THE EFFECT OF SURFACE INDICATIONS ON THE TENSILE PROPERTIES OF CAST STEEL JEFF HAMBY JOHN A. GRIFFIN, COMMITTEE CHAIR ROBIN D. FOLEY CHARLES A.

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1 THE EFFECT OF SURFACE INDICATIONS ON THE TENSILE PROPERTIES OF CAST STEEL by JEFF HAMBY JOHN A. GRIFFIN, COMMITTEE CHAIR ROBIN D. FOLEY CHARLES A. MONROE A THESIS Submitted to the graduate faculty of The University of Alabama at Birmingham, in partial fulfillment of the requirements for the degree of Master of Science in Materials Engineering BIRMINGHAM, ALABAMA 2013

2 THE EFFECT OF SURFACE INDICATIONS ON THE TENSILE PROPERTIES OF CAST STEEL JEFF HAMBY MATERIALS ENGINEERING ABSTRACT The objective of this thesis was to study the effect of surface indications on the tensile properties of cast steel. Four cast steel grades were selected for evaluation; these grades include three carbon and low alloy steels (110/80, 165/135, and Eglin) and one high alloy steel (CF8M). Using these steels, tensile specimens were produced, inspected via MT/PT, categorized by surface indications (as-cast or machined), and tested. Bars with natural surface indications were tensile tested and the properties recorded. The presence of a 1 / 16 inch, 1 / 8 inch, or 1 / 4 inch flat-bottomed hole drilled through half the thickness mimicked a similar nonlinear worse-case scenario indication. The 1 / 4 inch indication resulted in an ultimate tensile strength loss ranging from 21.5% to 36.0%, with the more ductile materials being impacted least. The percent elongation loss ranged from 38.5% to 69.9%, with the majority of the alloys showing an approximate 60 percent loss in elongation. The modulus decrease ranged from 2.9% to 17.5%. These results were modeled using ANSYS to observe capability in predicting a decrease in properties. The resulting decrease in properties matched the experimental data to an accuracy of 3±11%. The results provide a previously undocumented relationship between indication size and tensile properties. Keywords: Tensile, Steel, Indications, Surface, Castings, Model ii

3 TABLE OF CONTENTS Page ABSTRACT... ii LIST OF TABLES... iv LIST OF FIGURES... v LIST OF ABBREVIATIONS... vi BACKGROUND... 1 RESEARCH METHOD... 6 RESULTS Natural and Machined Surface Indication Lengths at Fracture % offset YS and UTS Elongation Young s Modulus Percent Indication Area on Fracture Surface % offset YS and UTS Elongation Young s Modulus Modeled Surface Indications % offset YS and UTS Elongation Young s Modulus Conclusions LIST OF REFERENCES APPENDIX: A TENSILE DATA B STEEL CHEMISTRIES AND STRESS-STRAIN CURVES iii

4 Table LIST OF TABLES Page 1 Inputs used for ANSYS model Percent decrease of average 0.2% offset YS and UTS vs. indication lengths compared to sound material Percent decrease of average % elongation vs. indication lengths Percent decrease in Young s modulus vs. indication lengths Strength comparison between experimental and model % elongation comparison between experimental and model Young s modulus comparison between experimental and model iv

5 Figure LIST OF FIGURES Page 1 Example of tensile bar and plate MTS machine IGS models and element meshes % offset YS and UTS vs. indication length at fracture % elongation vs. indication length measured at fracture Young's modulus vs. indication length measured at fracture % offset YS and UTS vs. fracture surface area of indication % elongation vs. fracture surface area of indication Young's modulus vs. fracture surface area of indication Stress-strain curves of experimental data and model data of (A) (A) and (B) model outputs CF8M (C) and (D) model outputs Eglin (E) model outputs v

6 LIST OF ABBREVIATIONS in. kip ksi MT MTS psi PT UTS YS inch or inches kilopounds kilopounds per square inch magnetic particle testing material test system pounds per square inch liquid penetrant testing ultimate tensile strength yield strength vi

7 1 BACKGROUND Every global industry strives to improve its processes and thus improve its product. This statement is especially true in today s quality-driven market. As a competitor in the global market, the cast steel industry has continuously improved its process and products to manufacture higher quality parts while minimizing costs and production time. Some of this improvement can be accredited to the many standards that have been written to help designers define acceptable product limits to produce required performance. However, some of these standards are workmanship standards and are not directly related to part performance. An example of a workmanship standard for steel castings is the radiographic standard ASTM E-186 [1]. This standard consists of reference radiographs that show examples of discontinuities categorized into severity levels, which allows considerable flexibility for the producer and buyer on how to interpret the radiographic grade of a part. This flexibility is necessary since stricter requirements would demand more information on the service environment of the part. Steel castings are used in an almost infinite variety of service conditions. In essence, the radiographic standards are a yardstick, and it is up to the producer and buyer on how to use the yardstick. Other standards such as ASTM A-903 provide quantitative values but were developed from other manufacturing processes and may be overly conservative [2]. This

8 2 standard specifies levels of acceptance criteria on the surface of castings using measured lengths and geometries of indications. As the cast steel industry has grown more sophisticated in the use of numerical modeling to predict process quality and part performance, design requirements should be reexamined to see if they actually affect part performance. In an attempt to link designer standards to the performance of materials, this study focused on characterizing the effect of surface/near surface indications on tensile properties using the backdrop of current acceptance standards such as ASTM A- 903 [2]. Most of the steels used in this study, ASTM A-958 Grade , ASTM A-958 Grade , ASTM A-351 Grade CF8M, are widely produced and normally used for valves, flanges, fittings, and other pressure containing parts. The only uncommon steel used was Eglin steel, which is a low cost replacement for super alloy steels such as HY- 180 and finds much of its use in military applications. The , , and Eglin steel are all low carbon and low alloy steels, the only exception being CF8M. In general, CF8M contains a high percentage of Cr and Ni and is essentially the cast equivalent of 304 type wrought alloys. CF8 may be fully austenitic, but it more commonly contains some residual ferrite (3-30%) in an austenitic matrix. CF8M is a version of CF8 alloy with an addition of 2-3% molybdenum, which increases resistance to corrosion by seawater and improves resistance. These molybdenum-bearing alloys are generally the superior choice for weakly oxidizing environments [3] (p ). In order to meet demands of buyers, the steel casting industry has used several recordable destructive tests to qualify the material properties. The two most prevalent tests are a cyclical loaded tensile test, also known as fatigue, and a monotonic loaded

9 3 tensile test. The monotonic tensile test is performed by applying an increasing load until failure, whereas the cyclical test applies an oscillating tensile load until failure. These two methods both result in quantifiable material properties. The monotonic tensile test was chosen for this study because of this industrial prevalence. To date, there has not been a study to determine the quantitative effect of surface indications on the monotonic tensile properties of steel castings. There has, however, been studies of the effect of internal indications on mechanical properties. The majority of these studies related fatigue performance to internal radiographic indications. A few studies related internal shrinkage, macro-porosity, and micro-porosity to tensile mechanical properties. In general, reasonable concentrations of internal shrinkage had little effect on 0.2% offset yield strength or YS, ultimate tensile strength or UTS, and elastic modulus, but produced a significantly reduced percent elongation when monotonically tested [5]. It was also observed that monotonically tested specimens with micro-porosity repeat the trend of having little effect on strength but did affect on ductility [6]. However, cyclically loaded fatigue specimens with macro-porosity showed elastic modulus varying as a function of porosity volume [6]. Hardin and Beckermann found that the elastic modulus decreases nonlinearly with porosity when cyclically loaded, and this relationship is dependent on the characteristics of the porosity [7]. These studies reveal that indications can potentially affect all mechanical properties, having the greatest effect on elongation. In order for this study to benefit from these past surface indication studies, a relationship between fatigue and monotonic tensile test must be formed. A comparison of the fatigue and monotonic tensile test is seen in Svoboda s study of fatigue and

10 4 fracture toughness of five different steels. The study revealed that the YS was lower in fatigue tests than in monotonic tests; however, the UTS was higher in fatigue versus monotonic in four of the five steels [4]. These results reveal that fatigue and monotonic tests are not directly relatable, but they do reveal which material properties will be affected most by surface indications. Thus, only general trends can be carried between surface indication studies using fatigue and studies using monotonic tensile tests. In order to quantitatively define the effect of surface indications on mechanical properties, the term surface indication must first be defined. Surface indication has historically been used to describe any visible inconsistency observed on the casting surface. An example of the current nomenclature, ASTM A903 conveys general acceptance guidelines, but does not reveal a quantitative relationship between the size of the indication and the mechanical properties[2]. With quantitative data, a more defined relationship between surface indications and properties can be developed. This relationship will give designers the ability to properly size a part and produce acceptable performance with a reasonable safety factor. Due to the random nature of surface indications, development of a machinable indication that mimics the effect of naturally occurring indications would be useful for experimental and numerical simulation testing. This technique has been used before by Rudy and Rupert in their study of the mechanical properties of aluminum and its relationship to porosity [8]. This study determined that fine porosity can be as detrimental to a weld as large porosity if the total area of the micro-pores were comparable to the single large pore. Thus, the machined indication replicated a natural indication. These results lead to a second goal of this study, which is to improve testing

11 5 repeatability in steel castings by using machined notches to mimic naturally occurring indications. The standard means of detecting a surface indication is by visual inspection. In order to improve this inspection, techniques such as magnetic particle testing also known as MT or liquid penetrant testing also known as PT have been developed, which aid the eye in the detection of hard-to-see indications on as cast surfaces. These tools greatly enhance detection, but classification and indication effect on properties are left up to operator interpretation. This study only contains linear and non-linear indication, not cracks from quenching or hot tears. Previous work has shown that linear and non-linear indications typically extend less than 13 mm beneath the surface while cracks developed from quenching or hot tears can run much deeper. By virtue of studying commonly used steels, the noticed effects of the surface indications will be able to directly contribute to real world safety applications. The less common Eglin steel was selected due to its extremely high tensile properties, thus broadening the data range for the study. A long-term use of this study will be the improvement of the quantification of surface indication effects on other mechanical properties, such as bending fatigue.

12 6 RESEARCH METHOD The four cast steels used included three carbon and low alloy steels and one high alloy steel. These steels provided a range of YS from 40 kilopounds per square inch or ksi up to 160 ksi. The carbon and low alloy steels include a 110/80 (minimum YS 80 ksi, minimum UTS 110 ksi), a 165/135 (minimum YS 135 ksi, minimum UTS 165 ksi), and Eglin steel. A high alloy CF8M cast steel was also included to provide different microstructure and modulus but with tensile properties similar to a 70/40 steel. Plates were cast from these steels yielding approximately 30 potential test bars for each alloy, with exception of the Eglin steel. The only available supply of Eglin steel was in machined billets with no as cast surface and hence no surface indications. In this case, tensile specimens were removed from the billets and artificial indications were machined into the gauge section. The other cast plates had approximately inches or in. removed from the cope to remove the as cast surface roughness. Most of the plates were machined to yield in. wide standard flat tensile bars [9]; however, the Eglin steel was machined with a thickness of in. as opposed to in. This reduced thickness was required for the Eglin steel in order to lower maximum load of the test bars to within 50 kilopounds or kips, the maximum load rating of the frame. These test bars were machined from the cope of a cast plate to capture any potential surface indications to the desired shape shown in Figure 1.

13 7 Figure 1: Example of tensile bar and plate Once machined, the carbon and low alloy steel specimens were MT inspected [10] to detect any surface/sub-surface indications present. All specimens were tested with PT [11] to distinguish surface and sub-surface indications, and reveal any indications running perpendicular to the gauge length. Of course, the CF8M specimens were only tested with PT. Indications found within the 2.25 in. reduced gauge section were photographed and measured using Image Pro Plus. According to ASTM A903, an indication is considered relevant if it is equal to or greater than 1 / 16 in. ASTM A903 surface inspection criteria also only considers this 1 / 16 in. relevant if the length of the indication is greater than 3 times its width i.e. linear [2]. For the purposes of this study, all indications detected via MT and PT will be considered relevant. Since the loading direction was known, indication length was measured as the length perpendicular to the loading direction, which will produce inherently conservative results.

14 8 Many tensile bars had no indications present. Many of these bars were used to provide baseline of properties for this study. However, some of these bars were notched to simulate a naturally occurring nonlinear surface indication. These notches were machined using different drill bit diameters ( 1 / 16 in., 1 / 8 in., and 1 / 4 in.) leaving a flatbottom circular (nonlinear) indication in the bar. Therefore, the created indication falls into the nonlinear class. The depth of drilling was limited to half the thickness of the tensile bar, which results in the surface class of indication as defined by Fatigue design of welded joints and components [12] (p.89). This simulated surface indication was meant to mimic worst case scenario nonlinear indications. These bars were tested according to ASTM E8 & A370 using an 810 material test system or MTS 50 kip frame with hydraulic grips machine seen in Figure 2. Figure 2: 810 MTS machine The tensile test was displacement controlled, while recording the applied force. Stress was determined using the resulting force over the determined cross-sectional area. The strain was recorded using a 2 in. clip-on extensometer utilizing MTS Flex Test software. The YS was obtained by plotting a stress-strain curve and recording the point at which

15 9 the curve becomes nonlinear. The modulus was obtained by performing a linear regression model on the elastic portion of the stress-strain curve and recording the slope. The 0.2% YS was determined by matching the slope with a parallel line that is transposed on the in/in strain reading and recording the intersection of this line and the stressstrain curve. After tensile testing, the bars were studied to see if fracture occurred at an indication. The fracture surfaces were then photographed and the defect s surface area, if present, was measured using Image Pro Plus. The natural and machined indication properties were then compared to the baseline properties to see if a quantitative effect of the measured indications is observable. The tensile properties studied were 0.2% YS, UTS, elongation, and Young s Modulus. Upon completion of testing, the tensile bars without any indications as well as the 0.25 in. machined indications were modeled within ANSYS 14.5 to see if the model predicted a similar property behavior. The model used a 10node187 tetrahedral mesh for an inelastic rate-independent isotropic-hardening bilinear material. The mesh density used was determined to be independent, as a finer mesh yielded an average of less than 1% change in outputs. The mesh density used allowed the model to run quickly without reducing accuracy. These options yielded the mesh seen in Figure 3.

16 10 Figure 3: IGS models and element meshes The large displacement static solution control was selected over the small displacement static solution, because it accounts for more modeling scenarios. The model ran using 200 substeps to produce sufficient data points for graphing accuracy. The model used load displacement control, similar to tensile testing, and required the inputs found in Table 1. Tensile Bar Group Young s Modulus Table 1: Inputs used for ANSYS model Poisson s Ratio YS (psi) Tangent Modulus Displacement Used (in) (A) (A 1 / 4 ) (B) (B 1 / 4 ) CF8M (C) CF8M (C 1 / 4 ) (D) (D 1 / 4 ) ES-1 (E) ES-1 (E 1 / 4 )

17 11 The displacement input was obtained by using the average displacement at failure of the experimental bars being measured. In other words, the average in. machined indication bars measured displacement was used for the in. machined indication model. The YS value was obtained by averaging the measured 0.2% offset YS produced by the bars without any detectable indications. Each alloy was categorized into a group A, B, C, D, or E. The models containing the 0.25 in. flat-bottom hole are indicated by the 1 / 4 following the alloy letter.

18 12 RESULTS Natural and Machined Surface Indications Lengths at Fracture 0.2% offset YS and UTS Naturally occurring indications were present in 3 of the 4 cast steels. Among these 3 cast steels, some test bars had more than one indication present. The Eglin steel test bars did not have any natural surface indications, only machine indications. Figure 4 shows the effects of indication length that instigated fracture on 0.2% YS and UTS. In most cases, fracture occurred at the largest measure indication. It should be noted that all length measurements were taken perpendicular to the load direction. These figures show that each alloy is affected differently by the indication lengths present, confirming an initial assumption that different alloys behave differently. A few data points in the upper right corner of the graph seemingly do not follow the same trend as the rest of the data in Figure 4, but these graphs only represent the indication length measured at the location of fracture. Figure 4 does not account for the width or depth of the indication. Common sense would suggest that the longest indication on the bar would be the initiation of fracture. During this study the majority of fractures initiated at the longest measured indication. However, exceptions to this trend occurred in bars with small indications, less than 1 / 16 in.; bars with two or more indications of similar lengths, 0.02 in. difference; or in the more ductile materials CF8M, C and , D.

19 13 A second notable observation in Figure 4 is how the machined indications trend alongside the naturally occurring surface indications. In all cases, each machined indication represented a worst-case scenario for each indication length group. These results imply that the easily modeled flat-bottomed hole was a valid representation of a natural indication. A percentage decrease of 0.2% offset YS and the UTS as a function of indication length is listed in Table 2. Table 2: Percent decrease of average 0.2% offset YS and UTS vs. indication lengths compared to sound material 0.2 % offset YS Group " " " " " " " " > 0.25 Eglin, E - 5% 12% - 31% , A 1% 1% 12% 9% 17% , B 4% 1% 2% - 24% , D 4% 3% 6% 0% 21% CF8M, C 0% 3% 5% 4% 14% UTS Group " " " " " " " " > 0.25 Eglin, E - 5% 22% - 36% , A 2% 3% 17% 15% 21% , B 4% 4% 11% - 34% , D 3% 3% 7% 1% 21% CF8M, C 5% 6% 10% 0% 25% Table 2 and Figure 4 shows that for these steel strength levels states, all 0.2% offset YS and UTS are unaffected until indications lengths in. or greater are present. It should be noted that due to the variation in the baseline properties seen in Figure 4, any effect less than 10% should be deemed statistically insignificant. Table 2 also suggests that the effect of the indication on 0.2% offset YS and UTS is dependent on the ductility

20 14 of the material. This correlation is shown by the CF8M, C s and the , D s resistance to the indications effects until the 0.25 in. size is reached. In summary, any indication less than 1 / 16 in. did not statistically impact the 0.2% offset YS or UTS of any alloy. The machined indication test bars matched similar worstcase scenarios found in the natural indication test bars. The more ductile alloys were less affected by the presence of surface indications, revealing a relationship to strength. Thus, the effect of the indication increased as the strength of the alloy increased.

21 Figure 4: 0.2% offset YS and UTS vs. indication length at fracture 15

22 16 Elongation in Table 3. Elongation was significantly more affected by surface indication length as shown Table 3: Percent decrease of average % elongation vs. indication lengths Group " " " " " " " " > 0.25" Eglin, E - 17% 33% - 38% , A 38% 46% 65% 64% 58% , B 13% 44% 70% - 78% , D 0% 29% 49% 47% 63% CF8M, C 17% 20% 34% 16% 52% Almost all elongations were affected by the presence of any indication. The only exception to this observed trend was seen in the ductile As previously seen, the decrease in elongation is a function of the strength of the material. In other words, the more ductile materials were more resistant to indications. The Eglin steel was an exception to this trend, but this difference is due to the scatter seen in the baseline properties. Similar to the strength, the test bars with machined indications generated data that conservatively matched similarly sized natural indications. Figure 5 shows the effect of indication length on the elongation.

23 Figure 5: % elongation vs. indication length measured at fracture 17

24 18 Young s Modulus The Young s modulus was less sensitive to indication size, compared to other material properties. The modulus was obtained by determining the slope of the linear portion of the stress-strain curve using a linear regression model. Table 4 lists the decrease in modulus seen by each alloy. Table 4: Percent decrease in Young's modulus vs. indication lengths Group " " " " " " " " > 0.25" Eglin, E - 5% 0% - 11% , A 8% 1% 1% 1% 7% , B 0% 0% 0% - 7% , D 0% 2% 6% 0% 16% CF8M, C 0% 8% 12% 0% 17% Interestingly, Table 4 reveals that all observed moduli were unaffected until the indication lengths reached 0.25 in. or greater. Figure 6 shows the graphs of the data collected. The machined indications again trended alongside the natural indications. Even the observed decreases in the moduli overlapped some of the baseline moduli seen in Figure 6. Previous studies of elastic modulus showed indications had a greater influence than the observed results [7]. This disconnect is most likely due to differences between the compromised length and the total length of the extensometers. The previous study used a 12 millimeter extensometer, and this study used a 2 in. or 50.8 millimeter extensometer. Therefore, 100% of the extensometer length was compromised in the previous experiments; and this study only had approximately 10% of the extensometer length compromised at most. Thus, the observed data does not show as localized a strain

25 19 increase as seen previously. These greater values of strain would lead to greater reductions in modulus. A second difference in the studies was the method of testing. The previous study used fatigue, whereas this study used monotonic tensile testing. The cyclical loading of fatigue can cause materials to strain-soften, thus lowering the modulus values [6].

26 Figure 6: Young's modulus vs. indication length measured at fracture 20

27 21 Percent Indication Area on Fracture Surface 0.2% offset YS and UTS The tensile bar fracture surfaces were examined to determine the total area of indications present, both surface and internal indications previously undetected. The relationship between the percentage of indication area, the 0.2% YS, and the UTS is illustrated in Figure 7. The effect of indication(s) area on the fracture surface had a greater degree of variation than the surface indication length measurements. The location differences of the indications contributed to this observed variation. For example, an indication that is present on the bar surface and penetrates through the entire cross section can be more impactful on mechanical properties than an indication that covers a larger portion of the fracture surface but is not present on the machined surface. Figure 7 shows the relationship between material strength and the fracture surface area of indications. Although the indication fracture surface area had a greater degree of scatter, the strengths followed the same trend as the indication length at fracture. All alloys remain unaffected until a 12.5% area is covered, with the decrease in properties following a function of the materials ductility. The more ductile the material, the less the properties are affected. The machined indications again offer a conservative prediction of loss in properties.

28 Figure 7: 0.2% offset YS and UTS vs. fracture surface area of indication 22

29 23 Elongation Following the same trend as the indication length at fracture, the greatest decrease is seen in the elongation of the materials. Practically all alloy elongations were affected by the presence of any form of indications. The more ductile materials, however, showed a greater resistance to the percent fracture area of indications. Although the elongation is affected by the presence of even minor fracture surface areas of indications, the majority of the effect occurs rapidly. In other words, the presence of indications greatly reduces the elongation, but additional indications or increases in the fracture surface area do not enhance this effect. This result is especially evident in the machined indications. The 1 / 4 in. indication is not much worse than the 1 / 16 in. indication. Figure 8 reveals the effect the percent fracture area covered by indications has on the elongation of the alloys studied. Young s Modulus Similar to strength and elongation, the modulus trended in the same manner as the indication length measured at fracture. As expected, a great deal of scatter was again observed in the modulus data. This scatter muddles the effect that fracture surface area of indications has on the elastic modulus. It seems, however; that some degradation does possibly occur at greater observed instances of defect fracture surface area. It seems that the moduli behaved in a similar fashion for all alloys except for 165/135. Again, the Young s modulus was influenced the least by the presence of indications. Figure 9 shows the observed Young s modulus versus the indication surface area at fracture.

30 Figure 8: % elongation vs. fracture surface area of indication 24

31 Figure 9: Young's modulus vs. fracture surface area of indication 25

32 26 Modeled Surface Indications 0.2% offset YS and UTS In order to utilize models to analyze the phenomenon caused by natural surface indications, tensile bars were machined halfway through to create flat bottom holes. The flat-bottom hole was chosen because it had been used previously [8], and it is easily modeled. Models were constructed for all alloys studied, both with a 0.25 in. diameter hole in the center of the gauge section and without. These models used experimental data from the tensile bars without any recognizable indications to see if ANSYS could predict the detrimental effect that a 0.25 in. flat bottom hole had on the tensile properties. The model was calibrated by ensuring that the outputs matched the experimental data of the bars without indications. Similar to the actual experiment, the load on the model was controlled by displacement and stopped only when this displacement value was reached. These displacement values were determined by verifying that the model elongations matched the experimental elongations. When input correctly, the model creates data suitable for a stress-strain curve and generates images comparable to the actual tensile test. The inputs used for this model were listed previously in Table 1. An example of two strain-strain curves generated by the model can be seen in Figure 10, where A6 is a sound test bar, A16 is a bar with a 0.25 in. flat-bottom hole, A Full is a model of sound material, and A 1/4 is a model with the flat-bottom hole.

33 27 Figure 10: Stress-strain curves of experimental data and model data of (A) The UTS was determined from the model by averaging the y-stresses at the 4 corner nodes on the surface that was displaced. These averages were then multiplied by 1.5 to account for the change in cross-sectional area of the observed face and the gauge section. The finite element analysis outputs can be seen below in Figures

34 Figure 11: (A) and (B) model outputs 28

35 Figure 12: CF8M (C) and (D) model outputs 29

36 Figure 13: Eglin (E) model outputs The resulting data from the models can be seen in Table 5, which shows a comparison of the actual versus predicted 0.2% YS and UTS for the tensile bars.

37 % offset YS Table 5: Strength comparison between experimental and model Tensile Bar Group Actual (avg.) psi Predicted (avg.) psi % Difference from Actual (A) 176, ,500 +4% (A ¼) 127, , % % Decrease A to A ¼ 27.8% 13.6% (B) 108, ,000 +6% (B ¼) 85, , % % Decrease B to B ¼ 21.3% 8.9% --- CF8M (C) 34,449 37,000 +7% CF8M (C ¼) 29,664 34, % % Decrease C to C ¼ 13.9% 7.4% (D) 96,742 99,500 +3% (D ¼) 76,454 87, % % Decrease D to D ¼ 21.0% 12.3% --- Eglin (E) 162, ,000-1% Eglin (E ¼) 112, , % % Decrease E to E ¼ 30.5% 5.7% --- UTS (A) 191, ,804 +1% (A ¼) 131, , % % Decrease A to A ¼ 31.0% 18.6% (B) 135, ,913 +3% (B ¼) 99, , % % Decrease B to B ¼ 26.6% 19.6% --- CF8M (C) 77,876 73,032-6% CF8M (C ¼) 58,728 52,862-10% % Decrease C to C ¼ 24.6% 27.6% (D) 114, ,712 +2% (D ¼) 90,133 99, % % Decrease D to D ¼ 21.5% 15.1% --- Eglin (E) 208, ,819-3% Eglin (E ¼) 133, , % % Decrease E to E ¼ 36.0% 13.2% --- The predicted tensile properties of the bars without any defects correlate well with the experimental data. These expected results verify that the correct inputs were chosen in order to replicate the sound tensile bars. The model predicts a decrease in strength

38 32 caused by the 0.25 in. flat-bottom hole; however, it is less accurate for the 0.2% offset YS. The generated percent decrease in 0.2% offset YS is on average 13 7%, and percent decrease in UTS is on average 8 10%. The less ductile the materials, the less accurate the model becomes. For 3 of the 5 alloys, however, the predicted percentage decrease is less than 7% off from the actual observed decrease. These results reveal that this model is more adequate for ductile materials in terms of predicting losses in strength, and that the model is more effective in predicting UTS than 0.2% offset YS. Elongation The elongation was measured within the model by following the change in displacement of 2 nodes within the gauge section that were approximately 2 in. apart. The resulting data can be seen in Table 6. Table 6: % elongation comparison between experimental and model Actual Elongation % Difference from Actual Tensile Bar Group Predicted Elongation A: % 6.7% +3% A ¼: % 2.1% -5% % Decrease A to A ¼ 65.4% 68.2% --- B: % 11.7% +1% B ¼: % 3.7% +5% % Decrease B to B ¼ 69.6% 83.7% --- C: CF8M 46.4% 47.4% +2% C ¼: CF8M 22.0% 22.8% +4% % Decrease C to C ¼ 52.6% 51.8% --- D: % 20.0% -4% D ¼: % 7.6% -1% % Decrease D to D ¼ 63.2% 61.9% --- E: Eglin 5.2% 5.1% -2% E ¼: Eglin 3.2% 3.2% -1% % Decrease E to E ¼ 38.5% 37.9% ---

39 33 Because the model used runs until the displacement is reached, the elongation directly related to the model inputs. Due to this relationship, the model follows the actual data closely. Young s Modulus The modulus was again obtained by taking the linear portion of the model generated stress-strain curve and determining the slope. This obtained model data was then compared to the experimental and is seen in Table 7. Table 7: Young s modulus comparison between experimental and model Tensile Bar Group Actual E (avg.) Predicted E (avg.) % Difference from Actual A: ,773,781 29,792,947 +0% A ¼: ,911,217 26,064,508-10% % Decrease A to A ¼ 2.9% 12.5% --- B: ,184,387 28,202,236 +0% B ¼: ,145,988 24,646,431-2% % Decrease B to B ¼ 10.8% 12.6% --- C: CF8M 25,807,251 20,536,743-20% C ¼: CF8M 21,279,905 22,520,720 +6% % Decrease C to C ¼ 17.5% -9.7% --- D: ,346,324 31,353,852 0% D ¼: ,391,115 27,336,453 +4% % Decrease D to D ¼ 15.8% 12.8% --- E: Eglin 26,864,791 26,967,907 0% E ¼: Eglin 23,864,716 23,841,044 0% % Decrease E to E ¼ 11.2% 11.6% --- Table 7 reveals that the predicted modulus of each alloy was affected by about the same amount of decrease. For most instances, the generated modulus matched the experimental data. The predicted moduli also reiterated that modulus is affected least by the indication in comparison to the other observed tensile properties. In all cases except

40 34 for the CF8M, the predicted percent decrease in modulus caused by the 0.25 in. flatbottom hole was off by less than 10%. This CF8M discrepancy is most likely due to the model s first generated data point being after the linear portion of the CF8M stress-strain curve. These results prove that ANSYS models can be used with relative accuracy in predicting the decrease in tensile properties seen by a 0.25 in. flat-bottom hole drilled through half the thickness. Thus the modeled 0.25 in. tensile bars proved useful in predicting the relationship between the defect and its properties. Conclusions In conclusion, alloy strengths were unaffected until the indication length reaches 1 / 8 in. All alloy elongations were greatly affected by the presence of practically any indication, thus revealing that elongation is the governing design factor. Also, the elastic moduli of the observed alloys were unaffected until indication lengths of the 1 / 4 in. or greater. In all observed instances, the more ductile the alloy, the less the impact of an indication. Also, the machined indications generated the most conservative properties in the experiment. ANSYS software was able to predict the percent decrease in properties from sound material to the machined 0.25 in. hole to an average accuracy of 3 11%.

41 35 References: [1] ASTM E Standard Reference Radiographs for Heavy-Walled Steel Castings [2] ASTM A903/A903M. Standard Specification for Steel Castings, Surface Acceptance Standards, Magnetic Particle and Liquid Penetrant Inspection [3] Steel Castings Handbook. Supplement 2: Summary of Standard Specifications for Steel Castings. Steel Founders Society of America [4] Svoboda, John M. Fatigue and Fracture Toughness of Five Carbon Low Alloy Steels at Room and Low Climactic Temperatures (Part II) A. Steel Founders Society of America Research Report No. 94A. Carbon and Low Alloy Technical Research Committee Steel Founders Society of America. October [5] Hamby, Jeff, John Griffin, and Dr. Robin Foley. Verification of the New Radiographic Testing (RT) Standard through Mechanical Testing. Proceedings of Steel Founder s Society of America Technical and Operating Conference UAB. Dec [6] Sigl, K.M. et al. Fatigue of 8630 cast steel in the presence of porosity. International Journal of Cast Metals Research 2004 Vol. 17 No.3. University of Iowa [7] Hardin, R. A., & Beckermann, C. Effect of Porosity on the Stiffness of Cast Steel. Metallurgical and Materials Transactions A. Vol. 38A(12) The Minerals, Metals, & Materials Society and ASM International [8] Rudy, J. F. and Rupert, E. J. Effects of Porosity on Mechanical Properties of Aluminum Welds. Welding Research Supplement. 322-s 335-s. July [9] ASTM E8/E8M. Standard Test Methods for Tension Testing of Metallic Materials [10] ASTM E709. Standard Guide for Magnetic Particle Testing [11] ASTM E 165/ E165M. Standard Practice for Liquid Penetrant Examination for General Industry [12] Hobbacher, A. Fatigue design of welded joints and components. 1996

42 36 APPENDIX A TENSILE DATA

43 37 Alloy # % Elong. Modulus, E 0.2% YS (psi) UTS(psi) Max Stress Hole in gage Max Indict. Length (in) 165/135 A ,252, /135 A ,691, /135 A ,364, /135 A ,073, /135 A /135 A /135 A ,084, /135 A /135 A /135 A ,601, /135 A /135 A ,094, /135 A /135 A /135 A ,397, /135 A ,015, /135 A ,943, /135 A ,420, /135 A ,637, /135 A /135 A ,623, /135 A ,004, /135 A ,535, /135 A ,245, /135 A /135 A /80 B ,961, /80 B ,086, /80 B ,730, /80 B /80 B /80 B /80 B ,221, /80 B ,316, /80 B /80 B ,320, /80 B /80 B Fract. Surf. Area (%)

44 38 110/80 B /80 B /80 B ,813, /80 B ,456, /80 B ,612, /80 B /80 B ,452, /80 B ,417, /80 B ,942, /80 B ,167, /80 B ,419, /80 B ,000, /80 B ,568, /80 B ,662, /80 B ,545, /80 B ,118, CF8M C ,503, CF8M C ,321, CF8M C ,467, CF8M C CF8M C CF8M C CF8M C ,259, CF8M C ,389, CF8M C ,612, CF8M C ,078, CF8M C CF8M C CF8M C ,764, CF8M C ,908, CF8M C ,570, CF8M C ,625, CF8M C CF8M C ,233, CF8M C CF8M C CF8M C ,384, CF8M C ,822, CF8M C ,286, CF8M C CF8M C ,016, CF8M C

45 39 CF8M C CF8M C ,410, CF8M C CF8M C CF8M C ,580, CF8M C D ,715, D D D ,122, D D D D ,762, D ,355, D D ,094, D ,192, D D ,007, D D D ,127, D ,033, D ,032, D ,805, D ,272, D ,634, D D D ,783, D ,139, D ,997, D ,496, D ,242, Eglin ES ,256, Eglin ES ,012, Eglin ES ,325, Eglin ES ,077, Eglin ES ,972, Eglin ES1-7 26,098, Eglin ES ,824,

46 40 Eglin ES ,081, Eglin ES ,482, Eglin ES ,189, Eglin ES ,922,

47 41 APPENDIX B STEEL CHEMISTRIES AND STRESS-STRAIN CURVES

48 Group C Si Mn P S Cr Mo Ni Co Cu Nb Ti V W Zr , A , B CF8M, C , D Eglin, E

49 43

50 44

51 45

52 46

53 47

54 48

55 49

56 50

57 51

58 52

59 53