Engineering, Technology & Applied Science Research Vol. 8, No. 3, 2018, 2931-2936 2931 www.etasr.com Boujelbene: Process Capability and Average Roughness in Abrasive Water Jet Cutting Process of … Process Capability and Average Roughness in Abrasive Water Jet Cutting Process of Stainless Steel Mohamed Boujelbene College of Engineering of Hail University of Hail Hail, Kingdom of Saudi Arabia mboujelbene@yahoo.fr Abstract—Process capability analysis is frequently employed to evaluate if a product or a process can meet the customer’s requirement. In general, process capability analysis can be represented by using the process capability index. Until now, the process capability index was frequently used for manufacturing processes with quantitative characteristics. However, for a process with qualitative characteristic like cutting surface, the data’s type and single specification caused limitations of using the process capability index. Taguchi developed a surface quality by abrasive water jet cutting or quadratic quality loss function to address such issues. In this study, we intend to construct a measurable index which incorporates the process capability index philosophy concept to analyze the process capability with the consideration of the qualitative surface roughness. The manufacturers can employ the proposed index to self-assess the process capability. The objective of this study was to examine the effects of abrasive water jet machining variables like cutting speed of the stainless steel material. The roughness of the varied surface through the cut depth was also measured and determined as a process capability index of 3 zones machined surface. Keywords-abrasive water jet cutting; process capability; cutting speed; surface roughness; stainless steel I. INTRODUCTION The abrasive water jet (AWJ) cutting technique is one of the most rapidly improving technological methods of cutting materials. In this cutting technique, a thin, high velocity water jet accelerates abrasive particles that are directed through an abrasive water jet nozzle at the material to be cut. AWJ is one of the most widely used technological methods. The advantages of AWJ cutting include the possibility of cutting almost all materials e.g. Titanium, Aluminum, the absence of thermal distortion, high flexibility, small cutting forces and being environmentally friendly. Due to these capacities, this cutting technique is more cost-effective than traditional and non-traditional machining processes [1-9]. The mechanism and rate of material removal during the AWJ cut depends on both the type of abrasive and the range of process parameters. A considerable number of studies have investigated the effects of cutting velocity, spreading distance, water pressure, abrasive grain size and other factors on the surface roughness [6-12]. Thus, it is necessary to have a deeper knowledge of the optimal conditions of operation, which will allow us to ensure a good surface roughness. A large amount of research effort has been made, in recent years, to understand the AWJ process and improve its cutting performance such as the depth of cut and surface finish for various materials [11-12]. Researchers used granite samples for their experimental studies and investigated the effect of process parameters on rock cutting. It was found that entraining of abrasive particles increase the cutting capability of water jets and increases of water jet pressure allow obtaining deeper cut depths. Process capability analysis (PCA) [13-16] is frequently employed by the manufacturers to evaluate if the capability of process can meet the customer’s requirement. Process Capability Indices (PCIs) are a quantitative measurement of the process capability in most manufacturing industries. PCIs, such as Cp and Cpk are commonly used for most manufactures [15–16], can frequently measure the process capability for the quantitative response for example surface roughness. Authors in [15] evaluate the related scale of the process mean with the tolerance specification (i.e. the difference between the upper tolerance limit and the lower tolerance limit). Cp evaluates the related scale of the specification’s tolerance with process’s tolerance. While Cpk simultaneously, evaluates the centering degree and the dispersion degree. These PCIs will make some adjustments if there are necessary particulars like the unilateral specification. For the quantitative type, the theories on PCA and PCIs are well developed in [15-17] but qualitative data type may exist during the manufacturing environment, e.g. the production parts, pistons, gears, the integrated circuit manufacturing, so, the process capability analysis for qualitative data will be an important issue to study. However, most studies only focus on the PCA application for the quantitative response data, and the qualitative response data is seldom mentioned [16-17]. Several difficulties can be mentioned as: (i) the target of the qualitative data may lead to unobvious centering evaluation, e.g. the target will be set as zero defect, (ii) the limitation of the unilateral specification, especially only the upper specification exists, e.g. the defect rate may be less than 1% and (iii) the quantitative data utilizes the process mean (µ) and process deviation (σ) to compute the PCIs, however, the qualitative data cannot directly utilize them to compute the PCIs. Under the global market environment, to realize the process capability comparison with other competitors can provide helpful inf ma eng hop pla [18 int low qu of cus nu pur req val ref con pro qu ma sho tha a t pro pro pro bei tig the pro int the bet val cos ens cus reg qu wa spe rou thr pro ma to the Th spe cut A. fro sta Ta Engineerin www.etasr formation for aking strategic gineering has pe to achiev anning, side by 8-22]. The go troducing the west producti ality. Hence, w the produc stomer’s qual mber of produ rchase contra quirement [14] lue which ref ference for ntrollers to acq ocesses [14, 19 ality measur anagement, se ould be addres an at the produ The PCI valu task or to ach ocess mean an ocess mean an ocess mean cl ing incurred. ghtening the pr e conventiona oduct and pr tend to raise th e target value, tter product q lue can easily sts that result suring toleran stomer deman gard, the off-li ality loss and as to examine eed of the stai ughness Ra. 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MATERI l used in this e 6CrNiTi18-10 nd its nominal steel is used y & Applied Sci Boujel organizationa Now, as the con dely accepted us product d arly stage of pr orten the time to the marke manufacturin d is a way to capability, nt. More imp this measurem ustomers, as measurement is e quality statu onitoring that grasp of the qu gh PCI is cons ployed during s have pointed ginning of the where PCA is t defined as the The controlla iance [16]. The et can be redu sign target with s variance ca ce, with extra c CI is used fo ns, designer e by locating the ing the toleran ever, simply onal and unne efforts and exp Hence, there is ty and produc is introduced, cost. The obje of AWJ variab material on a su roughness of also measured ally, the PC ficant factor f nd to construc ned by the AW to examine th across the cu nless steel. ALS AND METH experimental st 0. 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In , in considerat ective of this bles such as c urface with av the varied su d and expresse CA for dif for the collabo ct a PCI to ev WJ cutting pr he effect of c ut surface in HODS tudy has been gly allied aust mposition is giv ustrial sectors h V Capability and ce or current gineers rocess pment ed for in the good degree g the owing n their ntation ch is a as the rocess onsite of the quality e PCI rather . rry out re the etween ng the al cost ed by When ng the turally n near nsure a e PCI uction es for alance n this tion of study cutting verage urface ed as a fferent orators valuate rocess. cutting AWJ taken tenitic ven in s, like aero is a B. cutt ultr max exp the stai C. mea cap incl be p leng resp zon eva Fig Vol. 8, No. 3, 20 Average Rough onautic, chem also used in va Experiments M The experime ting system w ra-high pressu ximum water perimental stud AWJ Machin inless steel of Fig. TABLE I. C S 0.06 1 TABLE II. T Surface Roug The average asured by a pable of perf luding vertica performed eas gth and evalu pectively. The nes of the mac aluated. Surfac gure 2. Fig. 2. 018, 2931-2936 hness in Abras mical, electrical arious mechani Machine Tool ents were carri with a KMT S ure pumps SL r pressure of dy, there are 6 ne (AWJM) pr 8mm depth sh 1. Experimen CHEMICAL COMP Si Mn .00 2.00 0 CONSTANT T Technical Param Cutting lengt Water pressu Type of abrasi Density of the ab Abrasive grain Nozzle diame ghness Measur surface rough Mitutoyo po forming mea al and upside-d sily in various uation length e surface rou chined surface ce roughness m Experimenta 6 ive Water Jet C l, navy, nuclea ical pieces man l ied out on an Streamline TM L-V 50 capab f 600MPa (F 6 constant para rocess to cut a hown in Table ntal AWJ cutting s POSITION IN % OF P S 0.045 0.015 TECHNOLOGICAL meters Valu th 150 m ure 415 M ive Miner rasive 0.5 g/ size 80 g ter 0,76 m rement hness (Ra) of ortable surface asurements in down, allowin situations and were fixed at ughness was e. All measur measurements al surface roughne 2932 Cutting Process ar and petroleu nufacturing. NC 3015 EB M system [2] ble of providi Figure 1). In ameters throug a sheet metal o II. system. THE X6CRNITI18- Cr Ni 18 10 PARAMETERS ue mm MPa ral /l g mm the workpiece e roughness n any orienta ng measuremen d setups. The c t 0.8mm and measured in ement values s tester is show ess tester. s of … um. It AWJ with ing a n this ghout of the 10 e was tester ation, nts to cutoff 4mm three were wn in Engineering, Technology & Applied Science Research Vol. 8, No. 3, 2018, 2931-2936 2933 www.etasr.com Boujelbene: Process Capability and Average Roughness in Abrasive Water Jet Cutting Process of … III. EXPERIMENTAL RESULTS AND DISCUSSION A. Surface Roughness of the cut surface After machining operations by AWJ cutting process, the cut surface was monitored by optic microscope and is presented in Figure 3 which shows the very good surface cut of the upper edge beginning of the cut (zone 1) and the bad surface machined in the lower edge, ending of the cut (zone 3). In this zone there is a presence of the striation marks (Figure 3(b)). (a) (b) Fig. 3. Photograph of machined surface after AWJ Cutting (a) Good surface at low cutting speed, (b) Coarse surface at high cutting speed. Obtained photographs were analyzed and edited with the use of image manipulation software. Cut surfaces were divided in two zones; upper zone (beginning of the cut with no visible presence of machining marks), and lower zone, (ending of the cut with visible machining marks) as shown in Figure 3(b). Lines outlining machining marks and showing their approximate curve angle were added. For the surface cut with the highest cutting speed V equal to 250mm/min, numerous grooves and elevations in the lower zone are clearly visible marks (Figure 3(b)). With decrease in the cutting speed an improvement of surface quality in its lower part can be observed. For the lowest used cutting speed, machining marks are fewer and faintly visible. It can be observed that the width of the zone with visible machining marks and their curve angle increases with the growth in cutting speed. The presence of machining marks in the lower part of cut surfaces is linked to the decrease in kinetic energy of abrasive particles in AWJ. After machining by AWJ cutting process, with different cutting speeds, we measured the surface average roughness Ra in three zones (see Figure 4). Table III presents Ra of the all work pieces, in the three zones. It was observed that the sensitivity of measured parameters is directly related to both cutting speed and distance from upper cut edge. With the increase in cutting speed V, a degradation of surface quality defined by analyzed parameters for zone 2 and zone 1 planes was observed (Figures 3 and 5). Based on the analysis of Table III and Figure 5, it can be stated that the cutting speed V, has a significant influence on the surface roughness of the cut surfaces. Also, the distance from the upper cut surface edge directly affects surface quality and measurement results. In the area where AWJ enters the cut material, decrease in cutting speed by around 20% (from V=250mm/min to 200mm/min) results in drop of roughness parameter by approximately 14% in zone 3. Further decrease of the cutting speed to 150mm/min (decrease by approximately 40%) results in surface roughness parameters dropping by 26% in zone 3. Fig. 4. Measurement zones of Ra of the machined surface. A downward trend can also be seen when analyzing the measurement results taken in the center of cut surfaces. However, this time the drop is more significant by 20% for the medium cutting speed and 40% for the lowest used cutting speed. In the lower part of the studied cut surfaces the sharpest growth in values of measured parameters can be seen for the highest used cutting speed of 250mm/min. For the lower cutting speeds, 150mm/min, the increase in roughness is not as intense when compared to the values observed for the upper, parts of cut surfaces (zone 1). This can be caused by the drop in the kinetic energy of abrasive water jet being less intense for lower cutting speeds. In this part of the surface, biggest increases in surface quality can be achieved by using the lowest researched cutting speed of 150mm/min. Figure 5 shows an excellent surface roughness of the machined surface in the zone 1, and a bad surface quality in the zone 3, for cutting speed of 250mm/min. IV. PROCESS CAPABILITY A. Process Capability Process capability Cp and PCI Cpk are considered short- term potential capability measures for a process. In Six sigma, we want to describe processes quality in terms of sigma because this gives us an easy way to talk about how capable different processes are by using a common mathematical framework. In other words, it allows us to compare, for AWJ Zone 1 Zone 3 Zone 2 Measurement zones of Ra exa ana clu hav lan TA con ma aw wh oth but tol In wi wit det wit sta pro sta cla is pra sta dev low cap pro me Engineerin www.etasr ample, millin alogy is shoo uster or groupi ve a high Cp nding on the bu ABLE III. SU Exp N° V 1 2 3 4 5 6 7 8 9 Cpk measure nsistent you a ay be perform way from his t hich indicates her hand, a pe t the variation lerance band ( such case Cpk ll be higher on th minimum v As a quanti termine wheth thin customer atistical measu ocess specifica andard of pro assical method usually assum actical cases, able univariate viation σ, if w wer specifica pability (Cp) ocesses (Cpk easures that ar Cpk M= ng, Technology r.com ng processes oting at a targ ing in the same value. When ullseye, you n URFACE ROUGHNE V (mm/min) de 150 150 150 200 200 200 250 250 250 s how close are around you ming with min target towards lower Cpk, w erson may be n in performan (i.e., specificat k will also be nly when you’ variation [18]. itative measu her a process r specification ures is to estim ations. Further oduct quality ds, while norm med in the esti some non-no e normal proc we define US ation limits, and the proce k), defined in e inappropriat Cp = Cp Cp Min Cpu μ =  y & Applied Sci Boujel to turning p get. If the ro e spot anywhe you have a t ow have a hig ESS AFTER MACHIN epth (mm) 8 Zo 8 Zo 8 Zo 8 Zo 8 Zo 8 Zo 8 Zo 8 Zo 8 Zo you are to yo ur average perf nimum variati s one of the s whereas Cp w on average ex ce is high (but tion interval, f e lower, but Cp ’re meeting the ure, PCIs are s is capable o n limits. The mate process v rmore, a PCI p to suppliers mality of the qu imation proce ormal distribu cess with me SL and LSL to respectively, ess capability n (1) to (4), te for non-norm 6 USL LSL σ − = 3 USL l μ σ − = 3 LSL u μ σ − = , 3 LSL Cpl μ σ − = ience Research lbene: Process processes. A ounds form a ere on the targe tight group of gh Cpk. NING BY AWJ CUT Ra (µm) one 1 2.71 one 2 3.69 one 3 5.36 one 1 2.98 one 2 3.93 one 3 6.28 one 1 3.21 one 2 5.26 one 3 7.27 our target and formance. A p ion, but he c specification l will be high. O xactly at the t t still lower th for example ± p will be high e target consis e widely use of producing objective of ariability relat provides a com and custome uality characte ess of PCI, in utions occur. ean μ and sta o be the uppe then the pr ratio for off- , are the cla mal processes. 3 USL μ σ −    h V Capability and good good et you f shots TTING. d how person can be limits, On the target, han the ±0.15). h. Cpk stently ed to items these tive to mmon ers. In eristics many For a andard er and rocess center assical . (1) (2) (3) (4) esti dev ava nor mo cha mu B. F Ra, surf are the lim The mac and con very pro spe Ra cap an rou con con exp pro pre imp (Fig F zon num cap pro of US the Vol. 8, No. 3, 20 Average Rough In which, on imates defined viation S, re ailable in the rmal processe re than one aracteristics a ltivariate techn Fig. 5. Surf Results of the For analyzing , in three zon face by AWJ presented in T AWJC proces mit LSL 2.8µm e process cap chined surfac d Cpk=1.74> nsidered to be ry good proce ocess is contai ecifications. Fu in zone 2 (Ra pability Cp=5.9 excellent pro ughness value nforming prod ntrol. The proc perience least oduct. The proc sented potent plying that we gure 8). So we Figure 8 show ne 3, which in mber of non-c pability is ina ocess mean is n non-conformin L is 731642.4 observed perf 018, 2931-2936 hness in Abras ne can replac d as the samp espectively. A literature to es, in many p e quality ch are generally niques should face roughness Ra e Process Capa the process ca nes (zone 1- cutting (AW Table IV and ss of the stain m and upper pability report e presented th 1.33 (Cpk v e industry ben ss in zone 1 ined within sta urthermore, th a-z2) of mach 94 and Cpk=5 ocess in zone es, in zone ducts as long cess owner can difficulty an cess capability tial capability e have a bad o e have to choo ws that the pr ndicates the po conforming pa dequate. For not centered a ng parts out 49 and there i formance (Figu 6 ive Water Jet C ce unknown ple mean x an Although vari estimate PCI production pr haracteristics correlated, be employed. a as funtion of the ability apability of the 3) (Ra-z1-3), WJC). The resu Figures 6-8. T nless steel has specification t for Ra in z he potential c values of 1.3 nchmarks), im (Figure 6). T andard deviati he process ca hined surface p 5.24>1.33, imp e 2 (Figure 1 and zone g as they rem n claim that th nd greater re y report for Ra Cp=6.63 and or not adequate ose another pro rocess capabil otential risk of arts. It denote a Cp differen at the process w of the upper s a non value ure 8). 2934 Cutting Process μ and σ by nd sample stan ious methods of univariate rocesses, there involved. T and hence, e cutting speed V e surface rough of the mach ults of the me The process da lower specific limit USL 7. zone 1, (Ra-z capability Cp= 33 or greater mplying we ha his means tha ions of the pr apability repor presented pote plying that we 7). These su 2, will pro main in stati he customer sh eliability with a in zone 3 (R d Cpk=-0.20< e process in zo ocess for this c lity deteriorat f an increase i es that the pr nt to the Cpk width. The nu specification out of the LS s of … their ndard s are e non e are These some V. hness hined easure ata of cation 2µm. 1) of =6.38 r are ave a at the rocess rt for ential have urface oduce istical hould h this Ra-z3) <1.33, one 3 case. ed in in the rocess k, the umber limit SL in Engineering, Technology & Applied Science Research Vol. 8, No. 3, 2018, 2931-2936 2935 www.etasr.com Boujelbene: Process Capability and Average Roughness in Abrasive Water Jet Cutting Process of … TABLE IV. SURFACE ROUGHNESS RA IN THE 3 ZONES Exp N° V (mm/min) Ra-z1 (µm) Ra-z2 (µm) Ra-z3 (µm) 1 150 2.58 4.02 5.3 2 150 2.79 3.3 5.7 3 150 2.81 3.78 5.0 4 150 2.66 3.9 5.6 5 150 2.77 3.42 5.11 6 150 2.63 3.74 5.46 1 200 2.93 3.8 6.32 2 200 2.98 3.78 6.23 3 200 3.13 3.99 6.14 4 200 3.02 4.01 6.41 5 200 2.87 4.11 6.17 6 200 2.94 3.89 6.39 1 250 3.44 5.24 7.31 2 250 3.48 5.18 7.22 3 250 3.33 5.46 7.14 4 250 3.22 5.25 7.41 5 250 3.52 5.31 7.17 6 250 3.41 5.12 7.34 Fig. 6. Process capability report for Ra in zone 1 of the cut surface. Fig. 7. Process capability report for Ra in zone 2 of the cut surface. Fig. 8. Process capability report for Ra in zone 3 of the cut surface. V. CONCLUSION In this study, we constructed a quantitative measurement PCI for the qualitative response of the surface roughness. The quantitative measurements are based on the Taguchi’s quality function philosophy and PCI concept. It is a ratio deriving from the customer’s quality loss with respect to the actual process’s quality loss. By employing the proposed PCI, the manufacturers can assess and meet the customer’s requirement. The analysis of the machined surface by AWJC process extracted the following conclusions: • Edge quality of the cut surface is a function of cutting speed. • With decrease in cutting speed, cut surface quality visibly improves, which is most clearly noticeable for the lower part of examined cut surfaces. The difference in the measured value of Ra parameter is about 26% between the highest and lowest researched cutting speeds, in favor of the latter. • Cut surfaces are characterized by the occurrence of two zones. In the first zone, there are no visible machining marks. In the second one, machining marks can be easily observed. The second zone width and the visibility of machining marks is closely related to the cutting speed. • Results of this research can have a practical use in determining surface roughness parameters best suited to adequately evaluate cut surfaces of elements machined with the use of AWJ. • This process will produce conforming products, in zone 1 and zone 2, as long as it remains in statistical control. • The process capability report for Ra in zone 3, gives a bad or not adequate process in this zone, So, a new process must be chosen. REFERENCES [1] A. Akkurt, “Cut front geometry characterization in cutting applications of brass with abrasive water jet”, Journal of Materials Engineering and Performance, Vol. 19, No. 4, pp. 599–606, 2010. 7,26,66,05,44,84,23,63,0 LSL 2,8 Target * USL 7,2 Sample Mean 3,4 Sample N 6 StDev(Overall) 0,109362 StDev(Within) 0,114932 Process Data Pp 6,71 PPL 1,83 PPU 11,58 Ppk 1,83 Cpm * Cp 6,38 CPL 1,74 CPU 11,02 Cpk 1,74 Potential (Within) Capability Overall Capability PPM < LSL 0,00 0,02 0,09 PPM > USL 0,00 0,00 0,00 PPM Total 0,00 0,02 0,09 Observed Expected Overall Expected Within Performance LSL USL Overall Within Process Capability Report for Ra-z1 (µm) 7,26,66,05,44,84,23,63,0 LSL 2,8 Target * USL 7,2 Sample Mean 5,26 Sample N 6 StDev(Overall) 0,117473 StDev(Within) 0,123457 Process Data Pp 6,24 PPL 6,98 PPU 5,50 Ppk 5,50 Cpm * Cp 5,94 CPL 6,64 CPU 5,24 Cpk 5,24 Potential (Within) Capability Overall Capability PPM < LSL 0,00 0,00 0,00 PPM > USL 0,00 0,00 0,00 PPM Total 0,00 0,00 0,00 Observed Expected Overall Expected Within Performance LSL USL Overall Within Process Capability Report for Ra-z2 (µm) 7,156,505,855,204,553,903,25 LSL 2,8 Target * USL 7,2 Sample Mean 7,265 Sample N 6 StDev(Overall) 0,105214 StDev(Within) 0,110573 Process Data Pp 6,97 PPL 14,15 PPU -0,21 Ppk -0,21 Cpm * Cp 6,63 CPL 13,46 CPU -0,20 Cpk -0,20 Potential (Within) Capability Overall Capability PPM < LSL 0,00 0,00 0,00 PPM > USL 666666,67 731642,49 721682,06 PPM Total 666666,67 731642,49 721682,06 Observed Expected Overall Expected Within Performance LSL USL Overall Within Process Capability Report for Ra-z3 (µm) Engineering, Technology & Applied Science Research Vol. 8, No. 3, 2018, 2931-2936 2936 www.etasr.com Boujelbene: Process Capability and Average Roughness in Abrasive Water Jet Cutting Process of … [2] J. Valicek, S. Hloch, D. Kozak, “Surface geometric parameters proposal for the advanced control of abrasive waterjet technology”, The International Journal of Advanced Manufacturing Technology, Vol. 41, pp. 323–328, 2009 [3] A. Daymi, M. Boujelbene, E. Bayraktar, A. Ben Amara, D. Katundi, “Influence of feed rate on surface integrity of titanium alloy in high speed milling”, Advanced Materials Research, Vol. 264-265, pp. 1228- 1233, 2011 [4] Y. Wu, S. Zhang, S. Wang, F. Yang, H. Tao, “Method of obtaining accurate jet lag informationin abrasive water-jet machining process”, The International Journal of Advanced Manufacturing Technology, Vol. 76, No. 9-12, pp. 1827–1835, 2015 [5] I. Miraoui, M. Boujelbene, E. Bayraktar, “Analysis of cut surface quality of sheet metals obtained by laser machining: thermal effects”, Advances in Materials and Processing Technologies, Vol. 1 No. 3-4, pp. 633-642, 2015 [6] M. Boujelbene, A. S. Alghamdi, I. Miraoui, E. Bayraktar, M. Gazbar, “Effects of the laser cutting parameters on the micro-hardness and on the heat affected zone of the mi-hardened steel”, International Journal of Advanced and Applied Sciences Vol. 4, No. 5, pp. 19-25, 2017 [7] A. Alberdi, A. Rivero, L. N. Lopez de Lacalle, I. Etxeberria, A. Suarez, “Effect of process parameter on the kerf geometry in abrasive water jet milling”, The International Journal of Advanced Manufacturing Technology, Vol. 51, No. 5-8, pp. 467– 480, 2010 [8] M. Boujelbene, P. Abellard, E. Bayraktar, S. Torbaty, “Study of the milling strategy on the tool life and the surface quality for knee prostheses “, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 31, No. 2, 610-615, 2008 [9] M. Boujelbene, S. Ezzdini, N. Elboughdiri, W. Ben Salem, W. Youssef, “Investigation on the surface roughness of the high steel material after wire electrical discharge machining process”, International Journal of Advanced and Applied Sciences, Vol. 4, No. 5, pp. 130-136, 2017 [10] V. Perzel, P. Hreha, S. Hloch, H. Tozan, J. Valíček, “Vibration emission as a potential source of information for abrasive waterjet quality process control”, The International Journal of Advanced Manufacturing Technology, Vol. 61, No. 1-4, pp. 285– 294, 2012 [11] M. Palleda, “A study of taper angles and material removal rates of drilled holes in the abrasive water jet machining process”, Journal of Materials Processing Technology, Vol. 189, No. 1-3, pp. 292–295, 2007 [12] M. Boujelbene, “Influence of the CO2 laser cutting process parameters on the Quadratic Mean Roughness Rq of the low carbon steel”, Procedia Manufacturing, Vol. 20, pp. 259-264, 2018 [13] D. C. Montgomery, Introduction to statistical quality control, 4th edn. Wiley, New York, NY, 2001 [14] K. S. Chen, W. L. Pearn, “An application of non-normal process capability indices”, Quality and Reliability Engineering International, Vol. 13, No. 6, pp.355–360, 1997 [15] F. C. Kaminsky, R. A. Dovich, R. J. Burke, “Process capability indices: now and in the future”, Quality Engineering, Vol. 10, No. 3, pp. 445– 453, 1998 [16] L. I. Tong, K. S. Chenn, H. T. Chen, “Statistical testing for assessing the performance of life time index of elcetronic component with exponential distribution”, Int J Qual Reliab Manage, Vol. 19, No. 7, pp. 812–824, 2001 [17] J. P. Chen, C. G. Ding, “A new process capability index for nonnormal distribution”, International Journal of Quality and Reliability Management, Vol. 18, No. 7, pp. 762–770, 2001 [18] S. Aravind, K. Shunmugesh, K. T. Akhilc, M. Pramod Kumar, “Process Capability Analysis and Optimization in Turning of 11sMn30 Alloy”, Materials Today: Proceedings, Vol. 4, No. 2, pp. 3608–3617, 2017 [19] P. K. Sahu, S. Pal. “Multi-response optimization of process parameters in friction stir welded AM20 magnesium alloy by Taguchi grey relational analysis”, Journal of Magnesium and Alloys, Vol. 3, No. 1, pp. 36-46, 2015 [20] K. Abhishek, S. Datta, S. S. Mahapatra, “Multi-objective optimization in drilling of CFRP (polyester) composites: Application of a fuzzy embedded harmony search (HS) algorithm”, Measurement, Vol. 77, pp. 222-239, 2016 [21] A. Jeang, “Optimal process capability analysis for process design”, International Journal of Production Research, Vol. 48, No. 4, pp. 957– 989, 2009 [22] B. El Aoud, M. Boujelbene, E. Bayraktar, S. Ben Salem, I. Miskioglu, “Studying Effect of CO2 Laser Cutting Parameters of Titanium Alloy on Heat Affected Zone and Kerf Width Using the Taguchi Method”, Mechanics of Composite and Multi-functional Materials, Vol. 6, pp. 143-150, 2018