Issue 
Manufacturing Rev.
Volume 9, 2022



Article Number  2  
Number of page(s)  15  
DOI  https://doi.org/10.1051/mfreview/2021027  
Published online  11 January 2022 
Research Article
Taguchi based fuzzy logic model for optimisation and prediction of surface roughness during AWJM of DRCUFP composites
Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher
Education, Manipal 571604, Karnataka, India
^{*} email: adithya.hegde@learner.manipal.edu
Received:
27
July
2021
Accepted:
13
October
2021
From last two decades, plant fiber reinforced polymer/polyester composites have been effectively used in structural and automotive applications. Researchers and manufacturers are looking forward for an effective utilization of these composites. However, despite the outstanding properties in terms of load bearing capacity and environmental sustainability of plant fibers the uptake of these composites are limited due to its poor machinability characteristics. Hence in this paper, Taguchi based fuzzy logic model for the optimization and prediction of process output variable such as surface roughness during Abrasive Water Jet Machining (AWJM) of new class of plant fiber reinforced polyester composites i.e., Discontinuously Reinforced Caryota Urens Fiber Polyester (DRCUFP) composites has been explored. Initially machining experiments has been carried out using L_{27} orthogonal array obtained from Taguchi Design of Experiments (TDOE). Finally, Taguchi based fuzzy logic model has been developed for optimisation and prediction of surface roughness. From the extensive experimentation using TDOE it was observed that the optimum cutting conditions for obtaining minimum surface roughness value, water pressure (A): 300 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 12 g/s, depth of cut (C): 5 mm and Abrasive Size:200 microns. Further from FLM, it is observed that minimum water pressure (A): 100 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 8 g/s, depth of cut (C): 5 mm and abrasive size:100 microns gave higher surface roughness values (3.47 microns) than that at maximum water pressure (A): 300 bar, traverse speed (B): 150 mm, stand of distance: 4 mm, abrasive flow rate: 12 g/s, depth of cut (C): 15 mm and abrasive size:200 microns the surface roughness values (3.25 microns).
Key words: DRCUFP composites / AWJM / surface roughness / TDOE / fuzzy logic model (FLM)
© R. Shetty and A. Hegde, Published by EDP Sciences 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1 Introduction
Around 3000 years ago Egyptians made use of the concept of plant fiber reinforced composites (PFRC) using straw reinforced clay as construction materials [1]. It has been revealed that plant fibers possess a unique mechanical property, easy manufacturability and can be subjected to surface treatment process for improvising its properties [2–4]. Today PFRC has been widely used as nonstructural components for automotive and building industry [5–9] suggested that cost and ecological awareness has driven plant fiber composites a huge demand in wide variety of applications [10–18] carried out research on application of cellulose based natural fibers such as jute, flax and hemp as an alternative to glass fiber in composites. Ramesh et al. [19] suggested that less weight, low cost, biodegradability and good strength has made plant fibers a substitute for other materials. Raviraj Shetty et al. [20] carried out research on processing, mechanical characterization and its tribological study on DRCUFP composites. They suggested that 20 fiber vol.% DRCUFP composite had excellent load bearing capacity and it's bending properties [21] developed Discontinuously Reinforced Sisal Fiber Polyester (DRSFP) composite laminates. In their work mechanical characterization and machinability study on DRSFP such as surface roughness and delamination during drilling based on design of experiments has been investigated. Shetty et al. [22,23] and [24] explored areas related to application of Design of Experiments in metal cutting operations. However, high thermal distortion, poor machining versatility, more stress, poor surface finish and delamination occurring during machining of these plant fiber composites during conventional machining has led Abrasive Waterjet Cutting [AWJC] an alternate manufacturing technology for machining of metals and materials [25–30]. Chithirai et al. [31,32] made use of statistical approach during AWJ machining for various metals such as granite, Stainless steel and Cast Iron. Shirpurkar et al. [33], Ravindra et al. [34] suggested that fuzzy logic can be applied successfully to predict process output variables and can be effectively used for identifying the optimum cutting parameters and preventing time consuming experiments. But research on plant fibre reinforced composites is few.Hence optimization and prediction of surface roughness during AWJM of DRCUFP composites using Taguchi based fuzzy logic model has been investigated in this work.
2 Methods and materials
The machining tests were carried out in Abrasive Water machining (Fig. 1). The specifications of Abrasive water machining are shown in Table 1. The abrasive materials used in the present research was silicon carbide which is shown in Figure 2. Sieve analysis was carried out to estimate the average particle size and to know the particle size distribution of silicon carbide abrasive grains. Table 2 shows the properties of silicon carbide abrasive grains used in this experimentation.
The materials used was 20 vol.% Caryota Urens fibers having diameter of 8 microns and thickness of 0.2 mm reinforced with polyester resin of density of 1.1 g/cm^{3} and viscosity of 700 Centipoise as the matrix material manufactured by stir casting technique. Table 3 shows the mechanical properties of DRCUP composites. Figure 4 shows the SEM image of DRCUP composites. Figure 5 shows the Talysurf Surtronic 3+ surface roughness measurement tester used for analyzing of machined surface under different cutting conditions.
Fig. 1 Experimental setup. 
Specifications of abrasive water machining.
Fig. 2 Silicon carbide. 
Properties of silicon carbide abrasive grains.
Mechanical properties of DRCUFP composites (Raviraj Shetty et al., 2018).
Fig. 4 SEM image of DRCUFP composites. 
Fig. 5 Surface roughness measurement. 
2.1 TDOE
Taguchi techniques one of the methodology based on design of experiment that has been widely used in manufacturing, metal cutting and engineering applications. The experimentation is carried out using smaller the better characteristics obtained from Taguchi's L_{27} (3^{13}) orthogonal array. Table 4 shows the Levels and Factors used in this experimentation.where n is the number of observations, and y is the observed data.
Levels and factors.
2.2 Fuzzy logic model
In metal cutting industries mathematical and empirical modelling developed for predicting machining parameters became very complicated and time consuming. Hence fuzzy logic became a very effective tool to solve incomplete and imprecise information in various engineering fields. Fuzzy logic mainly deals with mathematical theory and probability theory. Fuzzy logic variables are tested with IFTHEN rules. The fuzzy decision making unit is shown in Figure 6.
In this paper, typical fuzzy logic model has been successfully used to analyse the surface roughness during machining of Ti6Al4V under MQL condition. Figure 7 demonstrates the factors used for predicting the surface roughness (microns) under different cutting conditions as information parameters. Table 5 shows the fuzzy design matrix of input and output parameters selected for experimentation.
The fuzzy standards were produced by TDOE. Linguistic factors like low, medium, and high are utilized for input parameters such aswater pressure, traverse speed, stand of distance, abrasive flow rate, abrasive size and output parameter, i.e., surface roughness are shown in Table 5. Membership functions of input and output parameters are presented in Figures 8–14. Mamdani fuzzy inference system has been used to find the better accuracy of output of the i.e., surface roughness during AWJM of DRCUFP composites. Seven membership functions have been selected for the output i.e., extremely low, very low, low, medium, high, very high and extremely high as shown in Figure 5.
Fig. 6 Fuzzy decision making unit. 
Fig. 7 Fuzzy inference system. 
Fuzzy design matrix.
Fig. 8 Fuzzification of Input parameter water pressure (bar). 
Fig. 9 Fuzzification of Input parameter abrasive flowrate (microns). 
Fig. 10 Fuzzification of Input parameter traverse speed (mm/min). 
Fig. 11 Fuzzification of Input parameter depth of cut (mm). 
Fig. 12 Fuzzification of Input parameter abrasive size (microns). 
Fig. 13 Fuzzification of Input parameter stand of distance (mm). 
Fig. 14 Fuzzification of output parameter surface roughness (microns). 
3 Results and discussions
Discontinuously Reinforced Caryota Urens Fiber Polyester (DRCUFP) composites due its anisotropic behaviour and unusual machinability responses such as fiber pullout, delamination and poor surface finish has made to introduce Taguchi based fuzzy logic model for optimisation and prediction of surface roughness during AWJM of DRCUFP composites.
3.1 Effect of process parameters on surface roughness using TDOE
The data means of main effects plot obtained from TDOE was to examine the effects of selected set of process input parameters significantly influencing the process output parameter i.e., surface roughness during AWJM of DRCUFP composites. From main effects plot for means for surface roughness (Fig. 15) the optimum cutting conditions for minimum surface roughness can be established at, water pressure (A): 300 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 12 g/s, depth of cut (C): 5 mm and abrasive size: 200 microns. Table 6 shows the response table surface roughness. Table 7 shows thepredicted values of surface roughness using TDOE. Figure 8 shows the SEM image of machined surface.
Fig. 15 Main effects plot of output parameter surface roughness (microns). 
Inputs and outputs to fuzzy logic modeling.
Response table surface roughness.
Fig. 16 SEM image of machined surface (a) 100 bar; (b) 200 bar; (c) 300 bar. 
3.2 Effect of process parameters on surface roughness using FLM
Fuzzy logic model has been developed using MATLAB version R2007b mamdani fuzzy expert system. Triangular membership functions are used for input variables and Gaussian membership functions are used for output variable. The rules applied are represented in the form of IF–THEN conditional statements (Ravindra et al., 2018). Figure 17 shows the fuzzy rule editor.
Figure 18 represents the effect of water pressure and traverse speed on surface roughness during AWJM of DRCUFP composites. Initially during machining, surface roughness was very high (3.36 microns) at 100 bar water pressure. As the water pressure increased to 300 bar, surface roughness gradually reduced to 3.26 microns. This is because as the AWJ pressure increases the fibers break down into smaller ones and kinetic energy of the fibers increases which results in smoother machined surface for all the conditions. Traverse speed showed a small variation in surface roughness value this is because at lower traverse speed there is more contact between the abrasive impingement and the workpiece which resulted in smoother finish at 50 mm/min traverse speed.
Figure 19 represents the effect of water pressure and stand of distance on surface roughness during AWJM of DRCUFP composites. Initially during machining, surface roughness was very high (3.38 microns) at 100 bar and low at stand of distance 1 mm. As the water pressure increased to 300 bar the surface roughness value improved. But in case of increase in stand of distance to 4 mm, surface roughness gradually deteriorated to 3.38 microns. This is because at higher stand of distance the jet expands at the nozzle exit which leads for decrease in kinetic energy which results in irregular surface.
Figure 20 represents the effect of water pressure and abrasive flow rate on surface roughness during AWJM of DRCUFP composites. Initially during machining, abrasive flow rate gave a constant surface roughness value (ranging from 3.25 microns to 3.26 microns). This clearly implies that there was no much effect of abrasive flow rate on surface roughness.
Figure 21 represents the effect of water pressure and depth of cut on surface roughness during AWJM of DRCUFP composites. Initially during machining, abrasive flow rate gave a constant surface roughness value (ranging from 3.25 microns to 3.26 microns). This clearly implies that there was no much effect of abrasive flow rate on surface roughness.
Figure 22 represents the effect of water pressure and abrasive size on surface roughness during AWJM of DRCUFP composites. Here during machining, abrasive size gave much variation in surface roughness value (ranging from 3.29 microns to 3.34 microns). This clearly implies that stable jet induced due high energy small particles resulting in secondary cutting operation that improves the surface quality. Whereas, as the particle size increases there is decrease in energy, resulting in scratches on Kerf walls and deteriorates the surface finish.
From the analysis for surface roughness (microns) generated for minimum and maximum values of input/output parameters for different cutting parameters based on fuzzy rule based model are shown in Table 8 which clearly indicates that at minimum water pressure (A): 100 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 8 g/s, depth of cut (C): 5 mm and abrasive size: 100 micronsgave higher surface roughness values (3.47 microns) than that at maximum water pressure (A): 300 bar, traverse speed (B): 150 mm, stand of distance: 4 mm, abrasive flow rate: 12 g/s, depth of cut (C): 15 mm and abrasive size: 200 microns the surface roughness values (3.25 microns).
From the comparison plots for TDOE and FLM for surface roughness (Fig. 23) validated for 27 sets of experiments it was observed that TDOE value were almost nearer to the FLM value.
Fig. 17 Fuzzy rule editor. 
Fig. 18 Effect of water pressure and traverse speed on surface roughness. 
Fig. 19 Effect of water pressure and stand of distance on surface roughness. 
Fig. 20 Effect of water pressure and abrasive flow rate on surface roughness. 
Fig. 21 Effect of water pressure and depth of cut on surface roughness. 
Fig. 22 Effect of water pressure and abrasive size on surface roughness. 
Predicted values of surface roughness using TDOE.
Minimum and maximum values of input/output parameters.
Fig. 23 Comparison plots for TDOE and FLM for surface roughness (microns). 
4 Conclusions
In this paper Taguchi based fuzzy logic model for optimisation and prediction of surface roughness during AWJM of DRCUFP composites have been investigated. From the analysis of the development and application of the TDOE and FLM following conclusion can be drawn:
From TDOE the optimum cutting conditions for minimum surface roughness can be established at water pressure (A): 300 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 12 g/s, depth of cut (C): 5 mm and abrasive size: 200 microns.
From FLM it is observed that minimum water pressure (A): 100 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 8 g/s, depth of cut (C): 5 mm and abrasive size: 100 microns gave higher surface roughness values (3.47 microns) than that at maximum water pressure (A): 300 bar, traverse speed (B): 150 mm, stand of distance: 4 mm, abrasive flow rate: 12 g/s, depth of cut (C): 15 mm and abrasive size: 200 microns the surface roughness values (3.25 microns).
From the comparison plots for TDOE and FLM for surface roughness validated for 27 sets of experiments it was observed that TDOE value were almost nearer to the FLM value.
In addition, DRCUFP composites are sustainable and could be fully recyclable, but could be more expensive if fully natural based and biodegradable and they are extremely sensitive to moisture and temperature. If a proper matrix is used, DRCUFP composites could be 100% biodegradable.
References
 K. Pickering, Properties and Performance of NaturalFibre Composites, CRC Press LLC, Boca Raton, 2008 [Google Scholar]
 C. Baley, F. Busnel, Y. Grohens, O. Sire, Influence of chemical treatments on surface properties and adhesion of flax fibrepolyester resin, Compos. Part A: Appl. Sci. Manufactur. 37 (2006) 1626–1637 [CrossRef] [Google Scholar]
 D.B. Dittenber, H.V.S. GangaRao, Critical review of recent publications on use of natural composites in infrastructure, Composites Part A 43 (2012) 1419–1429 [CrossRef] [Google Scholar]
 R. Joffe, J. Andersons, L. Wallström, Strength and adhesion characteristics of elementary flax fibres with different surface treatments, Composites Part A 34 (2003) 603–612 [CrossRef] [Google Scholar]
 A.K. Bledzki, V.E. Sperber, O. Faruk, Natural and wood fibre reinforcement in polymers, Rapra Review Reports. 13 (2002) 1 [Google Scholar]
 J.L. Broge, Natural fibers in automotive components, Automot. Eng. Int. 108 (2000) 120 [Google Scholar]
 C. Clemons, Woodplastic Composites in the United States: The interfacing of two Industries. , For. Prod. J. 52 (2002) 10–18 [Google Scholar]
 M. Karus, M. Kaup, S. Ortmann, Use of natural fibres in the German and Austrian automotive industry. Market survey, Status, analysis and trends. NovaInstitute GmbH, Hürth, Germany. (2002) [Google Scholar]
 P. Kandachar, R. Brouwer, Applications of BioComposites in Industrial Products. , Mater. Res. Soc. Symp. Proc. 702 (2002) 101–112 [Google Scholar]
 J. Biagiotti, D. Puglia, J.M. Kenny, A review on natural fibrebased composites − Part I: Structure, processing and properties of vegetable fibres, J. Natl. Fibers 1 (2004) 37–68 [CrossRef] [Google Scholar]
 H.L. Bos, K. Molenveld, W. Teunissen, A.M. Van Wingerde, D.R.V. Van Delft, Compressive behaviour of unidirectional flax fibre reinforced composites, J. Mater. Sci. 39 (2004) 2159–2168 [CrossRef] [Google Scholar]
 H.L. Bos, M. Van Den Oever, O. Peters, Tensile and compressive properties of flax fibres for natural fibre reinforced composites, J. Mater. Sci. 37 (2002) 1683–1692 [CrossRef] [Google Scholar]
 S. Chapple, R. Anandjiwala, Flammability of natural fiberreinforced composites and strategies for fire retardancy: a review, J. Thermoplast. Compos. Mater. (2010) 871–893 [CrossRef] [Google Scholar]
 C. Gourier, A. Le Duigou, A. Bourmaud, C. Baley, Mechanical analysis of elementary flax fibre tensile properties after different thermal cycles, Composites Part A 64 (2014) 159–166 [CrossRef] [Google Scholar]
 M. Hughes, J. Carpenter, C. Hill, Deformation and fracture behaviour of flax fibre reinforced thermosetting polymer matrix composites, J. Mater. Sci. 42 (2007) 2499–2511 [CrossRef] [Google Scholar]
 R. Joffe, J. Andersons, L. Wallström, Strength and adhesion characteristics of elementary flax fibres with different surface treatments, Composites Part A 34 (2003) 603–612 [CrossRef] [Google Scholar]
 P. Wambua, J. Ivens, I. Verpoest, Natural fibres: can they replace glass in fibre reinforced plastics? Compos. Sci. Technol. 63 (2003) 1259–1264 [CrossRef] [Google Scholar]
 L. Yan, N. Chouw, K. Jayaraman, Flax fibre and its composites − a review, Compos. Part B 56 (2014) 296–317 [CrossRef] [Google Scholar]
 M. Ramesh, K. Palanikumar, K.H. Reddy, Mechanical property evaluation of sisaljuteglass fiber reinforced polyester composites, Composites Part B 48 (2013) 1–9 [CrossRef] [Google Scholar]
 R. Shetty, R. Pai, A.B.V. Barboza, V. Pankaj Kumar Gandhi, Processing, mechanical charaterization and its tribological study of discontinously reinforced caryota urens fibre polyester composites, ARPN J. Eng. Appl. Sci. 13 (2018) 3920–3928 [Google Scholar]
 R. Shetty, R. Nayak, A.B.V. Barboza, Mechanical and machinability study of discontinuously reinforced sisal fiber polyester composites, Mater. Res. Express 6 (2019) 105370 [CrossRef] [Google Scholar]
 R. Shetty, R. Pai, S.S. Rao, V. Kamath, Machinability study on discontinuously reinforced aluminium composites (DRACs) using response surface methodology and Taguchi's design of experiments under dry cutting condition, Maejo Int. J. Sci. Technol. 2 (2008) 227–239 [Google Scholar]
 R. Shetty, R.B. Pai, S.S. Rao, R. Nayak, Taguchi's technique in machining of metal matrix composites', J. Br. Soc. Mech. Sci. Eng. 31 (2009) 12–20 [Google Scholar]
 G.D. Revankar, R. Shetty, S.S. Rao, V.N. Gaitonde, Selection of optimal process parameters in Ball Burnishing of Titanium Alloy, Mach. Sci. Technol. 18 (2014) 464–483 [CrossRef] [Google Scholar]
 A. Momber, R. Kovacevic, Principles of Abrasive Waterjet Machining. SpringerVerlag, London, 1998 [CrossRef] [Google Scholar]
 J.O. Obiko, F.M. Mwema, M. Oluwatosin Bodunrin, Validation and optimization of cutting parameters for Ti6Al4V turning operation using DEFORM 3D simulations and Tagucci method, Manufactur. Rev. 8 (2021) 5 [CrossRef] [EDP Sciences] [Google Scholar]
 J.O. Obiko, C. Obara, F.M. Mwema, J.N. Keraita, H. Shagwira, A multiresponse optimization of the multidirectional forging process for aluminium 7075 alloy using greybased taguchi method, SN Appl. Sci. Spri. Inter. 3 (2021) 1–20 [CrossRef] [Google Scholar]
 M. Poovalingam, Multi objective optimization of wear behaviour of Aluminum MMCs using GreyTaguchi method, Manufacturing Rev. 7 (2020) 16 [CrossRef] [EDP Sciences] [Google Scholar]
 S.K. Choudhary, R.S. Jadoun, Optimization of EDM process parameters for MRR of Inconel 600 using Taguchi method, Int. J. Mech. Prod. Eng. Res. Dev. 10 (2020) 11481–11492 [Google Scholar]
 P.B. Patole, V.V. Kulkarni, Optimization of Process Parameters based on Surface Roughness and Cutting Force in MQL Turning of AISI 4340 using Nano Fluid, Mater. Today: Proc. 5 (2018) 104–112 [CrossRef] [Google Scholar]
 M. Chithirai Pon Selvan, N. Mohana Sundara Raju, Assessment of process parameters in abrasive waterjet cutting of stainless steel, Int. J. Adv. Eng. Technol. 1 (2011) 34–40 [Google Scholar]
 M. Chithirai Pon Selvan, N. Mohana Sundara Raju, Effects of process parameters on depth of cut in abrasive waterjet cutting of cast iron, Int. J. Sci. Eng. Res. 2 (2011) 1–5 [Google Scholar]
 P.P. Shirpurkar, S.R. Bobde, V.V. Patil, B.N. Kale, Optimization of turning process parameters by using tool inserts − a review, Int. J. Eng. Innov. Technol. 2 (2012) 216–223 [Google Scholar]
 R. Malagi Ravindra, S.R. Chougula, R. Shetty, Prediction of cutting force in turning of Ti6Al4V under minimum quantity lubrication (MQL) using response surface model and fuzzy logic model, Int. J. Mech. Product. Eng. Res. Dev. 8 (2018) 263–274 [CrossRef] [Google Scholar]
Cite this article as: Raviraj Shetty, Adithya Hegde, Taguchi based fuzzy logic model for optimisation and prediction of surface roughness during AWJM of DRCUFP composites, Manufacturing Rev. 9, 2 (2022)
All Tables
All Figures
Fig. 1 Experimental setup. 

In the text 
Fig. 2 Silicon carbide. 

In the text 
Fig. 3 Caryota urens fibers [19]. 

In the text 
Fig. 4 SEM image of DRCUFP composites. 

In the text 
Fig. 5 Surface roughness measurement. 

In the text 
Fig. 6 Fuzzy decision making unit. 

In the text 
Fig. 7 Fuzzy inference system. 

In the text 
Fig. 8 Fuzzification of Input parameter water pressure (bar). 

In the text 
Fig. 9 Fuzzification of Input parameter abrasive flowrate (microns). 

In the text 
Fig. 10 Fuzzification of Input parameter traverse speed (mm/min). 

In the text 
Fig. 11 Fuzzification of Input parameter depth of cut (mm). 

In the text 
Fig. 12 Fuzzification of Input parameter abrasive size (microns). 

In the text 
Fig. 13 Fuzzification of Input parameter stand of distance (mm). 

In the text 
Fig. 14 Fuzzification of output parameter surface roughness (microns). 

In the text 
Fig. 15 Main effects plot of output parameter surface roughness (microns). 

In the text 
Fig. 16 SEM image of machined surface (a) 100 bar; (b) 200 bar; (c) 300 bar. 

In the text 
Fig. 17 Fuzzy rule editor. 

In the text 
Fig. 18 Effect of water pressure and traverse speed on surface roughness. 

In the text 
Fig. 19 Effect of water pressure and stand of distance on surface roughness. 

In the text 
Fig. 20 Effect of water pressure and abrasive flow rate on surface roughness. 

In the text 
Fig. 21 Effect of water pressure and depth of cut on surface roughness. 

In the text 
Fig. 22 Effect of water pressure and abrasive size on surface roughness. 

In the text 
Fig. 23 Comparison plots for TDOE and FLM for surface roughness (microns). 

In the text 
Current usage metrics show cumulative count of Article Views (fulltext article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 4896 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.