M. Hussain, J. Gu, R. Engel, and D. Shortt *General Motors Corporation, GMPT Headquarters, Pontiac, Michigan USA

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1 Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management Dubai, United Arab Emirates (UAE), March 3 5, 2015 Designed experiment to find the optimal combination of the factors for the coordinate measuring machine (CMM) to measure cylindricity of engine cylinder bore M. Hussain, J. Gu, R. Engel, and D. Shortt *General Motors Corporation, GMPT Headquarters, Pontiac, Michigan USA Abstract This experimental study investigated the relationship between scan speed, point density, outlier elimination, probe diameter, probing force, filtration, part location in the measuring envelope of a CMM, and part orientation in the CMM. The goal of this investigative study was not only to find the best optimal combination of the factors but also to determine factor levels in CMM that influence cylindricity measurement. The results of the CMM measurements were compared with FAG (a reference form measuring machine) and verified the CMM capability and accuracy of measuring the cylinder bore geometry. A quarter fraction experimental design with 64 runs was conducted to gain knowledge among all factors. A confirmatory run was performed to validate the findings. bore surface to its axis [4]. Deformation in cylinder bore geometry can cause excessive consumption of lubricating oil and also create irregular and abnormal wear pattern [5]. CMM and Incometer are the two most common devices in the automotive industry that can measure cylindricity of the bores. Zhu and Ding [6] in their study investigated the problem of determining the cylindricity error of a mechanical part using measured points by a CMM. Chou and Sun [7] developed a mathematical model in order to fine tune the algorithm to find the axis of the reference cylinder. Their optimum goal was to yield assessment of the cylindricity by using simulated algorithm. Keywords: CMM, Cylinder Bore measurement; Incometer, Engine Block, DOE, Bore Geometry 1. Introduction In order to produce a quality engine it is necessary to have a robust machining, assembly and measurement system for cylinder bores. Fujimoto et al. [1] Deformation of the cylinder bore is introduced by combination of static deformation due to head bolt clamp load, head gasket design etc., and by operating factors of gas explosion pressure in the bore and their thermal affects. They concluded that bore expansion due to gas pressure was evenly distributed while deformation due to thermal load was oval shape in the direction of thrust. Their study measured the deformation of the cylinder bore geometry while engine was operating. The deformation could also be introduced during machining of the engine block. During machining cylinder bores, machine tool accuracy, design deficiencies, close loop gaging system, thermal effect due to machining plays a major role in the deformation of bore geometry. Geometry of the cylinder bore is seriously affected by the above factors. Piston rings can cover a limited amount of distortion inside the bore. It is very important to have excellent bore geometry to avoid premature engine failure. A loose seal between the piston, ring and cylinder bore can cause problems like; high fuel consumption, high lubricating oil consumption, high exhaust emission and engine wear [2, 3]. Cylindricity is a complex call out to measure abnormalities of the cylinder Figure 1: Engine Block CMM performs variety of measurements on many components as shown in Figure 2. CMMs are widely used in industry for length and positional measurements. These days CMMs have become more sophisticated, since they can also perform precision measurements like roundness and cylindricity. 541

2 to conduct the study. The cylinder bores are finished honed; the cylindricity specification is 0.013mm maximum. This study measured the bore geometry in Free State at 68 F. In the literature research there is no evidence of any experimental evaluation of cylindricity using these variables and their effect on cylindricity of a cylinder bores using CMMs. Most of the literature is about algorithm development and mathematical models. There is some work done on noncontact optical methodology to measure machining marks in the cylinder bore surface [8]. They developed a cylinder bore probe to differentiate between different machining patterns of machining marks. Figure 2: Zeiss Vast Prismo Coordinate measuring machine (courtesy Zeiss) Incometer is another hand held sophisticated measuring equipment that can evaluate cylindricity. Based on its construction and care it is considered laboratory equipment for detail results. There are many ways to approach a particular designed experiment. In order to evaluate the results of a CMM to measure cylindricity, a quarter fraction design with 64 runs was chosen for this study. This experimental design is a powerful one because it can detect all main factor effects and all two-fold interactions. This provides significant factor information without the excessive cost of a full factorial design. The population in this study is inline engine aluminum block. Each test run was conducted on a random basis. Only cylinder bore # 4 in one cylinder block was used 2. Data Collection Technique This study was conducted in the Central Metrology Laboratory of General Motors Powertrain Headquarters Pontiac. The part print specifies diameter measurement levels at X1, X2, and X3mm from the deck face. Additional measuring levels of Y1, Y2, Y3, Y4, Y5, and Y6mm were defined by the experimenter to fill in between the levels specified by the part print in order to more thoroughly evaluate cylindricity. The "true measurement" of the cylindricity was determined by the FAG form measuring machine in the GMPT Metrology Laboratory. The FAG form measuring machine measured the cylindricity of the bore five times at the same specified levels. The average value of these five measurements, were considered the "true measurement". These measurements were done at the same measuring levels as the CMM. All measurements by the CMM were compared to that value. The dependent variable is cylindricity measured in microns. The independent variables for the CMM are listed in Table 1. Table 1: Independent Factors and their Levels for CMM study Independent Factors and their Levels for CMM study Dependent Variable Number Factor Code High Low Factor Type Cylindricity 1 Scan Speed s 25 mm/sec 10 mm/sec Variable 2 Point density p 1 Point/Degree 1 Point/4Degree Variable 3 Outlier Elimination o 4 Sigma 2 Sigma Variable 4 Probe diameter pr 10 mm 8 mm Variable 5 Probing Force f 1.6N 0.2N Variable 6 Filtration fi On* Off Attribute 7 Part Location l Front Rear Attribute 8 Part Orientation or Bore Vertical Bore Horizontal Attribute 542

3 3. Data Analysis Technique A quarter fraction designed experiment with eight factors and two levels requires 64 runs. Tables 2 show the design matrix with results. The quarter fraction design detected all main-factors effects as well as all 2-fold interactions. The experiment was analyzed by ANOVA and regression. Equipment The following equipment was required: (a) Honed L-6 cylinder block (b) FAG Form Measuring machine (c) Coordinate Measuring Machine (d) Accessories ( Fixtures and misc. tools for set up) (e) Computer: This was used to analyze the results. Statgraphics and Excel software were employed. 4. Experiment Preparation The data from experiments, for CMM was collected. Great care and attention were paid to ensure that each individual run was conducted carefully according to the specified factor levels. The measurements were performed by two operators. Both operators were trained to perform measurements without adding any additional variability to the study. The temperature of the part was measured before each measurement to ensure thermal growth of the part and the equipment. The results were analyzed to obtain the optimum values for the design variables. Cleanliness of the measuring probe was ensured. The target value from FAG machine The target value for this experiment was set by FAG machine. The cylindricity value from the FAG machine was calculated by averaging 5 consecutive measurements taken at the same cylinder bore measuring elevations as the CMM. The FAG machine measurements were taken after CMM measurements, in order to stay unbiased. The calculated target value was 4.5 microns Data/Information Collected The data collected were cylindricity measurements. CMM experiments had sixty-four runs. Since the experiments are very expensive in nature, there were no replications. Cylindricity was the dependent variable. The independent variables for the CMM were scanning speed, point density, outlier elimination, probe diameter, probing force, filtration, part location in the measuring envelope of the CMM, and part orientation. Table 2 is a summary of the experimental results for the CMM Table 2. Results of the experimental run for CMM Independent Factors Dep. Factor Run s p o pr f fi l or Cylindricity mm/sec. poi./deg. sigma mm newt. und/rev. frt./rear ver./hori um OFF REAR Bore Vert ON REAR Bore Hori ON FRONT Bore Hori OFF FRONT Bore Hori ON FRONT Bore Vert OFF FRONT Bore Vert ON REAR Bore Vert OFF FRONT Bore Hori ON FRONT Bore Vert ON REAR Bore Hori OFF REAR Bore Hori ON FRONT Bore Vert OFF REAR Bore Vert OFF FRONT Bore Hori OFF REAR Bore Vert OFF FRONT Bore Vert ON FRONT Bore Vert OFF FRONT Bore Vert OFF REAR Bore Vert ON REAR Bore Hori ON FRONT Bore Vert ON REAR Bore Hori ON REAR Bore Vert ON REAR Bore Vert OFF FRONT Bore Hori OFF FRONT Bore Vert ON FRONT Bore Hori ON REAR Bore Hori

4 ON FRONT Bore Vert ON REAR Bore Vert ON REAR Bore Vert ON FRONT Bore Hori ON REAR Bore Hori OFF REAR Bore Vert ON REAR Bore Vert OFF FRONT Bore Vert OFF FRONT Bore Hori ON FRONT Bore Vert OFF FRONT Bore Hori OFF REAR Bore Hori OFF REAR Bore Vert OFF REAR Bore Hori ON FRONT Bore Hori OFF FRONT Bore Vert OFF FRONT Bore Hori OFF REAR Bore Hori OFF FRONT Bore Vert OFF REAR Bore Hori OFF REAR Bore Hori OFF REAR Bore Hori ON REAR Bore Vert ON REAR Bore Hori OFF FRONT Bore Hori ON FRONT Bore Hori ON FRONT Bore Hori OFF REAR Bore Vert OFF REAR Bore Hori OFF REAR Bore Vert OFF FRONT Bore Vert ON REAR Bore Vert ON FRONT Bore Vert ON FRONT Bore Hori ON REAR Bore Hori ON FRONT Bore Hori Model The general equation describing the relation between a response variable and the factors of the design is a linear model. If Y is the response variable and x1, x2,, xn are the factors, then the linear response surface model will take the form Y = + x1 + ijxixj.. consisting of linear terms and all two-fold interactions of the factors. is the constant term and ij are coefficients associated with all factor interactions. 6. Data Analysis The analysis tools used in this experiment to determine the significant main effects and 2-factor interactions were backward stepwise Regression, weighted least square regression, and analysis of variance (ANOVA). Backward stepwise regression analysis starts with all main factors and 2-factor interactions; it then eliminates insignificant factors and interactions one at a time based on the F-ratio statistic. The larger the F-ratio, the more significant the factor or interaction are. For this experiment an F-ratio of 3 was used to determine significance of the factor and interaction. The final model was determined based on maximizing R-squared while trying to minimize the mean squared error simultaneously. Weighted least square regression was used to compensate for a non-normal residual pattern. Weighted fudge factor of 1/um 2 was used for the final regression model. The fudge factor does not appear in the data. The output shown in Table 3 shows the results of fitting a multiple linear regression model to describe the relationship between cylindricity (um) and 36 independent variables. The equation of the fitted model is ; um = *fi *or *p *pr *fi*p *l*p *or*p *or*pr *or*s *p*pr *p*s. Since the p-value in the ANOVA table is less than 0.01, there is statistically significant relationship between the variables at the 99% confidence level. The R-squared statistic indicates that the model as fitted explains % of the variability in cylindricity (um). The adjusted R-squared statistic, which compares models with different numbers of independent variables, is 544

5 %. The standard error of the estimate shows the standard deviation of the residual to be This value can be used to construct prediction limits for new observations. The mean absolute error (MAE) of is the average value of the residuals. The Durbin-Watson (DW) statistic evaluates the residuals to determine if there is any significant correlation based on the order in which they occur in this data file. Since DW the value is greater than 1.4, there is probably not any serious autocorrelation in the residuals. In determining whether the model can be simplified, notice that the highest p-value among the independent variables is , belonging to factor p. Since the p-value is less than 0.10, that term is statistically significant at the 90% confidence level. Table 3: Model fitting results of the backward stepwise regression analysis and ANOVA for full regression Multiple regression analysis Dependent variable: um Parameter Estimate Standard T p-value Error Statistic Constant fi or p pr fi*p l*p or*p or*pr or*s p*pr p*s Analysis of variance Source Sum of Squares Df Mean Square F-Ratio p-value Model Residual Total (corr.) R-Squared = percent R-Squared (adjusted for d.f.) = percent Standard Error of Est. = Mean Absolute Error = Durbin-Watson statistic =

6 7.2 Plot of um 5 Means and 95.0 Percent LSD Intervals observed um predicted Figure 3: Plot observed Vs Predicted values residual Residual Plot predicted um Figure 4: Plot of residual Vs predicted values of cylindricity before WLSR residual Residual Plot predicted um Figure 5: Plot of residual Vs predicted values of cylindricity after WLSR The goal of this designed experiment was to determine factor levels which produce cylindricity measurements that match the FAG machine results as closely as possible and also find the optimum combination of settings of all the variables to produce consistent results Figure 6: Plot of Filtration at High and Low levels um fi Interaction Plot -1 1 Figure 7: Interaction Plot of Orientation and Scan Speed at High and Low levels um or Interaction Plot 1 4 Figure 8: Interaction Plot of Point Density and Scan Speed at High and Low levels The factors that gave the best results and their optimum values based on graph interpretations as shown below. In order to determine the optimum levels, plots of main factors and plots of interactions were used for CMM. The optimum factor levels are given below. p s s

7 Factors Optimum Level s Speed (-1) 10 mm/sec p Point density (1) 1 Point/Degree o Outlier Elimination (1) 4 Sigma pr Probe Diameter (1) 8 mm f Force (1) 1.6 newtons fi Filtration (1) on 50 un/rev. l Location (1) Front or Orientation (1) Bore Vertical The optimum levels for the CMM were directly interpreted from Figures 6, 7, and 8. In Figure 6 the graph shows that the higher level of the filtration i.e., 50 un/rev. produces cylindricity closer to the FAG target value of 4.5 um. So the optimum level of the filtration is at on i.e., 50 undulations/rev. In Figure 7 the interaction plot of orientation and scan speed it shows that the combination of scan speed at low level (10) and orientation at high level (1) is closest to the target value. So the optimum level for scan speed is 10 mm/sec and the optimum level of orientation is bore vertical. In Figure 8 the interaction plot of point density and scan speed shows that the combination of scan speed is at the low level and the point density is at the high (1) level the result is closest to the target value. Thus, the optimum value of the scan speed is 10 mm/sec, and the point density is at 1 point/ degree. Table 4 shows that, since the % contribution of the factors/interactions was < 5%, they did not play any significant role in the experiment; therefore they may be set at any level. The contributions of the main effects and 2- factor interactions are provided by using the ANOVA sum of square method. Table 4 shows the percent contributions of the factors. Figure 5 shows the Pareto chart of % ANOVA contribution of factors. Table 4: Percent Contribution of Main Effects and 2-Factor Interactions Factors Variance % Contribution Error % Fi % Or % P % Pr % Fi*P % L*P % Or*P % Or*Pr % Or*S % P*Pr % P*S % 547

8 % contribution % Anova contribution P*S Or*S Or P Fi Or*Pr Fi*P P*Pr L*P Pr Or*P Factors Figure 9: Pareto Chart of %ANOVA for CMM The analysis of eight factors at two levels was used to determine the significant factors and the best combinations of any significant 2-factor interactions to measure cylindricity of the cylinder bore with the CMM. The importance of the 2-factor interactions in relation to the main effects was of particular interest. With the CMM, it was discovered that filtration had a significant effect on cylindricity as a main factor. The 2-factor interactions, (orientation)*(scan speed), and (point density)*(scan speed) were significant. 7. Conclusions A linear model of cylindricity by CMM was obtained for the factors: scan speed, point density, outlier elimination, probe diameter, probing force, filtration, part location in the measuring envelop, and part orientation. The optimum levels of the design factors from this experimental study were identified. A confirmation run was made using the optimized parameter levels. This confirmed the validity of the predictions. The design variable came very close to the target value. References [1] H. Fujimoto, Y. Yoshihara, T. Goto, S. Furuhama, Measuring of cylinder bore deformation during actual operating engines, International congress and exposition Detroit, Michigan February 25-March 1, 1991 SAE Technical Paper [2] E. W. Schneider, Effect of cylinder bore out-ofroundness on piston ring rotation and engine oil consumption, Society of Automotive Engineers, , pp , [3] S. H. Hill, Piston ring designs for reduced friction, Society of Automotive Engineers, , pp. 1-20, [4] ASME Y , Geometric Dimensioning and Tolerancing. [5] Hitosugi, H. et al. (1996) JSAE Review, 17 (2), pp Study on cylinder bore deformation of dry liner in engine operation. [6] L. Zhu and H. Ding, Application of kinematic geometry to computational metrology: distance function based hierarchical algorithms for cylindricity evaluation, International Journal of Machine Tools and Manufacture 43 (2003) [7] Chou SY, Sun CW Assessing cylindricity for oblique cylindrical features International Journal Of Machine Tools & Manufacture, Volume: 40 Issue: 3 Pages: Published: FEB [8] Agapiou, J.S., Segall, S. Evaluation of cylinder bore inspection system, (2009) Transactions of North American Manufacturing Institute of SMS, 36, pp