Investigating Cavitation Peening Parameters for Fatigue Performance Using Designed Experiment

Transcription

1 Western New England University From the SelectedWorks of Mohammadsadegh Mobin Summer May 2, 2015 Investigating Cavitation Peening Parameters for Fatigue Performance Using Designed Experiment Mohammadsadegh Mobin, Western New England University Afshan Roshani, Western New England University Mahmood Mobin Ali Rastegari Available at:

2 Proceedings of the 2015 Industrial and Systems Engineering Research Conference S. Cetinkaya and J. K. Ryan, eds. Investigating Cavitation Peening Parameters for Fatigue Performance Using Designed Experiment Mohammadsadegh Mobin, Afshan Roshani Western New England University, MA, USA Mahmood Mobin Amirkabir University of Technology, Tehran, Iran Ali Rastegari Volvo GTO, Västmanland, Sweden Abstract Mechanical surface enhancement techniques such as cavitation peening (CP) have various design parameters which can affect the fatigue performance of the part. In this study, a full factorial design of experiment is applied to investigate the effects of cavitation peening process parameters on the fatigue performance of carburized steel. These parameters include standoff distance, cavitation number, nozzle size, and exposure time. The response variables considered in this experiment include residual stress, surface roughness and austenitic ratio. Results obtained from full factorial design of experiment method were compared with the results from literature which applied Taguchi method. The comparison revealed that applying full factorial design in this case study is more efficient than Taguchi method since full factorial design considers the interaction effects of variables. Furthermore, the optimal setting of each variable was provided to have the optimum response variables. Keywords Cavitation Peening, Design of Experiment, Full Factorial Design, Taguchi Method 1. Introduction For many mechanical components, fatigue performance is an important aspect of product quality. In practice, design for reliable fatigue performance is often experience-based. Use of surface treatment processes to enhance fatigue performance also depends; to a large extend, on experimental knowledge. To seek further improvement in fatigue performance, this paper investigates the effect of surface treatment process parameters of carburized steel using full factorial design of experiment. Captivation peening serves as a representative surface treatment process whose existing input/output relationship has largely been empirical. In the literature, the robust design of surface treatment processes has been extensively investigated. Nelson and Ishii [1] applied an integrated robust design simulation and Taguchi method to investigate the effects of shot peening parameters and material properties on the predicted fatigue performance, including mean life and scatter. They considered 3 controllable and 8 noise factors in their experiment and constructed the standard Taguchi control and noise arrays. Romero et.al [2] investigated the effect of the shot peening variables on the surface changes of the 2014 aluminum alloy. They applied DOE/robust design methodology to optimize the surface treatment parameters and to find their optimum levels. Macodio and Soyama [3] investigated the effect of surface characteristics on the improvement of fatigue strength of carburized chrome-molybdenum steel subjected to surface treatment by cavitation peening (CP). Baragetti [4] applied integrated simulation-doe approach to optimize the fatigue performance by offering the best choice of treatment parameters. Mahagonakar et.al [5] used DOE technique (ANOVA) to investigate the effect on fatigue performance of shot peened AISI 1045 and 316L material. They developed expressions correlating fatigue life and the process parameters for both materials, which can be used in predicting fatigue life.

3 In this research, a few comparisons are made between the results obtained from Full Factorial Design (FFD) and Taguchi method in investigating the effects of cavitation peening parameters for fatigue performance of carburized steel. The designed experiment and initial data are adopted from Macodiyo and Soyama [3]. They used Taguchi method to investigate the residual stress, surface roughness and austenitic ratio of carburized chrome-molybdenum steel (Japanese Industrial Standard JIS SCM420) in the non-peened and peened conditions for optimal conditions for fatigue performance. The rest of the article is set up as follows. In Section 2, the experimental set-up and conditions are presented. A brief introduction of Taguchi method and Full Factorial Design of experiment are presented in Section 3. The result of applying Taguchi method and FFD method are presented in Section 4. The conclusion part includes a comparison between result obtained from two methods are presented in Section Cavitation peening experimental techniques The experimental set-up of the cavitating jet machine is presented in this section. The test section is filled with tap water. Water from the head tank is pumped into the test section by a plunger pump through a nozzle. The main parameter of the cavitating jet is the cavitation number, which is defined as the measure of the resistance of the flow to cavitation. With respect to nozzles and orifices, the flow velocity depends on the pressure difference between the upstream and downstream pressures. Hence the cavitating number σ the ratio of p 2 /p 1 ; where p 1 and p 2 are upstream and downstream pressure respectively. The standoff distance S d, is defined as the distance between the upstream corner of the nozzle throat and the surface of the specimen under test. Hence, the optimum standoff distance S opt is determined qualitatively by an erosion test in which the standoff distance is varied. The residual stress was measured at different exposure time per unit length t to determine the optimum scanning speed v opt. The exposure time per unit length is expressed as the ratio of the number of scans n to the scanning speed v (t = n/v). The surface roughness for the cylindrical specimen can be measured along the gage length using stylus-based profilometer. The residual stress and austenitic ratio can be measured using the X-ray diffraction method (XRD). The residual stress is measured by XRD method using side inclination technique. The stress was measured parallel to the longitudinal direction of the specimen, since the direction of the applied maximum bending stress during the rotating bending fatigue test was in this direction. The surface roughness changing with the processing time was measured before and after peening using the stylus-based surface profilometer. The specimen was characterized by X-ray diffraction method. The retained austenitic content was measured using XRD facility with special attachment. In order to determine the improvement of fatigue strength, the fatigue testing was performed using the rotating beam at a stress ratio R = 1. Fatigue tests were carried out at room temperature under constant sinusoidal load amplitude at 50 Hz. The experimental set-up of the cavitating jet apparatus is illustrated in Fig. 1. Figure 1. Cavitating jet apparatus [3] 3. Taguchi and Full Factorial Design methods In this section, a summary of Taguchi and full factorial design of experiment are presented as follows: 3.1. Taguchi method: Taguchi techniques introduced by Genichi Taguchi may be regarded as a variant, more engineering and less mathematic, of the Design of Experiment (DOE). Taguchi main contribution is the development of tables with a reduced combination of experiments claiming that the accuracy if the results is significantly similar to the one obtained by a whole DOE [6]. The Taguchi approach designs from a robust design perspective. A three step method for achieving robust design (Taguchi) includes Concept design, Parameter design and Tolerance design. The concept design is the process of examining competing technologies for producing a product includes choices of technology and process design. The tolerance design includes development of specification limits. It is necessary because there

4 are always some variations in the production process. This step usually occurs after the parameter design. The focus of Taguchi is on parameter design. Parameter design is the process of selection of control factors (parameters) and theirs optimal level. The main objective is to make the design robust [7]. The unique aspects of the Taguchi method include the Taguchi definition of the quality, the Taguchi quality loss function (QLF) and the concept of robust design. Taguchi defined the quality as the Ideal quality refers to a target value for determining the quality level, since the traditional definition of quality is conformance to specification. As it is shown in Fig. 2, the traditional model for quality losses is based on no losses within the specification limits (Fig. 2.a) but the Taguchi loss function shows that the quality is zero only if the process is on target (Fig. 2.b)[8]. Figure 2a. Traditional model for quality losses Figure 2b. The Taguchi loss function Figure 2. The quality loss function Taguchi s loss function can be expressed in terms of the quadratic relationship (L(y) = k(y m) 2 ), where L is loss in monetary unit (the loss associated with a particular parameter y); m represents value at which the characteristic should be set (the nominal value of the parameter specification), y is actual value of the characteristic (the critical performance parameter value), k shows constant depending on the magnitude of the characteristic and the monetary unit involved (can be determined conservatively by dividing the cost of scrap in $, by the square of the lower or higher tolerance values). This function penalizes the deviation of a parameter from the specification value that contributes to deteriorating the performance of the product, resulting in a loss to the customer. The loss function given in Eq.1 is referred to as nominal is best, but there are also expressions for cases when higher or lower values of parameters are better [9]. In parameter design, there are two types of factors that affect a product s functional characteristic: control factors and noise factors. Control factors are those factors which can easily be controlled. Noise factors are factors that are difficult or impossible or too expensive to control. There are three types of noise factors: outer noise, inner noise, and between product noise. Hence, parameter design seeks to identify settings of the control factors which make the product insensitive to variations in the noise factors, i.e., make the product more robust, without actually eliminating the causes of variation. Orthogonal Arrays (OAs), which is a DOE technique, is employed in Taguchi s approach to systematically vary and test the different levels of each of the control factors. Commonly used Orthogonal Arrays include the L4, L9, L12, L18, and L27. The columns in the OA indicate the factor and its corresponding levels, and each row in the OA constitutes an experimental run which is performed at the given factor settings. Another interesting approach from Taguchi s work is the use of the concepts of the signal/noise (S/N) ratio from electrics adapted to the factors in the experiments. Noise factors are those difficult to control so increasing the ratio S/N means that important factor influencing the process (signal) are robust enough to the noise factors. The concept of robustness in Taguchi s approach means setting the value of the factors in such a level that will make them insensible to the uncontrollable variations (noise) [10]. There are three standard types of SN ratios depending on the desired performance response: 1- Smaller the better (Eq. 1) for making the system response as small as possible; 2- Nominal the best (Eq. 2) for reducing variability around a target; and 3- Larger the better (Eq. 3) for making the system response as large as possible. These SN ratios are derived from the quadratic loss function and are expressed in a decibel scale. SN s = 10log ( 1 n n y i 2 ) (1) SN T = 10log ( y 2 i=1 S 2) (2) n SN L = 10log ( 1 n 1 2) (3) y i i=1 Once all of the SN ratios have been computed for each run of an experiment, Taguchi advocates a graphical approach to analyze the data. In the graphical approach, the SN ratios and average responses are plotted for each factor against each of its levels. The graphs are then examined to pick the factor level which best maximize SN and bring the mean on target (or maximize or minimize the mean, as the case may be).

5 3.2. Full factorial design of experiment Design of experiments makes it possible to obtain and arrange the greatest number of items of information about a process (or a mechanical component) in order to optimize it is an efficient, cheap and fast way, without requiring a deep knowledge of statistical methods. The advantages deriving from the use of DOE are product (or process) quality improvement, in terms of reliability, with a contemporary diminishing of the design and execution costs, the ability to optimize different factors at the same time (the method is mainly used to improve processes in which quality depends on a lot of variables), the possibility of taking into account the interaction among the parameters and the velocity of the test and of the analysis of residuals [11]. A design in which every setting of every factor appears with every setting of every other factor is a full factorial design. A full factorial design contains all possible combinations of a set of factors. This is the most fool proof design approach, but it is also the most costly in experimental resources. In full factorial designs, an experimental run will perform at every combination of the factor levels. The DOE technique used in full factorial design is analysis of variance (ANOVA) which used to investigate and model the relationship between a response variable and one or more independent variables. Each explanatory variable (factor factor) consists of two or more categories (levels). In addition to determining the main effects for each factor, it is critical to identify how multiple factors interact in effecting the results (interaction effects). Main effect and interaction plots are useful plots to show the main and interaction effects. Full factorial designs are the most conservative of all design types. There is little scope for ambiguity when you are willing to try all combinations of the factor settings. Unfortunately, the sample size grows exponentially in the number of factors, so full factorial designs are too expensive to run for most practical purposes [11]. 4. Cavitation peening experiment investigation In this section, the Taguchi and FFD are applied to investigate the effect of cavitation peening design parameters on the fatigue performance. The design parameters include: Standoff distance (A), Cavitation number (B), Nozzle size (C) and Exposure time (D). The responses include residual stress, Surface roughness and austenitic ratio Cavitation peening experiment using Taguchi method In this section, the results obtained from study done by Macodio and Soyama [3] is reinvestigated. They used Taguchi method to investigate the cavitation peening parameters for fatigue performance of carburized steel. As it is mentioned in Section 3.1, there are three categories of performance characteristics used for analyzing S/N ratio; the lower the better, the higher the better and the nominal the better. To obtain the maximum fatigue strength improvement while retarding crack initiation and/or development, minimum surface roughness and maximum surface residual stress and case depth are desired. Minimum austenitic ratio is desirable for higher work hardening. The first criterion selects the larger-the-better characteristics of the residual stress and depth case. Therefore, the S/N ratio can be calculated as given in Equation 3. The second and third criteria select the smaller-the-better characteristics of the surface roughness and the S/N ratio can be obtained by Equation 1. The fishbone (cause-effect) diagram (Fig. 3) presented the primary parameters, controllable and uncontrollable, that affect fatigue life and its variability using CP. UC: Uncontrollable, C: Controllable, SC: Semi-Controllable, I: Intermediate Figure 3. Fishbone for cavitation peening [3] The controllable parameters that could affect the quality of specimen are standoff distance (A), cavitation number (B), nozzle size (C) and exposure time (D). The control factors are the design parameters that need to be optimized. The objective is to select the control factor levels. Table 1 shows the factors and levels in this experiment. Macodio

6 and Soyama [3] used the L 16 (2 4 ) DOE design which is all the combination of for factors with 2 levels. Table 2 shows the orthogonal arrays for residual stress, surface roughness and austenitic ratio, respectively, and their corresponding experimental data. Table 1. Factors and Levels Symbols and factors Level 1 Level 2 A. Standoff distance (mm) B. cavitation number C. Nozzle size (mm) D. Exposure time (s) Table 2. Orthogonal array L 16 (2 4 ) for Taguchi DOE residual stress, surface roughness and austenitic ratio Exp. L 16 (2) 4 Residual stress, σ R (MPa) Surface roughness, Ra (μm) Austenitic no. A B C D 1 2 Average Average ratio An orthogonal array is a fractional factorial matrix that ensures a balanced comparison of levels of any factor. In a Taguchi design analysis, each factor can be evaluated independently of all other factors. Since there are 4 factors each with 2 levels in this experiment, there is no need to design orthogonal array and 16 runs will completely cover all the combinations of the factors. In Taguchi analysis, the p-values are used to determine which of the effects in the model are statistically significant. The p-value for each factor is identified by the software and compared to the level of significance, α-level. The commonly used α-level is If the p-value is less than or equal to α, then the effect is significant and vice versa. The main effect plots show how each factor affects the response characteristic. A main effect is present when different levels of a factor affect the characteristic differently. MINITAB 16 creates the main effects plot by plotting the characteristic average for each factor level. These averages are the same as those displayed in the response table. A line connects the points for each factor. After conducting the Taguchi analysis for each response variables in MINITAB 16, following results are obtained Taguchi Analysis: Residual stress versus A, B, C, D The results of Taguchi analysis show the significant effect of factors B and D on the residual stress (Table 3). This significant effect also can be proven by the rank of factor B and D in Table 4. Furthermore, the slight slop of factor A and C in Fig. 4 indicates that Factor A and C have less effect on the S/N ratio. The opposite effect is experienced with factor B and D. Table 3. Analysis of Variance for SN ratios of residual stress Table 4. Response Table for S/N ratio of residual stress (Larger is better)

7 Figure 4. Main effects plots for S/N ratio of residual stress Taguchi Analysis: Surface Roughness versus A, B, C, D The obtained results show the significant effect of Factor B and D on surface roughness. The lowest p-value in Table 5 indicates that factor B has the highest impact on surface roughness. This significant effect can also be proven by the rank of factor B in the response for S/N ratios of surface roughness (Table 6). Factors A, B and C have positive gradients while factor D has negative gradient (Fig. 5) Taguchi Analysis: Austenitic ratio versus A, B, C, D The analysis of variance of S/N ratios for austenitic ratio (Table 7) indicates the significant effect of factor C in the austenitic ratio. Moreover, the response for S/N ratios of austenitic ratio shows the effect of factor C as first rank. Table 5. Analysis of Variance for SN ratios Table 6. Response Table for S/N Ratios of surface roughness (Smaller is better) Table 7. Analysis of Variance for SN ratios Table 8. Response Table for S/N Ratios of austenitic ratio (Smaller is better) Figure 5. Main effects plots for S/N ratio of Surface Roughness Figure 6. Main effects plots for S/N ratio of austenitic ratio

8 4.2. Cavitation peening experiment using full factorial design of experiment In this section, the full factorial design of experiment technique is utilized to investigate the effect of discussed factors (A, B, C, D) on the fatigue performances include residual stress, surface roughness and austenitic ratio which are considered as response variables. The results of conducting full factorial design for each response variable are presented as follows Analyzing residual stress with FFD Based on the observation data presented in Table 2, there are 2 replicates for residual stress. From Tables 9 and 10, and Fig. 8, it can be concluded that all main factors and their interactions have significant effect on the residual stress. From Fig. 8, which presents the interaction plots for residual stress, it can be concluded that setting parameters A, B and C on level 1 and parameter D on level 2 will result higher amount of residual stress. The assumptions of FFD are checked in Fig. 9, which represent no violation. Table 9. Estimated effects and coefficients for residual stress (coded unit) Table 10. ANOVA for residual stress (coded units) Figure 7. Half normal plot of the standardized residual for residual stress Figure 8. Interaction plot for residual stress Figure 9. Residual plot for residual stress

9 Analyzing surface roughness with FFD Based on the observation data presented in Table 2, there are 3 replicates for surface roughness. The p-values presented in Tables 11 and 12 and also half normal plot for surface roughness presented in Fig. 10 show that all factors have significant effect on the surface roughness. Fig. 11 shows that level 2 would be the optimal setting for factors A, B and C and level 1 is optimal setting for factor D. The residual checking in Fig. 12 shows the model adequacy. Table 11. Estimated effect and coefficient for surface roughness (coded units) Table 12. ANOVA for surface roughness (coded units) Figure 10. Half Normal Plot of the Standardized Residual for surface roughness Figure 12. Residual Plot for surface roughness

10 Analyzing austenitic ratio with FFD Based on the observation presented in Table 2, there is only 1 replicate for austenitic ratio. After conducting the FFD analysis, factors B, C, D and the interactions B*D and B*C*D found to be significant (Alias structure) (Table 13, 14 and Fig. 13). From Fig. 14, it can be concluded that level 1 is optimal setting for factor C. Fig. 15 represents the interaction plot for factor B and D. It can also be concluded that level 2 has better response for factor B and level 1 would have better result for factor D. The dispersion of factor A in Fig. 16 indicates that level 1 would have better result for factor A. Table 13. Estimated Effects and Coefficients for Austenitic Ratio (coded units) Table 14. Analysis of Variance for Austenitic Ratio (coded units) Figure 13. Half normal plot of the standardized effects for Austenitic Ratio Figure 14. Main Effects Plot for Austenitic Ratio Figure 15. Interaction Plot for Austenitic Ratio Figure 16. Residuals from Austenitic Ratio versus factor A 5. Discussions and conclusions Two DOE based techniques were employed to identify the critical factors and their effects on the fatigue strength of chrome-molybdenum steel. The design parameters considered in this investigation included: Standoff distance (A), cavitation number (B), Nozzle size (C) and Exposure time (D). Each factor had 2 levels and the response variables included: Residual stress, Surface roughness and Austenitic ratio. At the first step, the Taguchi method was applied to investigate the design parameters. The S/N ratio was used to analyze experimental results involving multiple runs. Analyzing using S/N ratio allows the selection of the optimum level based on least variation around the mean. The second approach to investigate this experiment was Full Factorial Design (FFD) of experiment. Since the table of observations obtained from literature has all the combination of factors, we found the possibility of conducting full

11 factorial design. The p-values obtained from analysis of variance, half normal plot, and main and interaction plots were used in FFD to make the conclusions. Table 15 summarizes the results obtained from both methods. Response variables Residual Stress Surface roughness Austenitic ratio Table 15: summary of results obtained from Taguchi and FFD methods Analyses methods Optimal level A B C D Significant factors Taguchi B, D FFD A, B, C, D, all interactions Taguchi B, D FFD A, B, C, D, all interactions Taguchi C FFD C, B*D, B*C*D The results obtained from Taguchi and FFD methods are slightly different. Since the Taguchi method does not consider the interaction effects, these different results are rationale. Furthermore, Taguchi method is useful in situations that there are some noise factors in the experiment, but there is no noise factor in this case study. The other criticism on the study done by Macodio and Soyama [3] is that although they used Taguchi method for their investigation, the orthogonal array was not well utilized in their study. They obviously used all factor combinations (16 run) and considered OA as L 16 (2 4 ) which can be used to reduce a design with 15 factors each with 2 levels. Finally, the comparison between two methods shows the better investigation of cavitation peening design parameters on fatigue performance of carburized steel. References 1. Nevarez, I., Nelson, D., Esterman, M., & Ishii, K., 1996, Shot Peening and Robust Design for Fatigue Performance. In 1996 Conference Proceedings of the Sixth International Conference on Shot Peening (pp ). 2. Romero, J. S., Rios, E. D. L., Fam, Y. H., & Levers, A., 2002, Optimization of the Shot Peening Process in Terms of Fatigue Resistance, (Doctoral dissertation, University of Sheffield). 3. Macodiyo, D. O., & Soyama, H., 2006, Optimization of Cavitation Peening Parameters for Fatigue Performance of Carburized Steel Using Taguchi Methods. Journal of Materials Processing Technology, 178(1), Baragetti, S., 1997, Shot Peening Optimization by Means of' DOE: Numerical Simulation and Choice of Treatment Parameters. International Journal of Materials and Product Technology, 12(2), Mahagaonkar, S. B., Brahmankar, P. K., & Seemikeri, C. Y., 2009, Effect on Fatigue Performance of Shot Peened Components: An Analysis Using DOE Technique. International Journal of Fatigue, 31(4), Taguchi, G., & Cariapa, V., 1993, Taguchi on Robust Technology Development. American Society of Mechanical Engineers. 7. Tsui, K. L., 1992, An Overview of Taguchi Method and Newly Developed Statistical Methods for Robust Design. Iie Transactions, 24(5), Taguchi, G., 1986, Introduction to Quality Engineering: Designing Quality into Products and Processes. 9. Phadke, M. S., 1995, Quality Engineering Using Robust Design. Prentice Hall PTR. 10. Taguchi, G., 1985, Quality Engineering in Japan. Communications in Statistics-Theory and Methods, 14(11), Montgomery, D. C., 2008, Design and Analysis of Experiments. John Wiley & Sons.