A DMAIC approach for process capability improvement an engine crankshaft manufacturing process

Sharma, G.V.S.S.; Rao, P. Srinivasa Article A DMAIC approach for process capability improvement an engine crankshaft manufacturing process Journal of Industrial Engineering International Provided in Cooperation with: Islamic Azad University (IAU), Tehran Suggested Citation: Sharma, G.V.S.S.; Rao, P. Srinivasa (2014) : A DMAIC approach for process capability improvement an engine crankshaft manufacturing process, Journal of Industrial Engineering International, ISSN 2251-712X, Springer, Heidelberg, Vol. 10, pp. 1-11, https://doi.org/10.1007/s40092-014-0065-7 This Version is available at: https://hdl.handle.net/10419/157401 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/2.0/ ORIGINAL RESEARCH A DMAIC approach for process capability improvement an engine crankshaft manufacturing process G. V. S. S. Sharma •P. Srinivasa Rao Received: 16 February 2014 / Accepted: 15 May 2014 / Published online: 27 May 2014 ÓThe Author(s) 2014. This article is published with open access at Springerlink.com Abstract The define–measure–analyze–improve–control (DMAIC) approach is a five-strata approach, namely DMAIC. This approach is the scientific approach for reducing the deviations and improving the capability levels of the manufacturing processes. The present work elaborates on DMAIC approach applied in reducing the process variations of the stub-end-hole boring operation of the manufacture of crankshaft. This statistical process control study starts with selection of the critical-to-quality (CTQ) characteristic in the define stratum. The next stratum constitutes the collection of dimensional measurement data of the CTQ characteristic identified. This is followed by the analysis and improvement strata where the various quality control tools like Ishikawa diagram, physical mechanism analysis, failure modes effects analysis and analysis of variance are applied. Finally, the process monitoring charts are deployed at the workplace for regular monitoring and control of the concerned CTQ characteristic. By adopting DMAIC approach, standard deviation is reduced from 0.003 to 0.002. The process potential capability index (C P ) values improved from 1.29 to 2.02 and the process performance capability index (C PK ) values improved from 0.32 to 1.45, respectively. Keywords Critical to quality (CTQ) characteristic  Cause and effect diagram Statistical process control (SPC) Process monitoring charts (PMC) Failure modes and effects analysis (FMEA) Analysis of variance (ANOVA) Physical mechanism (PM) analysis Introduction An engine crankshaft forms the most dynamic and crucial part of an engine. The manufacture of the crank shaft poses challenges as it involves as many as 23 critical to quality (CTQ) characteristics. Table 1enlists all the 23 CTQ characteristics associated with the crankshaft manufacturing operations. One such CTQ characteristic is the stubend-hole diameter formed by boring operation. This machining operation is important because, after this operation the finishing line operations of crankshaft journals’ and pins’ grinding and lapping succeed. So the stub-endhole boring operation is the last roughing line operation of the crank shaft and is also a bottleneck operation. This is the inspiration behind to select the stub-end-hole boring operation for process capability improvement study. Statistical process control (SPC) widely employs various process monitoring charts for determining whether the process under consideration is performing within the specified limits are not. Process monitoring charts gives a graphical description of the process performance and it instantly helps the process personnel to differentiate chance causes from assignable causes. Process capability indices are the measure of efficiency of the process to produce the product within the specified dimensional tolerance limits. To be specific, C P is the process potential capability index and C PK is the process performance capability index. C P gives a measure of the variation and deviation in the G. V. S. S. Sharma (&) Department of Mechanical Engineering, GMR Institute of Technology, Rajam 532127, AP, India e-mail: [email protected] P. S. Rao Department of Industrial Engineering, GITAM University, Visakhapatnam 530045, AP, India 123 J Ind Eng Int (2014) 10:65 DOI 10.1007/s40092-014-0065-7 process. The higher the C P value, the less the variation and deviation in the process. C PK , on the other hand, is obtained from C PKU and C PKL . As a precaution of safety, the lower value among the C PKU and C PKL is considered to be the value of C PK . Elaborating on this, it can be said that centering of the process within the specification limits is done by C PK . It provides an indication whether the process is operating at the center of the specified tolerance zone or nearer to the upper or lower specification limits. Literature review Schilling (1994), has thrown light on the superiority of process control over the traditional sampling techniques. Locke (1994), stressed on the importance of process charts, cause and effect relationship and control charts. Lin (2004), had emphasized on process capability indices for normal distribution. Tong et al. (2004), focused on define–measure–analyze–improve–control (DMAIC) approach and its application for printed circuit board quality improvement. Li et al. (2008) adopted DMAIC approach to improve the capability of surface mount technology in solder printing process. Hwang (2006) employed the DMAIC procedure in context of application to manufacturing execution system. Gentili et al. (2006) applied the DMAIC process for a mechanical manufacturing process line, which manufactures both professional and simple kitchen knives. Sahay et al. (2011) used the DMAIC approach for analyzing the manufacturing lines of a brake lever at a Connecticut automotive component manufacturing company. Singh (2011) improved the process capability of polyjet printing for plastic components and charted the procedure for attaining the C pk value attainment [1.33, i.e., [4 sigma level, which is considered as industrial benchmark. Lin et al. (2013) elaborated on the accurate yield assessment of the processes of multiple characteristics of the turbine blade manufacturing process. Kumaravadivel and Natarajan (2013) applied the cause and effect matrix and failure modes and effects analysis (FMEA) for solving problems associated with flywheel casting process. Mariajayaprakash et al. (2013) identified the CTQ characteristics of shock absorber manufacturing process and improved the process by minimizing the defects using Taguchi approach. Genetic algorithm was applied to optimize the parameters using Taguchi approach. Chen et al. (2013) applied Taguchi’s orthogonal array and analysis of variance (ANOVA) to find the optimal values for the nine process parameters namely, injection time, injection pressure, packing time, packing pressure, cooling time, cooling temperature, mold open time, melt temperature and mold temperature, in a plastic injection molding process. Lal et al. (2013) in their paper discussed about the performance of piston manufacturing plant through stochastic models. They concluded that the time-dependent availability of the piston manufacturing plant is affected by fixture seat machining and circlip grooving. The literature cited here reveals that the DMAIC approach is widely used for process capability improvements across the manufacturing sector. Hence, without any iota of doubt, this paper straightaway adopts the DMAIC approach for process capability improvement of the stubend hole boring operation of an engine crankshaft manufacturing process. Definition stratum The definition stratum starts with defining the project charter and then the mapping of the machining sequence flow of the crankshaft. It is followed by identifying the CTQ characteristic of interest, for the scope of improvement study. Table 1 The project charter Objectives To recognize crankshaft stub end hole operation as a process capable operation To relieve the stub end hole operation from being a bottle-neck and with a smooth work-in-flow without any staggered inventory Deliverables and success metrics To achieve the process potential capability index and process performance capability index, i.e., C P and C PK values for the stub end hole boring operation of the crankshaft to be [1.33, i.e., more than 4 sigma levels The C P and C PK values to be achieved consistently [1.33 for over a persistent period of 3 months Business impact Raise the process capability levels and awareness of the importance of process monitoring charts in daily production. Reduce the component rejection and rework by 99 % in the first 6 months after sustenance S. no. Components of the project area Value 1 Total no. of rejects and rework per day, i.e., three production shifts of 8 h each =15 crankshafts 2 Time taken for segregation and rework of components =1 h per day 3 Production loss due to rejection and rework per month =30 91=30 h 4 Monetary loss of 1 h delay in the crankshaft cell =$7,000 5 Total monetary loss per month with 25 working days per month =25 9$7,000 =$175,000 6 By avoiding 99 % of rejections & rework, the amount that can be saved per month is =0.99 9$175,000 =$174,240 Scope of this DMAIC project is limited to the Engine Plant of M/s Hindustan Motors Ltd, Pithampur Power Unit Plant, India 65 Page 2 of 11 J Ind Eng Int (2014) 10:65 123 Project charter The project charter for the process capability improvement of the stub-end hole boring operation of the crankshaft machining process is depicted in Table 1. The project charter outlines the objectives, deliverables and success metrics of this improvement project. The business impact in terms of monetary benefits is also reflected in the project charter. Process mapping The process flow chart for machining line of the crankshaft manufacturing cell consists of the following machining operations sequence, as shown in the Fig. 1. The manufacturing sequence of the crankshaft consists a total of 15 operations. Of all the manufacturing operations, the operation number 90 is of concern. The operations from 10 to 90 constitute the roughing line operations involving mainly facing and centering, turning, whirling, drilling, tapping and chamfering. The operations from 100 to 150 constitute the finishing line operations involving mainly grinding, sursulfing and lapping. Table 2depicts the description of the machining operations of crankshaft manufacturing cell along with the associated CTQ characteristics. Identifying CTQ characteristic Timing belt pulley is located onto the stub-end-hole of the crank shaft. Hence if the stub-end-hole is undersize or oversize, it leads to incorrect fitment of the timing belt pulley and subsequently the timing belt. Ultimately, this leads to incorrect timing of fuel combustion within the cylinders and detonation and knocking. Therefore, from functional view-point, the Stub-end-hole diameter forms a CTQ characteristic. Second, from manufacturing viewpoint, the stub-end hole is the last operation performed on the roughing line and it forms the locating reference for the subsequently operations in the finishing line. Any small variations in the stub-end-hole boring operations are carried to the finishing operations of journal and pin grinding. Hence, unanimously, the stub-end-hole boring operation forms the prime choice for identifying it as a CTQ characteristic. Figure 2shows the pictorial representation of the CTQ characteristic. Measurement stratum The measurement stratum involves data collection of the critical to quality characteristic and is performed for 32 consecutive machined components. Data collection is performed in 3 iterations spanning for a period of 4 weeks each, i.e., about 900 consecutive components. The data are tabulated in the tabular form in Table 3and the graphical plot of the three sets of measurement iterations is captured in Fig. 3, in the form of process monitoring charts. Analysis stratum The analysis stratum comprises of performing the calculations for the C P and C PK values across each iteration. In this stratum, the root cause analysis is performed with the help of various quality control (QC) tools like cause and effect diagram and physical mechanism analysis. Prioritization of the corrective actions is extracted from the output of process FMEA. This is followed by one-way ANOVA method of investigation to test the differences between the three iterations of the data sets. Calculations of C P and C PK The calculations of C P and C PK are charted in Table 4. Based on the process monitoring charts and calculations of C P and C PK the following analysis is performed: Iteration no. 1 primarily indicates the primitive status of the problem on hand before carrying out any improvement work. Continuous sets of measurements of the CTQ characteristic are taken and it is seen that the C P and C PK values here are below the target value of 1.33, with C P value equal to 1.29 and C PK equal to 0.32. Fig. 1 Process flow chart J Ind Eng Int (2014) 10:65 Page 3 of 11 65 123 Iteration no. 2 corresponds to the intermediary phase readings after performing moderate improvements like setting up a standard procedure for tool-insert setting on boring bar, cleaning the filter–regulator–lubrication (FRL) unit of pneumatic gauges and replacing the worn out cutting tool insert edge. In Iteration 2, a slight cyclic pattern is observed. This is because of the reason that there is a constant progressive wear out of the boring bar insert on continued boring operation. After every ten components being machined, the boring bar insert must be compensated for the wear by elevating the insert by about five micrometers, over the diametric dimension. This exercise is performed with the help of a test mandrel and a boring bar tool insert setting Vee-block. Because of worn out Veesurfaces of tool insert setting Vee-block, though the insert setting is done it is not accurate and this is reflected in the cyclic pattern observed in the second optimization step of Iteration 2. As a measure of corrective action, a new insert setting Vee-block is replaced with the old worn-out piece and the Vee surfaces of the Vee-Block are case hardened to achieve hardness up to 55 HRc with a case depth of 0.5–0.8 mm. In this iteration we see a marginal increase in C P to 1.32 and C PK to 0.90. Finally, Iteration no. 3 corresponds to the final phase readings which are taken after introducing the corresponding corrective actions identified in the analysis stratum and bringing in a noticeable improvement in the process performance with C P =2.02 and C PK =1.45. Cause and effect diagram The cause and effect diagram, also known as Ishikawa diagram or fish-bone analysis, is a directional approach where the common as well as special causes are classified under the heading of 4 M’s, i.e., man, material, method and machine. All the causes are directed towards the common effect namely, C P and C PK of the CTQ being \1.33. Figure 4below charts the detailed cause and effect diagram pertaining to this research. Through the cause and effect diagram, and the PM analysis, the various causes for the poor performance of the machining operation can be identified and corrective actions are taken upon based on the prioritization by the risk priority number (RPN) generated in the FMEA. Physical mechanism analysis The PM analysis or in other words physical mechanism analysis is a QC tool originated during the quality improvements under the Hinshitsu Hozen pillar of total productive maintenance. The same concept is applied to this research. Physical mechanism analysis starts with the identification of the ‘‘physics’’ related to the machining operation under study. The direction of components of the forces acting at the ‘‘junction point’’ of the cutting tool and the workpiece are identified. Conceptually, this Junction-point of the contact of tool and workpiece is the area which involves the cutting forces acting on tool as well as on workpiece, heat transfer, frictional forces opposing the cutting force. It is at this point where the cutting action starts for single as well as multi-point cutting tool. The ‘‘junction point’’ involves three aspects, namely: Table 2 Machining operations of connecting rod manufacturing cell Machining operation no. Description of machining operation CTQ characteristic pertaining to the machining operation 10 Facing and centering Center to center distance Locating hole diameter and chamfer angle 20 Web milling Web flatness 30 CNC rough turning of journals Journal diameter 40 Pin whirling Phase-out of pins Throw of pins Pin diameter after whirling Journal diameter after whirling 50 Finish turning of journals Journal diameter after finish turning 60 Oil hole gun drilling Oil hole diameter 70 Flange end finish turning Flange diameter Flange circularity 80 Bolt hole PCD drilling and tapping Bolt hole pitch circle diameter Thread pitch of bolt holes 90 Stub end boring and chamfering using a combination boring-cumchamfering inserts tool bar Stub-end-hole diameter Chamfer angle 100 Journal grinding Journal finish diameter Journal circularity 110 Pin grinding Pin finish diameter Pin circularity 120 Magnetic crack detection Check for internal cracks 130 Sursulfing heat treatment Hardness of journals and pins after sursulfing operation 140 Lapping of pins and journals Surface roughness of journals and pins after lapping 150 Final inspection quality check 65 Page 4 of 11 J Ind Eng Int (2014) 10:65 123 Cutting tool aspect Cutting tool insert indexing, insert changing for single point cutting tool like boring bar; cutting tool dressing for a multi point cutting tool like grinding wheel; cutting tool holder location and clamping aspects; calibration of cutting tool setting in the tool-pre-setting area. Workpiece aspect Work location, work holding, work clamping, work supporting, power required for driving the work and related machinery, coolant circulation continuity for flushing out the chips generated in the cutting process, heat dissipation by the coolant, Measurement aspect Measurement system comprising of the go and no-go gauges on the shop floor, calibration of gauges, the pneumatic pressure fluctuation in the pressure lines corresponding to the pneumatic gauges, the FRL unit maintenance, in-process sensing instrument sensitivity and repeatability. Process failure modes and effects analysis (FMEA) Process FMEA is an approach for prioritizing the sequence of corrective actions. Based on the severity, occurrence and detection ratings the RPN is calculated. The RPN[100 are of concern and liable for corrective action. The detailed FMEA sheet for stub-end-hole boring operation is tabulated in Table 5. Among the various enlisted causes, the cause which most affects and responsible for the poor performance of (a) (b) XX Fig. 2 Stub-end-hole diameter where, X=hole of ø30.000 (?0.020/0.000) .aPhotograph of the crank shaft, bstub-end-hole modeled in CATIA V5R14 Table 3 Dimensional readings of Stub-end-hole spanned over three iterations S. no. Iteration 1 Iteration 2 Iteration 3 01 30.003 30.007 30.010 02 30.000 30.004 30.010 03 30.004 30.012 30.008 04 30.005 30.010 30.007 05 29.998 30.012 30.008 06 30.005 30.006 30.006 07 30.004 30.007 30.006 08 30.001 30.008 30.007 09 30.006 30.005 30.005 10 29.999 30.007 30.005 11 30.004 30.006 30.010 12 29.999 30.003 30.009 13 30.000 30.011 30.009 14 30.004 30.008 30.008 15 30.002 30.005 30.008 16 30.000 30.007 30.007 17 30.005 30.010 30.006 18 30.005 30.005 30.006 19 30.000 30.007 30.005 20 30.006 30.006 30.005 21 30.002 30.002 30.010 22 30.006 30.010 30.008 23 29.999 30.005 30.008 24 30.006 30.007 30.009 25 30.001 30.005 30.007 26 30.003 30.004 30.007 27 29.999 30.007 30.007 28 30.005 30.003 30.006 29 30.000 30.008 30.006 30 30.003 30.005 30.005 31 30.000 30.008 30.005 32 30.005 30.007 30.006 J Ind Eng Int (2014) 10:65 Page 5 of 11 65 123 the CTQ characteristic, is found by using the ANOVA technique. Analysis of variance (ANOVA) ANOVA starts with the formulation of the hypothesis to be tested, followed by, tests for the assumption about the normality of the data and the homogeneity of variance among the sets of the data. A post hoc analysis is required if F STATISTIC is found to be [F CRITICAL . Formulating the hypothesis and testing the assumptions for normality of data and homogeneity of variance The null hypothesis (H 0 ) and the alternate hypothesis (H 1 ) can be formulated in the present context as H0:li¼lall i¼1;2;3; H1:li¼lfor some i¼1;2;3; where l i is the population mean for level i, and lis the overall grand mean of all levels. With respect to the data in Table 3, it is seen that there are 3 levels (i.e., 3 iterations) with each level consisting of 32 measurement readings of stub-end-hole diameter of connecting rod. The data plot for normality is captured in Fig. 5, and the histogram bar chart is captured in Fig. 6. The pre-requisite for performing one-way ANOVA test is to find departure from normality and to check the homogeneity of variance among the sets of the data. The normal probability plot is seen linear with equispaced values and this is supported by the histogram with near bell-shaped curvature. The Pvalue is \0.005, which is less than the a value of 0.05, thereby indicating that a linear relationship exists with normality retained. The results are further strengthened by the fact that there are no unusual data points. The sample size of 32 is sufficient to detect differences among means and because all the sample sizes are [15, normality is not an issue. Also based on the data observations and alpha level of 0.05, there is at least a 90 % chance of detecting a difference of standard deviation of 0.0023297 and at most a 60 % chance of detecting a difference of standard deviation of 0.0013632. Finding the F STATISTIC The ANOVA Table obtained from Minitab software is captured in Table 6. Here it is seen that FSTATISTIC ¼41:24:ð1Þ An avalue of 0.05 is typically used, corresponding to 95 % confidence levels. If ais defined to be equal Fig. 3 Process monitoring charts Table 4 Calculations of C P and C PK Formula Iteration 1 Iteration 2 Iteration 3 USL 30.020 30.020 30.020 LSL 30.000 30.000 30.000 r0.003 0.003 0.002 CP¼USLLSLðÞ 6r 1.29 1.32 2.02 CPKU ¼USLMEANðÞ 3r 2.26 1.75 2.60 CPKL ¼MEANLSLðÞ 3r 0.32 0.90 1.45 C PK =min (C PKU, C PKL ) 0.32 0.90 1.45 65 Page 6 of 11 J Ind Eng Int (2014) 10:65 123 to 0.05, then the critical value for rejection region is F CRITICAL(a,K-1, N-K). and is obtained to be 3.094. Thus, FCRITICAL ¼3:094:ð2Þ Hence, it is seen that FSTATISTIC [FCRITICAL:ð3Þ Therefore, the decision will be to reject the null hypothesis. If the decision from the ANOVA is to reject the null hypothesis, then it indicates that at least one of the means (l i ) is different from the remaining other means. In order to figure out where this difference lies, a post hoc ANOVA test is required. 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 ¼qffiffiffiffiffiffiffiffiffiffiffiffiffi MSTE n r¼3:38 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0:53 105 32 r¼0:00137;ð4Þ where qis the studentized range statistic which is equal to a value of 3.38, for a df of 93 and k=3, i.e., number of levels as 3. If ‘‘C1’’ denotes ‘‘Iteration 1’’, ‘‘C2’’ denotes ‘‘Iteration 2’’ and ‘‘C3’’ denotes for ‘‘Iteration 3’’, then from Minitab software, the following grouping information using Tukey method is shown in Table 7: From Table 7, it is seen that means that do not share a letter are significantly different, i.e., Iteration 1 is significantly different from Iterations 2 and 3. The pairwise comparison using Minitab is depicted in Table 8. In Table 8, it is seen that the pairwise comparison between C2 and C3 is 0.000375 which is less than HSD is Eq. (3), whereas the pairwise comparison between C1 and C2 is 0.004312 and that between C1 and C3 is 0.004687, which are greater than that of the HSD in Eq. (4), with the difference 0.004687 being the largest. So, it is deduced that the differences 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 cutting tool insert, insert setting v-block wear out and non-calibration of the vernier calipers and tool setting mandrel. Control stratum In the control stratum, the X-bar and Rcontrol charts are implemented at the workplace for monitoring the process and preventing it from deviating. These charts ensure that the process remains capable and is prevented from Fig. 4 Cause and effect diagram J Ind Eng Int (2014) 10:65 Page 7 of 11 65 123 Table 5 FMEA sheet Process name Potential failure Potential effect Severity Potential cause Occurrence Current controls Detection RPN Stub-endhole boring operation number 90 Stub-end-hole oversize Component rejection 7 Inaccurate insert pre-setting on the boring bar by operator 7 Tool-presetting checklist 8 392 Loose fit of timing belt pulley in assembly 9 Tool insert setting v-block wear out 6 Calibration checklist included in measurement system analysis (MSA) 3 162 Calibration of tool insert setting mandrel 5 Calibration checklist included in measurement system analysis (MSA) 3 135 Calibration of vernier caliper 5 Calibration checklist included in measurement system analysis (MSA) 3 135 Stub-end-hole undersize Component rework 7 Overused and worn out cutting tool inserts 7 Tool-indexing checklist 4 192 Calibration of vernier caliper 5 Calibration checklist included in measurement system analysis (MSA) 270 Insert pre-setting on the boring bar 7 Tool-presetting checklist 3 147 Geometrical dimensional variations of stub-end hole Variations in axiality, cylindricity and position of hole 6 Loose Insert and insert loosening in the middle of operation 2 Insert torque checklist 224 Worn out locating pads on the locating Vee of fixture 3 Fixture calibration check-list 236 Fig. 5 The normal plot of residuals for stub-end hole dimensional observations 65 Page 8 of 11 J Ind Eng Int (2014) 10:65 123