QUALITY IMPROVEMENT OF ALUMINIUM ALLOY (LM-6) CASTING USING TAGUCHI METHOD Ravneet kakria ∗ , Chandandeep Singh**, Priyavrat Thareja*** ABSTRACT There is no alternative to the intricacy; evidenced through sand casting as a production route, economical and poised for shorter runs. Quality of castings and parametric control thereof is more important than that of die casting. With increasing demand for high-quality castings @ close tolerances, a need however was felt to study ways to get the optimal settings for main parameters to ensure better surface finish. The requirement in unison was hitherto attempted in case of LM-6 Aluminum alloys castings, developed through green sand casting route. Five main parameters namely Bentonite clay, AFS Grain Fineness Number, Moisture, Pouring temperature and Coal dust were duly identified. The effects of the selected process parameters on the surface finish and the subsequent optimal settings of the parameters were accomplished using Taguchi’s method. Using L8 (27) orthogonal arrays, experiments were conducted as per experimental plans given in this array. The results indicate that the selected parameters significantly affect the surface finish of LM-6 Aluminum alloys castings. The confirmatory experiments have also been carried out to verify the optimal settings of the parameters. KEY WORDS: Design of Experiments, sand casting of non ferrous alloy, surface finish, 1. INTRODUCTION: Casting remains as the most potent method for shaping of metals amongst the various manufacturing methods, while it underwent a successful transformation from art to science (Thareja 2005). More the advance towards volumes, as in case of automotive castings, higher has been the ∗ Department of Mechanical Engineering, University Institute of Technology,, Punjab University Chandigarh. E-mail: [email protected] **Department of Mechanical and production Engineering, Guru Nanak Dev Engineering College, Ludhiana E-mail [email protected],, ; *** Department of Metallurgical Engineering Punjab Engineering College, Chandigarh E-mail: [email protected] Electronic copy available at: http://ssrn.com/abstract=1487542 growth stimuli. In modern foundries, green sand moulding method has been widely used for especially small size automotive castings and intricate ones. While remaining as a least expensive method, it gives optimum quality thanks to a lower cost of sand, its ingredients and its reusability. Green sand moulding derives its name due to presence of moisture in it (Berth, 1990). Moisture is blended through sand Muller using a mixture of silica sand, Bentonite clay, coal dust, wood floor, dextrin powder, fire clay and water. The sand can be reused, till it exhibits requisite bond development and other molding properties; albeit with supplemented additions of Bentonite clay and water to account for due losses attributed to ignition, evaporation and other kinds of degradation. Innovative and strategic management of sand is essential for maintaining due Quality of both process and product, ensuring all around competitiveness (Thareja 2007). Product Quality of a casting is a measure of its dimensional accuracy, surface finish and soundness (Morgen, 1982). It depends upon the quality of various constituents of green sand and structural properties of green mould (Berth, 1990). It is also influenced by the metal or alloy, in terms of their castability. LM-6 aluminum alloy is a specification of cast Al-12% silicon alloy. Characterised by good fluidity and resistance to hot tears, it can be easily sand, pressure and gravity die cast. It is used for very thin, pressure tight and intricate castings such as motor casings, meter casings and pump impellers. It has very high resistance to atmospheric corrosion particularly to saline water. That is why; it is mostly used for marine castings such as cylinder head, inlet and exhaust manifolds, chemical and paint industry. LM-6 possesses very high ductility making it useful for post casting metal forming, which makes founding simplistic. More versatility, through achievement of closer control of process parameters and product characteristics, promises least costs and minimum process rejections while targeting for consistent qualities. Pignatiello and P. Ramberg (1985) have studied the heat treatment of leaf springs of truck. The important factors and their interactions have been studied and optimized for free height reduction to 8 inches standard for its travel through heating furnace and forming machine, while bringing in process improvement fo economics and robustness of process. Shan, et al. (2002) have studied the significance of effects of magnetic field on abrasive flow machining process on non ferrous materials. Syrcos (2003) Electronic copy available at: http://ssrn.com/abstract=1487542 analyzed various significant process parameters of the die casting method of AlSi9Cu13 aluminum alloy to obtain optimal settings of the die casting parameters, in order to yield the optimum casting density of the AlSi9Cu13 aluminum alloy castings. The process parameters considered were: piston velocity (first and second stage), metal temperature, filling time and hydraulic pressure. The effects of the selected process parameters on the casting density and the subsequent optimal settings of the parameters have been accomplished using Taguchi’s method. Tortum et al (2005) used Taguchi method to determine optimum conditions for tire rubber in asphalt concrete using Marshall Test. The various experimental parameters explored combinations of different tire rubber gradations, mixing temperatures, aggregate gradations, tire rubber ratios, binder ratio, compaction temperatures and mixing times. Guharaja (2006) made an attempt to obtain optimal settings of the green sand casting process in order to yield the optimum quality characteristics of the spheroidal graphite (SG) cast iron rigid coupling. The effect of selected process parameters i.e. green strength, moisture content, permeability and mould hardness and its levels on the casting defects have been accomplished using Taguchi’s parameter design approach. The result indicated that the selected process parameters significantly affect the casting defects of SG cast iron rigid coupling castings. An aggregate view of such studies makes a case for deploying Taguchi method for surface finish analysis. In this paper an attempt has been therefore been made to find out the optimal settings of the process parameters to get the better surface finish of LM6 aluminium alloy. The Taguchi Design of Experiment technique has been judicially employed as evidenced for its potency established in aforementioned survey of literature. The confirmatory experiments have also been carried out to verify the theoretical settings of the parameters. 2. TAGUCHI DESIGN APPROACH: There are three approaches to Taguchi’s process which are namely System design, Tolerance design and parameter design. Out of these three approaches, parameter design was used in aluminium alloy (LM-6) casting. It involves the following steps (Kackar, 1985): 1. Define the problem and determine the objective. 2. Choose relevant quality characteristics. 3. Brainstorm potential factors. 4. Choose important factors and their range & nos. of levels 5. Determine noise factors. 6. Develop the experimental design viz. Select the orthogonal arrays, assign control factors and their interactions to column of inner array and noise factors to outer array. Describe each trail condition and decide their order and repetition. 7. Perform experimental trails and collect data. 8. Analyse the data and evaluate the optimum design. 9. Interpret the result. 10. Run the confirmatory experiment. 11. Implement the recommendations. 3. EXPERIMENTAL DESIGN: The process parameters and their levels have been adapted from the literature (Burns, 1986) for LM-6 aluminum alloy castings in synthetic sand. The range of these parameters was selected on the basis of literature review. The sand tends to imprint its net topography on surface of casting. With this end in mind, and to effect a more robust conclusion to study, the response parameter selected in this study was limited to surface roughness. Coarser sand (represented by inverse of Grain Fineness value) thereby is expected to result in poorer surface finish. Bentonite though provides more strength and robustness to the mold, and cold dust higher smoothness besides its impacts available to the extent of its combustion. Moisture, though is considered as an interacting additive with bentonite, for later to perform, influences the element of gas, which has a big, yet complex role towards dictating required surface morphology of castings. Any increase in Pouring temperature must increase fluidity of metal and therefore may respond to surface conformance better. Will it be noticeable remains to be determined by experimentation? The whole process is thus complex. So, an experimental layout was proposed to be set at two distinct levels each for five main parameters. Foseco handbook details requisite specifications for synthetic sand compositions employable for Al-Si castings. The ranges for Water (%age moisture) and bentonite are expressed to vary with in 3-4 % and 4 % respectively. Recommended AFS Grain Fineness is 80 to 100, and AFS Permeability 40 – 50. The expected Green Compressive Strength is 5 lb/ in2. The output necessarily mandates development of a sound casting (Burns, 1986: 147). After considering recommended practices for metal treatment and necessary precautions, as recommended in Foseco handbook, the winners stand shaped castings (as shown in Fig 1) were successfully cast using parameters detailed in Table I. Fig 1 Dimensions of the pattern The possibilities of specific interactions between the parameters are detailed hereunder: (i) Bentonite clay and AFS grain fineness number. (A × B): Amount of bentonite depends upon grain fineness number. A bigger grain qualitatively requires more bentonite clay due to larger surface area. (ii) Bentonite clay and moisture. (A × C): Since sand-clay bonds are developed by water, so amount of moisture depends directly on amount of bentonite clay present in green sand mix. Table I: Process parameters and their levels Designation A B C D E Level- LevelMain paramete rs 1 2 Bentonite 4% 6% clay Grain 80 100 Fineness no. Moisture 3% 4% ° Pouring 710 C 730°C Temp. Coal dust 1% 1.5% 3.1 Selection of orthogonal Array: Orthogonal arrays are special experimental designs constructed by Taguchi that require only a small number of experimental trials to help discover main factor effects. Orthogonal arrays are fractional factorial designs and symmetrical subsets of all combinations of treatments in the corresponding full factorial designs. The Orthogonal arrays facilitate the experimental design process by assigning factors to the appropriate column. For the selection of particular OA, the number of parameters, the number of levels and possible interactions must be taken into considerations. In order for an array to be a viable choice, the number of rows in it must at least be equal to the degree of freedom required for the case study. Degree of freedom (DOF) for all the above factors is ftotal. = [5×1+2×1] = 7. Taguchi’s OA are selected on the basis of the condition that the total DOF of a selected OA must be greater than or equal to the total DOF required for the experiment. L8 (27) O.A. has eight number of rows, and more DOF, is therefore selected. For this study, the utilization of an L8(27) array of Taguchi method has reduced the number of experimental configurations from (27) =128 (duly required for a full factorial study) to 8 only. Figure 2 shows the linear graph of L8 orthogonal array. Figure 2 Linear 7 graph of L8 (2 ) 4. RESULTS AND PARAMETERS ANALYSIS FOR OPTIMIZATION OF In conformance with Process parameters selected at respective levels as tabulated in table I, founding experiments were made in the college metal casting laboratory. Following a stipulated experimental route as shown in table II, the aluminum castings were made in random order, dictated by L8 orthogonal array, using wooden pattern of dimensions as shown in figure 1. As the layout depicts, first casting was carried out using level 1 value for all the parameters of table I. Similarly the casting 2 was carried out using level 1 values of Bentonite and A.F.S Grain Fineness number and level 2 values each for moisture, coal dust and temperature. In similar way other experiments were carried out. The value of surface roughness was measured with the help of Surfcoder available in the laboratory. Table II: Experimental layout for LM-6 aluminum alloy castings Casting No. 1 1 2 3 4 5 6 7 8 Col. No. 2 3 4 5 6 7 Parameters A B C D A×B A×C E Bentonite A.F.S. Moisture Coal Temp. Clay (%) No. (% age) Dust (oC) 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 2 2 1 1 2 2 1 2 2 2 2 1 1 2 1 2 1 2 1 2 2 1 2 2 1 2 1 2 2 1 1 2 2 1 2 2 1 2 1 1 2 # The lower and upper level of the parameters has been shown as 1 and 2 respectively. It was decided to take three surface readings randomly each at end positions and center on all steps of the castings, as illustrated in figure 1 and tabulated in format P1, P2, P3 and Q1, Q2, Q3 respectively. The Taguchi paradigm stipulates determination of a loss function to gauge the deviation between the experimental and desired value of a performance characteristic. The loss function is defined by the S/N ratio (Signal to Noise) to determine the optimum parameters of casting process of aluminium alloy. There are three types of categorisation possible for S/N ratio analysis viz. Higher the better, the nominal the better and the smaller the better. In this work, the characteristic measure was surface roughness, whose lower (smallest) value should be able to label the product as becoming superior. In other words, to obtain the optimal parameters, the smaller the better type of response was considered. The S/N ratio for smaller the better is given by SN LB = − 10 log( 1 r 2 ∑Y i ) r i =1 Average value of the surface roughness and those of S/N ratios for all eight castings were calculated. The duly computed S/N ratios for respective surface roughness are shown in table III. Table III Surface roughness values and S/N ratio Casting Surface No. Roughness (µm) 1 5.676000 2 5.134333 3 7.097667 4 5.333167 5 5.094333 6 5.735333 7 6.702333 8 4.593000 S/N ratio (dB) -15.40 -14.43 -17.45 -14.73 -14.45 -15.41 -16.57 -13.30 4.1 Analysis of Variance for Raw and S/N data: Analysis of variance (ANOVA) was performed to determine the relative significance of factors in terms of their percentage contribution to the response. Results of pooled ANOVA for both (i) Raw Data and the output data for Surface Roughness (ii) Signal to Noise ratio are tabulated in Table IV and Table V. Significant parameters have been highlighted. Noticeable surface response in case of Moisture and Coal dust in case of both Surface roughness and S/N ratio is evidenced from the results, as shown in Fig 3 also. Bentonite addition responded in terms of improvement of surface finish when increased pouring temperature was used. Table IV: Pooled ANOVA of Raw Data. (Surface Roughness) Pool SS Y 0.934 Y 3.264 N 10.685 Y 1.750 Y 1 f V F Ratio F*Ratio SS* P % 1 0.934 0.496 1 3.264 1.381 1+ 10.685 5.674** 5.526** 8.757 7.34 1 1.750 0.929 1 1 0.531 Y 0.526 1 N N N 11.840 29.999 6.048 7.817 1+ 11.840 6.288** 6.139** 9.912 8.31 7 1+ 6.048 3.212* 3.136* 4.120 3.45 1+ 7.817 4.151** 4.053* 5.889 4.94 B×P Y 2.643 1 2.643 1.404 C×P Y 0.766 1 0.766 0.407 D×P Y 1.202 1 1.202 0.638 A×B×P Y A×C×P Y 1.963 1 1.963 1.042 3.624 1 3.624 1.925 Y 5.003 1 5.003 2.657 Source A B C+ D A×B A×C E+ SSTI P+ A×P+ E×P SST2 e3 ep+ SST3 Y 59.065 60.257 82.932 119.322 0.526 15 32 1.883 43+ 1.928 47 0.279 75.96 100 Table V: Pooled ANOVA of S/N Data. (Surface Roughness) Source A B C+ D ×B A×C E+ ep+ Total V F Ratio F*Ratio SS* P % 0.650 2.731 0.696 2.924 4.500 18.907 7.414** 3.893 32.42 0.769 3.231 0.684 2.874 Pool Y Y N Y Y SS f 0.650 1 0.696 1 4.500 1+ 0.769 1 0.684 1 Y 0.238 N 4.470 1+ 4.470 18.782 7.364** 3.863 32.17 5+ 0.607 35.41 12.007 7 100 1 0.238 - Table VI: process parameters and their optimum levels Process Parameters Level affecting average 2 Level affecting variability - Moisture percentage (C) 2 2 Coal Dust percentage (E) 1 1 Bentonite clay (A) (a) (b) (c) (d) (e) Fig 3: Response plots for surface roughness (for significant factors only). (a), (b) & (c) show effects of A, C & E on process average and (d) and (e) shows effects of C & E on S/N data. The factors of LM-6 aluminum alloy sand castings experiment for surface roughness can be classified as: It is found that the optimal parameter combination of Green sand casting process for best surface finish corresponded to a Bentonite clay addition to 6% (Level-2), a moisture level of 4% (Level-2) and Coal dust addition of 1% (Level-1). The response values in terms of levels impacting either Average and/or variability are summarized in table VI. Thus, a state of A2 C2 E1 is recommended for achieving highest surface finish for LM-6 aluminium alloys castings made in Green sand. Higher percentage of Bentonite clay was found to affect the average of the surface finish only. The effect is attributable to lower mould wall degradation as a result of bentonite strengthening. Higher percentage of water was found to affect the phenomenon of mould binding and finishing resulting in better finish of produced castings. It was found to affect both the average and variability of surface finish. Lower percentage of Coal dust was found to optimally produce best surface finish on castings. It was also found to affect both the average and variability of the surface finish. The metal pouring temperature and Grain fineness number showed no effect on surface finish, so a lower level of these factors for metal superheat and also of AFS no. can be selected. 4.1.1 Estimation of Optimum performance characteristics: The averages of the levels of factor A, C and E are: A 1 = 5 . 811 C 1 = 6 . 143 A 2 = 5 . 532 C 2 = 5 . 199 E 1 = 5 . 174 E 2 T = 5 . 671 = 6 . 168 So, optimum levels are: A 2 = 5 . 532 μ m C = 5 . 199 μ m 2 E 1 = 5 . 174 μ m The estimated average results, when the three control factors are at their better level is: μA2,C2,E1 = A + C + E − 2 T =5.532+5.199 + 5.174 –2 (5.671) = 4.563 µm 2 2 1 ……..(1) The 90% confidence interval (C.I.) for the population and the confirmation experiments of 2 parts is: Calculated as: (a) For population: F CIpop= ± α ( fi & fe ) × V e ……. n eff (2) (b) For confirmation experiment: CICE = ± F α ( fi Where, neff. = & fe ) × Ve × 1 1 + n eff r …….(3) Total number of observations 1+ (Total degree of freedom associated with items used in estimating μ) 48 By substituting the following: F0.90 (1& 43) = 2.831, Ve = 1.928; n eff. = =4; r = 12, CI is 1+3 Calculated to be ± 0.674. Therefore the 90% confidence interval should be given by: 3.609 micron < μCE < 5.517 micron 4.1.2 Confirmation Experiments: As part of the validation; two samples were cast at the optimum condition as above; with surface roughness at position 1 & 2 determined as 5.134, 4.573, 5.218, 3.911, 5.176, 6.109; 6.014, 4.198, 5.368, 5.140, 4.366 and 5.004 micron respectively. The samples have been evaluated following the same criteria used for the original experiments. The average of the sample has been found to be 5.018 micron, which is within the confidence interval. The expected reduction in surface roughness has been found to be 11.5%. Avg. Surface Roughness (micron) 8 7 6 5 4 3 2 1 0 11.5 % Reduction 5.671 5.018 1 Before Exp. 2 After Exp. Figure 4 Expected reduction in surface roughness CONCLUSIONS: The study investigated the optimisation of Sand Casting process factors and levels using Taguchi Method. The results are summarized as follows: 1. Taguchi Technique can be effectively deployed to get desired Quality improvement @ austere experimentation. In presence work the metric of Quality improvement was chosen to be surface finish, and only 8 experiments sufficed against 128 necessitated in conventional circumstances. 2. The optimal parameter combination of Green sand casting process for best surface finish corresponded to a Bentonite clay addition to 6%, a moisture level of 4% and Coal dust addition of 1%. Therefore, A2 C2 E1 is recommended for achieving best surface finish for LM-6 aluminium alloys castings in Green sand. 3. The confirmation experiment was conducted to validate the experimental work done. It showed expected improvement of 11.5% in average surface finish of the castings. 4. The predicted optimal range for the confirmation experiment of two experiment is given by 3.609 micron < μCE < 5.517 micron. REFERENCES 1. 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