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  • 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 5, September - October (2013), pp. 116-129 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET ©IAEME PROCESS CAPABILITY IMPROVEMENT – A CASE STUDY OF AN ENGINE CONNECTING ROD MANUFACTURING PROCESS G.V.S.S.Sharma1*, Dr.P.S.Rao2, V.Jagadeesh3, Amit Vishwakarma4 1 Assistant Professor, Dept. of Mechanical Engg., GMR Institute of Technology, Rajam, A.P.,India. 2 Professor, Industrial Engineering Dept., GITAM University,Visakhapatnam, A.P., India. 3 Assistant Professor, Dept. of Mechanical Engg., GMR Institute of Technology, Rajam, A.P.,India. 4 Manager, Manufacturing Engineering, Volvo Eicher Commercial Vehicles Limted, Pithampur,M.P.,India ABSTRACT Statistical quality control forms an excellent Quality Assurance tool for improving the quality of manufacture and ultimately scores on the end-customer satisfaction. SQC uses process monitoring charts for recording the critical to quality (CTQ) characteristic of the component in manufacture. This paper elaborates on bolt-holes center distance, which forms one such CTQ characteristic of the connecting rod manufacturing of internal combustion engine. Here the journey for attainment of the Cp and Cpk values greater than 1.33 is elaborated by identifying the root cause through the quality control tools like the cause and effect diagram and examining each cause one after another. In this paper the DMAIC approach is employed (Define-Measure-Analyze-Improve-Control). The Definition phase starts with the process mapping and identifying the CTQ characteristic. The next phase is the measurement phase comprising of the cause and effect diagram and data collection of CTQ characteristic measurements. Then follows the Analysis phase where the process potential and performance capability indices are calculated, followed by the analysis of variance of the mean values (ANOVA). Finally the process monitoring charts are used for controlling the process and prevent any deviations. By using this DMAIC approach, standard deviation is reduced from 0.017 to 0.009 and the Cp values from 0.97to 1.77 and Cpk values from 0.57 to 1.49 respectively. Keywords: Critical to quality (CTQ) characteristic, cause and effect diagram, statistical quality control (SQC), process monitoring charts (PMC), Analysis of Variance (ANOVA) 1. INTRODUCTION One of the major manufacturing processes in engine manufacturing is that of connecting rod manufacturing. This paper implements the DMAIC approach, i.e., Define-Measure-Analyze116
  • 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME Improve-Control approach to improve the capability of connecting rod manufacturing process by reducing the bolt-holes center distance variations from a nominal value. Process mapping and identifying CTQ characteristic is carried out in “Define” phase, while estimates of process capability indices is carried out in the “Measure phase”. One way ANOVA method of investigation to test for the differences between the manufacturing data is employed in the “Analysis phase”. Finally, the PMC (process monitoring chart) for the gudgeon-pin bore diameter is employed in the “Improve and Control phase”. Statistical Quality Control studies form the basic tool for obtaining the required process capability confidence levels. The various process capability indices are defined as follows:USL − LSL 6σ USL − µ C PK U = 3σ µ − LSL C PK L = 3σ  USL − µ µ − LSL  C P K = m in  ,  3σ 3σ   w h e re , CP = (1 ) (2 ) (3 ) (4 ) U S L a n d L S L a r e U p p e r a n d L o w e r s p e c if i c a t io n lim it s µ = p r o c e s s m e a n , σ = s t a n d a r d d e v i a t io n The term Cp denotes for the process potential capability index and similarly the term Cpk denotes for the process performance capability index. Cp gives an indication of the dispersion of the product dimensional values within the specified tolerance zone during the manufacturing process. Similarly the index Cpk denotes for the centering of the manufacturing process with respect to the mean of the specified dimensional tolerance zone of the product. Cpk gives us an idea that whether the manufacturing process is performing at the middle of the tolerance zone or nearer to the upper or lower tolerance limits. If the manufacturing process is nearer to the lower limit then the process performance capability index is given by Cpkl and if the manufacturing process is nearer to the upper limit then the process performance capability index is given by Cpku. As a measure of perceptional safety the minimum value amongst the two is taken as the value of Cpk. 2. LITERATURE REVIEW In 1994, Schilling, E.G.[1], emphasized on how the process control is better than the traditional sampling techniques. During the same era, Locke and John.W. [2],in their paper titled “Statistical Measurement Control”, emphasized on the importance of process charts, cause and effect considerations, and control charting. After primitive studies on statistical quality control, Hung-Chin Lin [3] in 2004, had thrown some light on process capability indices for normal distribution. J.P.C.Tong et al. [4] suggested that how a Define-Measure-Analyse-Improve-Control (DMAIC) approach is useful for printed circuit board quality improvement. They also proved that how DesignOf-Experiments is one of the core statistical tools for six-sigma improvement. Subsequently, MingHsien Caleb Li et al. [5] once again proved the importance of DMAIC approach to improve the capability of surface mount technology in solder printing process. Yeong-Dong Hwang [6] in their paper discussed the DMAIC phases in detail with application to manufacturing execution system. Enzo Gentili et al. [7], applied the DMAIC process for a mechanical manufacturing process line, which manufactures both professional and simple kitchen knives. Chittaranjan Sahey et al. [8], once again brought the DMAIC approach into use for analyzing the manufacturing lines of a brake lever at a Connecticut automotive components manufacturing company. Rupinder Singh [9], investigated the 117
  • 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME process capability of polyjet printing for plastic components. In his observation, he voyaged the improvement journey of the process of critical dimensions and their Cpk values attainment greater than 1.33, which is considered to be industrial benchmark. In recent studies conducted by S.J.Lin et al. [10], they focused on turbine engine blade inspection, as it is a key aspect of engine quality. They elaborated on the accurate yield assessment of the processes of multiple characteristics like the turbine blades manufacturing process. A. Kumaravadivel and U. Natarajan [11] dealt with application of sixsigma methodology of the flywheel casting process. The primary problem-solving tools used were the process-map, cause and effect matrix and failure modes and effects analysis (FMEA). A careful study from the above literature reveals that the DMAIC approach is the best methodology for problem solving tools for improving the manufacturing process capability levels. Hence, this paper focuses on the application of DMAIC approach for process capability improvement of the crank-pin bore honing operation of an engine connecting rod manufacturing process. 3.DEFINE PHASE 3.1 Process Mapping The define phase starts with the correct mapping of the machining process flow of the connecting rod. The process flow chart for machining line of the connecting rod machining cell consisted of the following machining operations sequence, as shown in the Fig.1 below :- Fig. 1: Process Flow chart The Table 1 below depicts the description of the machining operations of connecting rod manufacturing cell. Table 1: Machining operations of connecting rod manufacturing cell. Machining Operation no. 10 20 30 40 50 60 70 80 90 100 110 120 130 140 Description Thrust face rough grinding Gudgeon pin rough boring Crank pin rough boring Side face broaching Finish grinding Bolt hole drilling Key way milling Rod and cap assembly Finish grinding of assembly Finish boring of gudgeon pin bore Finish boring of crank pin bore Crank pin bore Honing Magnetic crack detection Final quality check, set making and dispatch to engine assembly line. 118
  • 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME 3.2 Identifying CTQ characteristic The bolt-holes center distance forms a very important dimensional characteristic as it is the primary dimension responsible for fastening the connecting rod cap and the rod parts. Any out-ofdimension of the bolt-holes center distance of the connecting rod leads to incorrect assembly of the cap and rod. This paves way for unequal force distribution on the rod and cap parts and subsequently leading to engine failures and costly rework. Hence for achieving the desired engine performance the bolt-holes center distance of the connecting rod forms an important CTQ characteristic. In this regard, this research aims at improving the connecting-rod manufacturing process by reducing the bolt-holes center distance variations during the bolt-hole drilling and reaming operation so that these variations are not carried to the subsequent machining operations down the manufacturing line. The acceptable bolt-holes center distance tolerance zone variation for was limited to 1.000 mm. The connecting rods which were out of these tolerance limits resulted in inaccurate assembly of the cap and rod parts and subsequently got rejected. Even costly repair and rework could not restore the dimension back. Hence bolt-holes center distance was of the main concern and identified as a CTQ ± 0.050 mm. The figure 1 below depicts the characteristic, whose value is equal to 100.000 diagrammatic view of bolt-holes center distance. Fig 2 (a) Fig 2 (b) d = Center distance between bolt holes Fig 2 (c) Fig. 2 (a): Center distance ‘d’ of bolt holes shown on the connecting rod assembly view. Fig 2 (b): Center distance ‘d’ of bolt holes shown on the rod. Fig 2 (c): Center distance ‘d’ of bolt holes shown on the cap. 4. MEASUREMENT PHASE In this phase the data of bolt-holes center distance on cap and rod subassemblies for nominal 32 consecutive readings is collected and plotted on the process monitoring chart. This data collection was performed in 3 iterations. In each iteration the data set of bolt-holes center distance measurement readings is taken and analyzed for Cp, Cpk values and followed by suitable corrective action. After the corrective action is implemented, the next iteration was performed. This procedure was continued until the Cp and Cpk values are greater than or equal to 1.33, i.e., upto 4σ quality level as decided by the management of the Engine manufacturing Plant. 119
  • 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME 4.1 Cause and Effect diagram: The critical to quality characteristic identified was the bolt-holes center distance which is equal to 100.000 ± 0.050 mm whose machining tolerance zone is equal to 1.000 mm. The Cp value, i.e., the process potential capability index, {Cp= (USL-LSL)/6σ}, nominally was equal to 0.97, which was below the acceptance level limit of greater than 1.33 for the above CTQ characteristic. The first part of the measurement phase investigation was to track down and differentiate the common causes and special causes involved. For doing so, the cause and effect diagram, also known as the Fish-bone diagram or Ishikawa diagram [2][6], was employed. , as show below in Fig3 Fig3: Cause and Effect diagram showing the variables affecting the bolt-holes center distance during the engine connecting rod bolt hole drilling operation One important aspect to be mention at this juncture is the description of bolt-hole drilling and reaming operation set up. The machine employed was horizontal axis drilling machine of MICO make. It consists of a gang drilling attachment with dual drill set up where both the drills act simultaneously through the jig bushes. The machine platform was equipped with limit switch gauges for accurate positioning of the component locating fixtures onto the gang drill bits. Below shown is the machine snap shot of the horizontal axis bolt hole drilling and reaming machine employed for bolt hole drilling and reaming of con-rod. Fig 5: Horizontal axis bolt hole drilling machine 120
  • 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME 4.2 Process FMEA (failure modes and effects analysis) FMEA sheet for bolt hole drilling is shown in following Fig 4. From the causes enumerated in the cause and effect diagram, the Failure modes and effects analysis was performed and the corresponding FMEA sheet is displayed in the Fig.2. It can be noticed that the highest RPN (Risk Prority Number) is for the potential failure “bolt hole center distance more” and followed by the potential failure “Bolt hole center distance less”. Hence the bolt-hole center distance variations are of the prime concern and hence are liable for improvement action. The data collection was performed and the measurements of the bolt-hole center distance variations were recorded for further analysis and improvement of process capability. Fig 4 FMEA sheet 4.3 Data collection: Data collection of the critical to quality characteristic was performed for 32 consecutive machined cap and rod components. Data collection was performed in 3 iterations spanning for a period of 3 weeks i.e., about 2500 consecutive components. The data is tabulated in the tabular form in the Table 2 as follows: 121
  • 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME Table 2 Measured dimensions of CTQ characteristic S.No. Iteration 1 Iteration 2 Iteration 3 S.No. Iteration 1 Iteration 2 Iteration 3 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 99.980 99.970 99.985 100.000 99.965 99.995 99.980 99.960 100.000 99.950 99.995 99.960 99.980 100.010 99.980 99.960 100.000 99.990 100.005 100.020 99.985 100.015 100.000 99.980 100.020 99.970 100.015 99.980 100.000 100.015 100.000 99.970 100.005 99.995 99.995 100.015 100.000 100.020 100.005 100.020 100.020 100.010 100.000 100.010 100.000 100.020 100.005 100.000 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 100.01 99.980 99.955 99.990 99.960 99.980 99.995 99.965 99.995 99.990 99.955 100.000 99.960 99.990 99.965 99.990 100.010 100.000 99.970 100.010 99.980 100.000 100.015 99.985 100.015 100.010 99.975 100.020 99.980 100.010 99.985 100.010 100.020 100.005 99.995 100.015 99.995 100.005 100.020 100.005 100.020 100.015 99.995 100.020 100.005 100.015 99.995 100.015 The data in the Table 2 was plotted on the process monitoring chart with no. of components in x-axis and the dimension on y-axis and is shown in Fig 6. 5. ANALYSIS PHASE The analysis phase comprises of performing the calculations for the Cp and Cpk values across each iteration. And testing the differences between the three iterations using one way ANOVA method. Fig 6 Process Monitoring chart 5.1 Calculations of Cp and Cpk The calculations of Cp and Cpk are tabulated as below in Table 3 122
  • 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME Table 3 Calculations of Cp and Cpk Formula CP = (USL − LSL ) 100.050 99.950 100.050 99.950 0.016 0.009 0.97 σ Iteration 3 0.017 LSL Iteration 2 100.050 99.950 USL Iteration 1 1.01 1.77 1.36 1.05 1.49 0.57 0.97 2.06 0.57 0.97 1.49 6σ C PKU = (USL − MEAN ) 3σ C PKL = ( MEAN − LSL ) 3σ CPK = min (CPKU , CPKL) From the process monitoring charts and the calculations in Table 3, the following is the analysis done for each iteration set of data: 5.1.1 Iteration no.1 The first set of the Statistical process capability study comprised of the raw data of the CTQ characteristic, which depicted the transparent picture of the state of the existed problem. Continuous set of readings of bolt hole center distances of both the connecting rod and cap after the bolt hole drilling and reaming operations (operation no.60) were captured with the help of a venire caliper set up. Hence it is seen here in the 1st iteration of SPC studies that the process is not capable and the Cp and Cpk values of the characteristic under study are 0.97 and 0.57, which Is less than that for process to be capable, i.e., 1.33. Hence, next set of data is captured after replacing the worn out jig bushes and tool, jig and fixture maintenance. 5.1.2 Iteration no. 2 In this Iteration, data is collected after the machine preventive maintenance schedule completion and replacement of worn out jig bushes. From the above set of data from Table 3 it is seen that there is a marginal increase of Cp from 0.97 to 1.01 and Cpk from 0.57 to 0.97. This marginal increase is a positive sign but still the process is not capable as both Cp and Cpk are less than the desired value of 1.33. This calls for another iteration. 5.1.3 Iteration no.3 In this iteration the data is collected after performing the regular maintencance work for proximity limit switches. From the above set of data from Table 3 it is seen that there is a noticeable increase of Cp from 0.97 to 1.77 and Cpk from 0.94 to 1.49. Now. Since both Cp and Cpk are greater than 1.33 hence the bolt hole drilling and reaming machining process is declared as a capable process. 5.2 One way ANOVA method The one way ANOVA method of investigation is adopted to test for the differences between the three iterations of data collected and also to determine the extent of influence of the causes responsible for low process capability. 123
  • 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME 5.2.1 Procedure describing one way ANOVA: In general, one way ANOVA technique is used to study the effect of k(>2) levels of a single factor. A factor is a characteristic under consideration, thought to influence the measured observations and level is a value of the factor. To determine if different levels of the factor affect measured observations differently, the following hypotheses are to be tested:H0 : µ i = µ all i= 1,2,3, H1: µ i ≠ µ for some i= 1, 2, 3, where, µ i is the population mean for level i , and µ is the overall grand mean of all levels. Here we have 3 levels (i.e., 3 iterations) and each level consisting of 32 measurement readings of crank-pin bore diameter of connecting rod. The sum, sum of squares, means and variance for each iteration is tabulated in the table 4 below:Table 4: Mean and variance of all the three iterations Formula Sample size Sum Sum of squares Mean (µ i) Variance (σ2) Iteration 1 32 3199.350 319870 99.979 0.00029 Iteration 2 32 3199.940 319988 99.998 0.00027 Iteration 3 32 3200.260 320052 100.008 0.000088 If xij denote the data from the ith level and jth observation, then overall or grand mean is given by :4 32 x ij µ = ∑∑ , (5) i =1 j=1 N Where N is the total sample size of all the three iterations i.e., 32x3=96 Hence, from equation (5), we get, µ = 99.995 The sum of squared deviations about the grand mean across all N observations is given by: 4 32 SSTT = ∑∑ ( x ij − µ ) 2 (6) i =1 j=1 The sum of squared deviations for each level mean about the grand mean is given by: 4 SSTL = ∑ 4 × ( µi − µ ) 2 (7) i =1 The sum of squared deviations for all observations within each level from that level mean, summed across all levels is given by :4 32 SSTE = ∑∑ ( x ij − µi ) 2 (8) i =1 j=1 From equations (6), (7) and (8), the values of SSTT, SSTG and SSTE obtained are 0.0336, 0.01387 and 0.0204 respectively. On dividing SSTT, SSTL and SSTE by their associated degrees of freedom (df), we get mean of squared deviations respectively. 124
  • 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME Hence, mean of squared deviations between levels is given by: SSTL 0.01387 = = 0.006935 df L ( 3 − 1) Mean of squared deviations within levels is given by: MSTL = MSTE = SSTE 0.0204 = = 0.000219 df E ( 96 − 3) (9) (10) Finally, the F Statistic is given by the following formula: FSTATISTIC = MSTL 0.006935 = = 31.66 MSTE 0.000219 (11) On summarizing all the above values in tabular form, the ANOVA table is obtained as shown below in Table 5: Table 5: ANOVA Table: Source of variation Level Within/error Total df 2 93 95 Sum of squares 0.01387 0.0204 90x10-5 Mean of squares 0.006935 0.000219 F 31.66 An α value of 0.05 is typically used, corresponding to 95% confidence levels. If α is defined to be equal to 0.05, then, the critical value for rejection region is equalt to, F CRITICAL (α, K-1, N-K). and is obtained to be 3.094. Thus, FCRITICAL = 3.094 (12) From equations (11) and (12) it is seen that: FSTATISTIC > FCRITICAL (13) Therefore, the decision will be to reject the null hypothesis. If the decision from the one-way analysis of variance is to reject the null hypothesis, then, it indicates that at least one of the means ( µ i) is different from the remaining other means. In order to figure out where this difference lie, a post-hoc ANOVA test is required. 5.2.2 Post-hoc ANOVA test Since here the sample sizes are same, we go for the Tukey’s test for conducting the Post-hoc ANOVA test. In Tukey’s test, the Honestly Significant Difference (HSD) is calculated as: HSD = q M S TE = 3 .3 8 n 125 0 .0 0 0 2 1 9 = 0 .0 0 8 8 4 32 (1 4 )
  • 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME Where q is the studentized range statistic which is equal to a value of 3.38, for a degree of freedom of 93 and k=3. The difference between the individual mean values of the three iteration levels can be summarized in a tabular form as shown below in Table 6: Table 6 Differences of means between any two iterations Difference µ1 − µ 2 = Computation 30.002 – 30.006 Numerical value -0.019 µ1 − µ3 = 30.002 – 30.007 -0.029 µ 2 − µ3 = 30.006 – 30.007 -0.010 In the above table 6, the absolute difference is of the concern and so the negative signs are to be ignored. From the above Table 5, it is seen that the differences of µ1 − µ 2 , µ1 − µ3 , µ 2 − µ3 are greater than that of the HSD in equation 14. So, the differences between the means are statistically significant. Hence, it is concluded that among all the different causes enumerated in the cause and effect diagram, the most influencing causes are the worn out drill jig bushes and non-maintencance of the proximity limit switch leading to hunting of the measurements readings. The extent of influence is given by eta-square (η2). It measures the proportion of the between factor variability to the total variability and is given by: sum of square between the levels η2 = sum of squares across all the 96 observations 0.01387 ⇒ η2 = = 0.4127 = 41.2% = 41% (15) 0.0336 Eta-square is just a ratio of treatment effect variability to total variability. One drawback with eta-square is that it is a biased estimate and tends to overestimate the effect. A more accurate measure of the effect is the omega-square (ω2) given by: ω2 = SSTG − ( k − 1) MSTE SSTT + MSTE ⇒ ω2 = 0.033819 = 0.3971 = 39.71% = 40% (16) Hence, from above equation (16) it is deduced that 40% of variability is due to the worn out drill jig bushes and non-maintenance of the proximity limit switch. 6. IMPROVE AND CONTROL PHASE In this phase the process monitoring charts are regularly employed for monitoring the bolthole center distance of the connecting rod. In addition to this the Design of Experiments (DOE) methodology was employed to improve the process capability. 6.1 DOE procedure The DOE had been adopted in order to improve the capability levels (sigma levels). The initial experiments were carried out to screen out the factors that might have influenced on the drilling performance. The further experiments were used to determine the optimal settings of the significant factors screened in initial experiments. 126
  • 12. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME 6.1.1 Initial Experiments In this initial experiments stage four influencing factors thought to affect the boring process were selected. A full factorial experiment was carried out and the whole experiment was completed in about 9 consecutive shifts, i.e., 3 days. In total about 200 components in each shift were measured for the bolt-hole center distance. The factors in the initial experiment are tabulated in Table 7. The experimental conditions are as follows (1) Ambient temperature 25°C (2) Humidity 56% (3) Machine operation no.60 (4) No. of operators :1 (5) bolt-holes center distance is equal to 100.000 ± 0.050 mm Table 7 the initial experiment with levels of each factor Factor Level 1 Level 2 Worn out drill bush Before replacing the drill bush After replacing the drill bush Calibration of venire caliper Before calibration After calibration limit switch malfunction Before repairing the limit switch After repairing the limit switch Set-up changeover inaccurate Manual drill bit loading Drill bit loading with a portable v-block insert setting gauge From the experimental results, the main effect of the proximity limit switch malfunction and calibration of venire caliper showed significant influence on the bolt-holes center distance. The interaction between worn out drill bush and the accuracy of set-up changeover were also significant. These significant effects were supported by the normal probability plot of the standardized effects shown in Fig. 7. Since there were significant differences due to spring back effect of the material after the drilling operation, the toggle clamping force variations, inaccurate re-sharpening of drill bit and reamer, hence these independent variables were taken into consideration in the further experiments. P r o b a b ility P lo t o f C 1 N o rm a l 99 M ean S tD e v N A D P - V a lu e 95 90 1 0 0 .0 0 .0 0 9 3 9 7 32 1 .3 1 0 < 0 .0 0 5 80 Percent 70 60 50 40 30 20 10 5 1 9 9 .9 8 9 9 .9 9 1 0 0 .0 0 1 0 0 .0 1 1 0 0 .0 2 1 0 0 .0 3 C1 Fig 7 Normal Probability plot of the CTQ characteristic 6.1.2 Further Experiments The further experiments were used to determine the optimal settings of the significant factors screened. In further experiments, apart from considering the previous factors (shown in Table 7) in further experiments, spring back effect of the material after the drilling operation, the toggle clamping force variations, inaccurate re-sharpening of drill bit and reamer were also taken into consideration. A full factorial experiment was carried out and the whole experiment was completed in about 12 consecutive shifts, i.e., 4 days. The levels of each factor in further experiments are given 127
  • 13. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME in Table 8. The experimental conditions are as follows (1) Ambient temperature 24°C (2) Humidity 55% (3) Machine operation no.60 (4) No. of operators :1 (5) bolt-holes center distance is equal to 100.000 ± 0.050 mm Table 8 the further experiment with levels of each factor Factor spring back effect of the material after the drilling operation the toggle clamping force variations inaccurate resharpening of drill bit and reamer 7. Level 1 Level 2 Low clamping force of 10kgf Revised high clamping force of 20 kgf Before replacing the toggle clamp bushes After replacing the toggle clamp bushes New drill bits and new reamers Drill bits and reamers after resharpening RESULTS AND CONCLUSION It is seen from the design of experiments that proximity limit switch accuracy was the major contributor followed by, calibration venire caliper, worn out drill bush, accuracy of set-up changeover, and spring back effect of material after drilling operation, toggle-clamping force variations and accuracy of resharpened drill-bits, in the said order. As a part of standardizing the process, as per the results of ANOVA and DOE, the following activities were carried out: i. ii. After every 10000 components being bored, the replacement of the drill bush needs to be done. Regular machine maintenance schedule were established and regular checks were included in the check lists. iii. After every 10 components bored, the accuracy of the limit switch was tested. iv. The venire caliper calibration is done periodically as a part of measurement system analysis. v. After every one month (about 12000 components) the drill-bit needs to be resharpened. vi. Coolant recirculation pressure was set at a value of around 10 kgs. vii. Drill-bit presetting on the drill tool holder was carried out by the help of a portable v-block insert setting gauge on the horizontal drill mandrel. viii. Component clamping force was raised to 20 kgf from 10 kgf in order to avoid any spring back effects of the component material after the drilling and reaming operations. SPC studies were found to be useful for eliminating the special cause for errors and streamling the process and making the process to be a capable manufacturing process by improving the Cp and Cpk values of the critical to quality characteristic under study. The cause and effect diagram formed an important scientific tool for enlisting of the causes behind the poor performance of the process. On adapting the DMAIC approach, the estimated standard deviation “σ” of the crank-pin bore diameter is reduced from 0.017 to 0.009, while the process performance capability index Cpk is enhanced from 0.57 to 1.49. The Cp/Cpk values after performing the three iterations of data collection were greater than 1.33 and hence the process being declared as a capable process. After performing the root cause analysis, the major root cause, confirmed by the one-way ANOVA technique, was the wearing of the cutting tool inserts followed by improper pneumatic gage calibrations. Hence, the one-way ANOVA technique was employed successfully for identification of the root cause and its magnitude liable for the low process capability. This was further strengthened by the design of experiments results. 128
  • 14. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 5, September - October (2013) © IAEME REFERENCES: [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] “The transition from sampling to SPC”, by Schilling E.G.,Quality and Statistics ; Total Quality Management, ASTM STP1209, Milton J.Kowalewski, Jr. Ed.,American Society for Testing and Materials, Philadelphia, 1994. Locke, John W., “ Statistical Measurement Control” , Quality and Statistics: Total Quality Management, ASTM STP 1209, Milton J. Kowalewski, Jr., Ed., American Society for Testing and Materials, Philadelphia, 1994. Hung-Chin Lin, “The measurement of a process capability for folded normal process data”, Int J Adv Manuf Technol (2004) 24: 223–228, DOI 10.1007/s00170-003-1615-0,Springer-Verlag London Limited 2004 J. P. C. Tong, F. Tsung, B. P. C. Yen, “A DMAIC approach to printed circuit board quality improvement”, Int J Adv Manuf Technol (2004) 23: 523–531, DOI 10.1007/s00170-003-1721z, Springer-Verlag London Limited 2004 Ming-Hsien Caleb Li, Abbas Al-Refaie, and Cheng-Yu Yang, “DMAIC Approach to Improve the Capability of SMT Solder Printing Process” IEEE transactions on electronics packaging manufacturing, vol.. 31, NO. 2, April 2008, pg126 Yeong-Dong Hwang, “The practices of integrating manufacturing execution system and six sigma methodology”, Int J Adv Manuf Technol (2006) 30: 761–768, DOI 10.1007/s00170-0050090-1, Springer-Verlag London Limited 2006 Enzo Gentili, Francesco Aggogeri and Marco Mazzola “The Improvement of a Manufacturing Stream Using the DMAIC Method” University of Brescia Paper No. IMECE2006-14469, pp. 127-133; 7 pages, doi : 10.1115/IMECE2006-14469,ASME Chittaranjan Sahay, Suhash Ghosh and Pradeep Kumar Bheemarthi “Procee Improvement of Brake Lever Production Using DMAIC (+) “ University of HARTFORD, West Hartford, CT Paper No. IMECE2011-63813, pp.801-826; 26 pages, doi:10.1115/IMECE2001-63813, ASME 2011 Rupinder Singh, “Process capability study of polyjet printing for plastic components”, Journal of Mechanical Science and Technology 25 (4) (2011) 1011~1015 www.springerlink.com/content/1738-494x, DOI 10.1007/s12206-011-0203-8 Lin, S. J., Yang, D. L., Cheng, F. T., and Wu, M. F., “Aircraft Turbine Engine Manufacturing with Multiple Specifications,” Journal of Testing and Evaluation, Vol. 41, No. 1, 2013, pp. 1–7, doi:10.1520/JTE20120022. ISSN -0090-3973. A. Kumaravadivel & U. Natarajan, “Application of Six-Sigma DMAIC methodology to sandcasting process with response surface methodology”, Int J Adv Manuf Technol DOI 10.1007/s00170-013-5119-2, Springer-Verlag London 2013 U. D. Gulhane, C.A.Nalawade, K.P.Sohani , V.S.Shirodkar, “Six Sigma Implementation Model For File Manufacturing Industry” International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 59 - 66, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359, Published by IAEME Dr Z Mallick, Mr Shahzad Ahmad, Lalit Singh Bish, “Barriers And Enablers In Implementation Of Lean Six Sigma In Indian Manufacturing Industries” International Journal of Advanced Research in Management (IJARM), Volume 3, Issue 1, 2012, pp. 11 - 19”. ISSN Print: 0976 – 6324, ISSN Online: 0976 – 6332, Published by IAEME. 129