely low measure of carbon from 0.06% to 0.26% by weight, and its high measure of carbon starting from 0.31% to 2.2%. Mild steel has a Manganese content of 0.69 to 0.89%, Silicon maximum of 0.41%, Sulfur maximum of 0.04%, and Phosphorous maximum of 0.04%. Mild steel grade 350 plates were utilized as the examples in this experimental study. The elements of these mild steel plates were 160 110 70 mm. It has the modulus of elasticity which is equivalent to 190,000 MPa. 2.2. Equipment details The apparatus used for machining operations of the samples was KMT Waterjet Cutting Pumps STREAMLINEÒ SL-VI, it has the capacity to cut most of the material including steel, glass, plastic, and many more. It has a Nominal Power Rate of about 15 hp (11 kW), Pressure of about 60,000 psi, Water Flow Rate of about 0.30 gpm (1.14 L/min), Control Voltage & Power Supply of about
24 V DC; 10 Amps DC, Operating Temperature of about Min. 5°Celsius, Max. 40°Celsius, Hydraulic Reservoir Capacity of about 12 Gal (45 L), Cooling Water Flow at 24°Celsius Water Temperature of about 2 gpm (7.6 L/min), Attenuator Volume of about 0.11 Gal (0.41L), Length of about 5600 (1.4 m), Width of about 2800 (711 mm), Height of about 3300 (838 mm), Weight of about 1800 lbs [15]. The diagram of an AWJM process is shown in Fig. 1. The AWJM head is appeared in Fig. 2. 2.3. Design of experiments (DOE) and AWJM procedure The choice of parameters which can deliver ideal outcomes is normally a complex process. Regularly this requires completing various tests, and along these lines the impact of innovative technological parameters on the properties of the last item can be resolved Table 1. The arranging of such studies is an interdisciplinary science, which lies at the convergence of metrology, connected arithmetic, measurements, and software engineering, enabling analysts to utilize the data the program has given them to decrease both the expense and time exhausted in getting the applicable data. This DOE empowers specialists to choose the info factors which altogether influence the procedure and can likewise construct a numerical model of the procedure and the scientific connections among information. Further, it can decide the estimation of the information amounts which influence the most wanted result of the procedure and decide the impact of variety in the extent of the contribution on the changeability of the entire procedure. The Taguchi Method is a technique that gives an orderly and proficient technique for procedure streamlining and is a helpful device for the plan of great frameworks. The Taguchi way to deal with DOE is simple for clients with less experience of factual techniques to apply and has in this manner developed wide fame in the core engineering industry. The Taguchi Method is utilized to create a S/N proportion g to decide the present disperse of qualities. The sign (S) is gotten from variables which are customizable or under the control of the client, yet noise (N) alludes to those components which influence the sign, however which are outside the ability to control of the client. AWJM normally aims for accomplishing the maximal profundity of cut and low surface roughness of the cut surface. Particularly during accuracy machining low surface roughness of the cut is of vital significance. For this situation the unpleasantness of the kerf’s surface ought to be as little as could reasonably be expected. This is depicted by the littler is better condition: n 1X g ¼ 10log y2 n i¼1 1
!
ð1Þ
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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Fig. 2. (a) Raw Image of Garnet 80 Mesh – Abrasive particles; (b) SEM Image of Garnet 80 Mesh – Abrasive particles.
3. Experimental results and discussions
Table 1 Three levels of input parameters. Parameters
Units
Level 1
Level 2
Level 3
Water Pressure Traverse Speed Standoff Distance Mass Flow Rate
MPa mm/s mm g/s
270 0.42 1.8 8
335 1.46 3.4 11.5
400 2.5 5 15
where n is the quantity of redundancies of estimation, y the present estimation of the estimation, and I the quantity of factors/variables. Surface roughness of materials is a proportion of the mechanical nature of an item and a factor that incredibly impacts the assembling cost. It portrays the geometry and surface textures of the machined parts.
Ra ¼
1 L
Z
L
jyðxÞjjdxj
ð2Þ
0
where L is the testing length, y is profile bend and x is the profile course direction. The normal surface roughness is indicated by Ra. Some of the parameters which have been kept constant during conducting the experiment which are work piece thickness – 10 mm, Focusing tube diameter – 0.8 mm, Impact angle – 900 (neutral nozzle position), Nozzle diameter – 1 mm, Orifice diameter – 0.25 mm, Water flow rate – 5.6 L/minute, Abrasive size – 80 Mesh (0.180 mm), Abrasive type – Garnet. Fig. 2(a) indicates the raw image of garnet 80 mesh abrasive particles and Fig. 2(b) indicates the SEM image of the garnet 80 mesh abrasive particles.
3.1. Water pressure on depth of cut & abrasive mass flow rate on depth of cut In Fig. 3(a), the complete graph was plotted between depth of cut (mm) versus water pressure (MPa) by keeping three process parameters as constant, which are mass flow rate is considered as 8 g/s, standoff distance is considered as 5 mm, and traverse speed is considered as 0.42 mm/s. By the plot results we can confirm that depth of cut (mm) of the mild steel is directly proportional (/) to water pressure (MPa). This is mainly because, at high water pressure there will be high jet kinetic energy. In Fig. 3(b), the complete graph was plotted between depth of cut (mm) versus mass flow rate (g/s) by keeping three process parameters as constant, which are water pressure is considered as 270 MPa, standoff distance is considered as 5 mm, and traverse speed is considered as 0.42 mm/s. By the plot results we can confirm that depth of cut (mm) of the mild steel is directly proportional (/) to mass flow rate (g/s).
3.2. Traverse speed on depth of cut & standoff distance on depth of cut In Fig. 4(a), the complete graph was plotted between depth of cut (mm) versus traverse speed (mm/s) by keeping three process parameters as constant, which are water pressure is considered as 270 MPa, standoff distance is considered as 5 mm, and mass flow rate is considered as 8 g/s. By the plot results we can confirm
Fig. 3. (a) Depth of cut (mm) on water pressure (MPa); (b) Depth of cut (mm) on mass flow rate (g/s).
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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Fig. 4. (a) Depth of cut (mm) on traverse speed (mm/s); (b) Depth of cut (mm) on standoff distance (mm).
that depth of cut (mm) of the mild steel is inversely proportional (/) to traverse speed (mm/s). In Fig. 4(b), the complete graph was plotted between depth of cut (mm) versus standoff distance (mm) by keeping three process parameters as constant, which are water pressure is considered as 270 MPa, traverse speed is considered as 0.42 mm/s, and mass flow rate is considered as 8 g/s. By the plot results we can confirm that depth of cut (mm) of the mild steel is inversely proportional (/) to standoff distance (mm). 3.3. Water pressure on surface roughness & abrasive mass flow rate on surface roughness In Fig. 5(a), the complete graph was plotted between Mean surface roughness (mm) versus water pressure (MPa) by keeping three process parameters as constant, which are standoff distance is considered as 5 mm, traverse speed is considered as 0.42 mm/s, and
mass flow rate is considered as 8 g/s. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is inversely proportional (/) to water pressure (MPa). In Fig. 5(b), the complete graph was plotted between Mean surface roughness (mm) versus Mass flow rate (g/s) by keeping three process parameters as constant, which are standoff distance is considered as 5 mm, traverse speed is considered as 0.42 mm/s, and water pressure is considered as 270 MPa. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is inversely proportional (/) to Mass flow rate (g/s). 3.4. Traverse speed on surface roughness & standoff distance on surface roughness In Fig. 6(a), the complete graph was plotted between Mean surface roughness (mm) versus Traverse speed (mm/s) by keeping three process parameters as constant, which are standoff distance
Fig. 5. (a) Mean surface roughness (mm) on water pressure (MPa); (b) Mean surface roughness (mm) on mass flow rate (g/s).
Fig. 6. (a) Mean surface roughness (mm) on traverse speed (mm/s); (b) Mean surface roughness (mm) on standoff distance (mm).
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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C.P. Selvan et al. / Materials Today: Proceedings xxx (xxxx) xxx
is considered as 5 mm, mass flow rate is considered as 8 g/s, and water pressure is considered as 270 MPa. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is directly proportional (/) to Traverse speed (mm/s). In Fig. 6(b), the complete graph was plotted between Mean surface roughness (mm) versus Standoff distance (mm) by keeping three process parameters as constant, which are traverse speed is considered as 0.42 mm/s, mass flow rate is considered as 8 g/s, and water pressure is considered as 270 MPa. By the plot results we can confirm that Mean surface roughness (mm) of the mild steel is directly proportional (/) to Standoff distance (mm).
From the chain rule,
@E2 @E2 @IB ¼ ¼ OA @W AB @IB @W AB From equation (4) and (2) the new weight is
W ABðnewÞ ¼ W ABðoldÞ g
@E2 OA @IB
Neth1 ¼ W 1 I1 þ W 2 I2 þ ðb 1Þ 1 1 þ eNeth1
Oh1 ¼
Table 2 shows the deviation in the values of depth of cut when the selected input parameters are varied. The mathematical model is determined to see the variation with the actual value. Table 2 shows input and output experimental data for depth of cut in AWJM of mild steel used for ANN modelling.
Neto1 ¼ W 5 Oh1 þ W 6 Oh2 þ b 1
ð3Þ
where represents the weight matrix from one node to other. Η indicates the learning rate and E is the error
W ABðnewÞ ¼ W ABðoldÞ g
@E2 @W AB
ð8Þ ð9Þ ð10Þ
NET indicates the hidden layer and b is the bias.
Oo1 ¼
1 1 þ eNet01
5. Back propagation algorithm (ANN model training)
Network Error ¼ Pred Req ¼ E
ð7Þ
I and represents the input and the output of nodes.
4. Artificial neural network (ANN) modelling
The following equation shows the algorithm used in the backpropagation algorithm
ð6Þ
Etotal ¼
1X ðt oÞ2 2
ð11Þ
ð12Þ
E indicates the final error in the back-propagation algorithm. The figure below indicates how the errors are propagated. As per the industrial necessities, 2 fundamental parameters considered work-piece material type, abrasive water-jet pressure are kept as I/P information parameters of the system, while machining
ð4Þ
@E2 @W AB
ð5Þ
Table 2 Input and output process parameters. Pressure (MPa)
Traverse Speed (mm/s)
Mass Flow Rate (g/s)
Standoff Distance (mm)
Depth of Cut (mm)
270 275 280 285 290 295 300 305 310 315 320 325 330 335 340 345 350 355 360 365 370 375 380 385 390 395 400
2.5 2.42 2.34 2.26 2.18 2.1 2.02 1.94 1.86 1.78 1.7 1.62 1.54 1.46 1.38 1.3 1.22 1.14 1.06 0.98 0.9 0.82 0.74 0.66 0.58 0.5 0.42
8 8.27 8.54 8.81 9.08 9.35 9.62 9.89 10.16 10.43 10.7 10.97 11.24 11.51 11.78 12.05 12.32 12.59 12.86 13.13 13.4 13.67 13.94 14.21 14.48 14.75 15
5 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3 2.3 2.2 2.1 2 1.9 1.8
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56
Fig. 7. Artificial Neural Network Topological Structure.
Fig. 8. Weights Modification in Artificial Neural Network.
Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757
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Fig. 9. (a) Artificial Neural Network Learning; (b) Actual and predicted depth of cut; (c) Three-dimensional view for depth of cut.
Fig. 10. (a) Actual and predicted depth of cut; (b) Error curve from Artificial Neural Network; (c) Three-dimensional view for depth of cut.
velocity is set as yield O/P of the system. The first Fig. 7 indicates the topological structure and the second Fig. 8 shows the weight matrix are updated. Here the MATLAB is used to program the back-propagation algorithm and to update the weights. 6. Simulation results and discussion The following Fig. 9(a) below shows that the iteration with respect to the error. It can be seen that the error keeps reducing and is stable between the 0 and 1.5 stable. In this case the number of iterations considered is 1000. The Fig. 9(b) shows the depth of cut based on the experimental set up highlighted in red colour and the other one indicates the prediction based on the artificial neural networks. The Fig. 9(c) indicates the three-dimensional view which shows the depth of cut based on the two parameters. Similarly, the depth of cut also depends upon two more parameters which are the stand of distance and the transverse speed. The Fig. 10(a) shows that two curve one is the predicted depth of cut for all the data and the red line indicates the actual depth of cut from the experimental set up. The Fig. 10(b) shows the learning curve where it is observed that the error decreases with number of iterations. It reaches less that 0.5 after 1000 iteration. The Fig. 10(c) shows the three-dimensional view for the depth of cut for the traverse speed and the standoff distance. 7. Conclusions The abrasive waterjet machining conditions between input and output parameters were studied for machining mild steel. Depth of
cut and surface roughness were considered as output parameters and water pressure, traverse speed, abrasive mass flow rate and standoff distance were considered as input parameters. The following conclusions were derived while machining mild steel using abrasive waterjet machining technique. Both water pressure and mass flow rate are directly proportional to depth of cut and surface smoothness. Depth of cut and surface smoothness values are decreased, when traverse speed and standoff distances values are increased. These results are also observed with the artificial neural network modelling. It can be seen from the graphs that the proposed prediction of artificial neural network model of certain AWJM system can be utilized for any further theoretical studies of AWJM processes.
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Please cite this article as: C. P. Selvan, D. Midhunchakkaravarthy, S. R. Pillai et al., Investigation on abrasive waterjet machining conditions of mild steel using artificial neural network, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.06.757