Clean Steel Project—                                                          INTRODUCTION                                ...
Test CastingThe casting that was chosen for the study was an engine support fora large off-road vehicle that Harrison has ...
Table 1.                                          After the pattern was rerigged for the basins, it was decided that      ...
Thirty-four variables remained in the study, with a confidence            Phase IIlevel greater than 90%. A group of peopl...
The importance of pouring temperature is shown in Table 5. High             Table 9 also shows that the temperature after ...
recoveries that had an influence on the dirt length were the recoveries   High Level of Oxidizable Elementsof Mn and Si. A...
“good” melting practice and being too hot (which caused furnace            Ladle Treatment Variableslining damage and a th...
Table 15.                 2-Factor, 2-Level Designed Experiments      Slag Thinner’s Effect on Dirt Length   Effects of Re...
Fig. 5. 2x2 matrix on the effects of slag thinner.                                                          Fig. 6. Phases...
Phase V                                                                                              Table 19.            ...
APPENDIX                                                                                Warm Up Variables                 ...
General Foundry Variables                                                             Height of Steel . Based on the geome...
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  1. 1. Clean Steel Project— INTRODUCTION The Harrison Steel Castings Company manufactures parts for off-Identification of Variables road mining and earth moving equipment. The shop pours primarily into green sand molds from bottom pour ladles. Melting furnacesThat Affect Cope Oxide consist of two 20-ton and one 8-1/2-ton acid-lined, electric arcInclusions in Steel Castings furnaces. A clean steel project was started, in an effort to identify the variables that influence the variation in cope surface oxide inclusionsJ.D. Carpenter from one heat to the next. It was hoped that, by collecting as manyR.G. Shepherd melting and pouring variables as possible, statistical analysis couldHarrison Steel Castings Co. be used to identify the important variables that would explain thisAttica, Indiana variation. The following was stated in a paper given at the 1995 Steel Founders’ Technical and Operating Conference: “It is our opinion that reoxidation products are associated with every pound of metal poured; however, factors such as casting design, gating system andABSTRACT metal flow determine the degree and existence of these defects.”1This paper discusses an experiment at a midwest steel foundry From previous work associated with the clean steel project, “ap-concerning the surface cleanliness of one particular casting. proximately 83% of the casting defects in carbon and low-alloy steelThe experiment was started in an effort to identify variables that samples collected in the United States consisted principally ofhave an effect on casting surface cleanliness. The emphasis on reoxidation products.” 2 The main reason for this project was thatthe specific variables that were collected for the study was “…there is still an unknown set of circumstances, which exists in theplaced on the acid steelmaking and pouring operations. process, that suppresses the formation of reoxidation products or otherwise modifies the nonmetallics such that the degree of visual The experiment began by collecting as many variables as material is much reduced.” 1possible from heats containing the casting of interest. The datafrom the 35 heats was analyzed to isolate the important vari-ables. The next phase of the experiment involved setting the EXPERIMENTALidentified variables at their optimum level for 24 heats, in orderto verify their effect. Inspection Methods for Castings A change was then made to the gating system to reduce the The inspection methods used for examining the castings for defectsvelocity of the stream into the mold cavity, and create a more were visual and magnetic particle inspection. Harrison Steel usesstreamlined flow. More data was collected by observing heats both wet and dry magnetic particle inspection methods. The first 29with the new gating system. The collected data was reanalyzed trials used dry magnetic particle inspection. The inspection processto try to confirm the significance of the original variables. was switched to wet magnetic particle inspection during the 30th trial. The difference between the two processes was that, with the wet Both steelmaking and pouring variables were found to have magnetic particle inspection, an average of two inches fewer cracksan effect on the heat-to-heat variation in surface casting quality. per casting were detected. No effect on the amount of dirt wasThe large within-heat variation suggested that the pouring detected with the change to wet magnetic particle inspection.variables have the greatest influence on the surface quality ofthe castings. The measurement system selected for the study was the current process for marking and recording defects after the first magnetic Current trials are underway to gain a better understanding of particle inspection. The quality control technician records each of thethe variables associated with melting practice changes. An marked weldable defects in the appropriate location on a sketch of theoxygen probe will be used to quantify the effect of the changes on part being inspected. A length is associated with each marked defect.active oxygen in the system. The length assigned to a defect is the longest dimension of an oval that can be drawn around the defect. The total number of each type of defect is recorded, as well as the total inches of each type of defect. The categories of defects are cracks, dirt (sand or oxidation products), shrinkage, misrun, gas and other. In order to reduce the variation in the rating process, all of the castings associated with the project were marked by the same inspector and recorded by the same technician. The goal of the project was to reduce the defect category labeled “dirt.” In this report, the dirt number is the number of areas where dirt was found on the casting’s surface; the dirt length is the total severity of the inclusions. A high correlation between the dirt number and the dirt length can be seen in Fig. 1. This conclusion was important because a reduction in dirt will reduce both the number and the severity of defects. Dirt length showed the most variation and wasFig. 1. Dirt number vs. dirt length. chosen as the dependent variable for the data analysis.AFS Transactions 97-31 237
  2. 2. Test CastingThe casting that was chosen for the study was an engine support fora large off-road vehicle that Harrison has been producing since 1976.This casting is termed #2260 in this paper. Figure 2 shows the shapeof the casting. Figure 3 shows the gating system on the casting. Thegating system had approximately a 1:1:1 sprue:runner:ingate ratio.The changes made to the casting during the previous 19 years areshown in Table 1. Recent efforts had lowered the average dirt lengthon the casting from around 42 inches in 1993 to around 32 inches in1994. The clean steel data collection started at the beginning of 1995. Each time this casting was run, five castings were poured in theheat. Each casting had a gross pour weight of 2260 pounds. Thesefive castings accounted for 28% of the weight poured from a 20-ton Fig. 2. Shape of #2260 casting.heat. The castings could be poured anywhere from the start of the heatto the end of the heat; however, they should be poured back-to-back,in order to keep the measurements for individual castings as closetogether as possible. The nozzle could be burned out, but no othercastings could be poured between the molds. Since the heat-to-heat dirt variation rather than the within-heatdirt variation was being studied, the defect lengths from all fivecastings were averaged. This number represented a cleanliness levelfor all of the #2260 castings poured in the heat. When a heat containing the #2260 casting was scheduled forpouring, an observer was always notified. Most of the data used in thestudy was normally recorded for every heat poured. The observerensured the integrity of the recorded data, collected the data notnormally recorded and collected the samples taken during the heat.The observer also ensured that special instructions for each trial werefollowed. The day following a heat, all of the gathered data wasassembled into a packet and assigned a sequential trial number.VariablesThe project of data collection started with a list of variables to becollected that was assembled by the clean steel project steeringcommittee. This list consisted primarily of melting and pouringpractice variables. A complete list of the variables collected is givenin the Appendix. The actual data collection started as soon as the heat was meltedin the furnace and continued until the ladle was slagged. Molding andsand variables were considered background noise in the analysis ofthe data, except when scabs were found on the gating system. Oneheat with scabs on the gate was eliminated from the study, in order Fig. 3. Original gating system.to limit the analysis to reoxidation products rather than includinggross sand defects. sample were used by the melter to calculate the adjustments in theMelting and Pouring Practice ferroalloy additives necessary for the heat, based on the carbon, manganese and silicon levels. After the block additions, the tempera-The furnace was initially charged with bags of graphite to increase ture was adjusted and the heat was tapped into the ladle. The finalthe meltdown carbon level. A charge containing approximately 50% deoxidation of the heat was done by adding pellets of aluminum tofoundry returns and 50% purchased scrap was then dropped into the the tapping stream at the rate of 2 lb/ton. The chemical specificationfurnace. When the initial charge was partially melted, the balance of for the steel poured for the trials is listed in Table 2.the heat was added as a back charge, if needed. When the bath wascompletely melted, a chemistry was taken from a sample dipped from When the heat was tapped, a chemistry sample and a temperaturethe furnace. The metallurgical lab reported the level of carbon, were taken in the pit. The chemistry was quickly checked to deter-manganese and silicon to the melter. Oxygen was injected to lower mine if the heat was acceptable to be poured. If the heat was to bethe carbon to the desired aim point. After oxygen injection, a sample treated with calcium wire, it was treated in the pit.was taken and sent to the lab to verify that the carbon aim had been The castings for the project were poured with a bottom-pour ladlereached. through a two-inch diameter clay nozzle. The flow was controlled After verification that the heat was ready to block, another dip with a graphite-tipped stopper rod. All of the ladles were lined withsample was taken just prior to blocking the heat. The results of this bloating fireclay brick.238 AFS Transactions
  3. 3. Table 1. After the pattern was rerigged for the basins, it was decided that History of Changes to #2260 Casting the first few heats would be poured without the basins. Without the pouring basins, the average dirt length for the first nine heats was 2.2 in. with a standard deviation of 1.745 inches. Phase IV was an attempt to measure the effect of calcium wire on the cleanliness of the castings by removing the wire requirement. This phase consisted of data collected on heats with the new rigging, without the injection of calcium wire in the pit. The average dirt length for these heats was 9.3 in. with a standard deviation of 3.514 inches. Phase V will be continued data collection using the new gating system and calcium wire injection. A more rigorous investigation of melting practice will be done using an oxygen probe. STATISTICAL ANALYSIS TECHNIQUES The majority of the statistical analyses consisted of comparisons between different groups of data. The average and standard devia- tions were calculated for each variable in the two groups. Two mean, equal variance test statistics were run to determine if there was a difference in each variable between the two groups. Simple linear regression analysis (least squares) was also used for continuous variables. Pearson product moment correlation analysis was used toPhases of Data Collection determine how variables affected one another (Note: only correla- tions that exceeded the 90% statistical confidence level are shown inPhase I was observation with little or no interference with the making the paper.) Since the heat-to-heat variation was being studied ratherand pouring of the heats. After 35 heats were observed, the data was than within-heat variation, the variables that were unique for eachanalyzed using student’s “T” tests and regression analysis. The main casting were averaged for each heat.variables that had a potential effect on dirt (cope surfacemacroinclusions) were identified. The average dirt length for theseheats was 32.3 in. with a standard deviation of 8.306 inches. Phase I Phase II involved setting the variables, identified as being impor- The analysis was initially started after the first 12 heats had beentant in Phase I, at their optimum value. These 24 trials were attempts collected. The benefit from beginning the analysis this soon was thatat making clean steel. The goal of these trials was to see if the it identified non-normal distributions for some of the variables. Allidentified variables did, indeed, have the proposed effect on the dirt of the statistical work done on the trials required that each variablelength detected at the first cleaning-room inspection. The average have a normal distribution. The cause of the discrepancies wasdirt length for these heats was 19.8 in. with a standard deviation of related to comparing heats from the 20-ton furnaces with heats from7.542 inches. the 8-1/2-ton furnaces. (Charge makeup and ladles were consider- ably different.) It was specified that this casting could only be poured Phase III came about in an attempt to separate the effect of head in heats from the larger furnaces, and data that had been collectedheight from pouring temperature. Pouring at the start of the heat was from the smaller furnace was eliminated.equivalent to having a high head height and a high pouring tempera-ture. In order to separate the effect of these two variables to see which All of the data was entered into a spreadsheet and the heats werewas more significant, one had to be controlled. Controlling tempera- then ranked by average dirt severity. This ranking was then used toture was not a viable option; therefore, it was decided to use a pouring form two separate groups, labeled clean heats and dirty heats. Thebasin on all of the molds. In order to add a pouring basin, yet keep the averages and standard deviations were found for all of the indepen-same pouring times to avoid mix-running the castings, the pattern dent variables in the study. A student’s T test was then performed onhad to be rerigged. every independent variable collected, to find out whether or not there was a difference in that variable between the groups of clean and dirty heats. (The logic being that, if the variable had the same value when Table 2. the heats were clean as when the heats were dirty, that variable could Chemical Specifications not have had a major influence on dirt.) The output given for a student’s T test is a confidence level that the averages for each group are different. Each variable was assigned a confidence level and the variables were then ranked by this confidence level. The percent chance that there was a difference in the value of the variable between the clean and dirty heats did not imply that there was a relationship between the variable and dirt. It only implied that one might exist. All of the variables that did not meet the 90% confidence level were eliminated from the study.AFS Transactions 239
  4. 4. Thirty-four variables remained in the study, with a confidence Phase IIlevel greater than 90%. A group of people, including representatives The variables identified as being important in Phase I were set at theirfrom Steel Founders’ Society, the University of Alabama at Birming- optimum levels in Phase II to try to make cleaner heats. A meetingham, Tri-State University and Harrison Steel, reviewed the data after was held with the melters and pouring supervisors to explain what24 heats had been collected. It was decided that many of the variables had been learned. The melting practice was modified in order to aimremaining in the study were representing the same thing. For in- for higher levels of oxidizable elements after decarburization. Thestance, the levels of all of the oxidizable elements in the bath just prior molds were all poured at the extreme end of the heat in order to satisfyto blocking the heat remained in the list. Each of these variables were the requirements of lower head height and temperature. The numberexamined individually to see which had the highest correlation with of bags of slag conditioner was increased from one or two to three ordirt. The one with the highest correlation with dirt was chosen to four. The melters were instructed to tap the heat in less than 88represent that family of variables, and the rest were discarded. (Two seconds. The number of heats on the furnace was allowed to fluctuatevariables were kept if the R 2 values were extremely high, as in the because cost and time considerations made this variable impracticalcase of block Si and Mn). Engineering judgment was used to reduce to control.the list to the seven important variables shown in Table 3. After data from 24 heats was collected, the final analysis was Figure 4 shows the distribution of dirt lengths for the 35 observed performed. The analysis of the data consisted of the following.heats. All of the variables will be discussed in detail in Phase II of theproject. 1. A comparison for each variable between the 10 heats with the lowest dirt lengths against the 10 heats with the highest dirt lengths. Table 3. 2. A comparison between the dirt length for the highest 10 heats Seven Important Variables from Phase I against the lowest 10 heats of each variable (i.e., the highest 10 head height heats vs. the lowest 10 head height heats). These two analyses were used as the basis for excluding or including variables. All of the variables that had a statistical confi- dence of over 90% in both studies are listed in Table 4. Most of the variation in cleanliness on the #2260 castings could be explained using three variables. The first two variables were related to where the castings were poured in the heat. Pouring location, from the start of the heat to the end, sets the level of the head height and the pouring temperature in a bottom-pour ladle. The third variable was the level of residual silicon in the bath after decarbur- Table 4. ization (referred to as block silicon). The block silicon was highly 23 Important Variables from Phase II correlated with almost all of the variables during the making of the heat. Fig. 4. Phase I dirt length distribution. Table 5. Effect of Pouring Temperature on Dirt Length240 AFS Transactions
  5. 5. The importance of pouring temperature is shown in Table 5. High Table 9 also shows that the temperature after oxygen injectionpouring temperatures corresponded with dirty heats. Various theo- was highly related to the meltdown Mn and Si. As the meltdown Siries may explain this correlation. One theory was that the higher was increased, the temperature after oxygen increased. The oxida-temperatures superheat the mold, causing the steel to freeze more tion of one pound of Si provides 12,887 Btu. Manganese does notslowly, thus allowing the inclusions time to agglomerate and float provide much heat of oxidation (3131 Btu per lb5); however, it showstoward the cope surface. Another theory is that the inclusions formed as being correlated, due to its high correlation with Si (when the Siat the higher temperatures may be more detrimental to cope surface is high, Mn is high). The meltdown carbon did not correlate well withcleanliness than inclusions formed at lower temperatures. Turkdogan, the temperature after oxygen. The oxidation of one pound of carbonfor instance, has shown that the compositions of the reoxidation generates 4375 Btu.5products change as the formation temperatures change.3 Residual Cr had an effect on the level of residual silicon and Mn The importance of head height in steel cleanliness is shown in after the injection process because of the inter-relationship betweenTable 6. Previous work with the clean steel project used water meltdown Mn, Si and Cr. The level of block Cr for the grade studiedmodeling to show that the air entrainment was proportional to the set the level of the final Cr, since there was no Cr addition. When finalhead height, raised to the 2.5 power, times pouring time.4 Cr showed up in the list of 25 important variables, it related back to heats that finished with a high block silicon. With other items, such as inspection criteria and gating systemconstant, the combination of head height and pouring temperature Table 10 shows the correlation between chemistries taken fromwas the largest contributor to steel cleanliness for a casting. the furnace and the percent recoveries for the elements. The block Mn and Si are highly correlated with the recoveries of these elements. The second largest contributor to steel cleanliness was the biggest The block chemistry and recovery for these elements were directlysurprise in the study. The importance of the levels of residual silicon related to the temperature of the bath after oxygen injection. The onlyand manganese, after the blow, came as a shock to the investigators.Table 7 shows that higher levels of residual silicon, after the blow, are Table 8.associated with cleaner castings. Analysis of Variance Using 3 Important VariablesAnalysis of VarianceDue to the large variation attributed to these three variables—pouring temperature, head height and block silicon—an analysis ofvariance was run on the 59 trials collected. Dirt length was thedependent variable and head height, pouring temperature and blocksilicon were the independent variables. All factors were found to besignificant. The results are shown in Table 8.Oxidizable Elements and RecoveriesTable 9 shows the correlation between the chemistries taken from the Table 9.furnace. The meltdown chemistries were highly correlated with one Chemical Correlationsanother, as expected. If the charges were built with higher alloymaterials, the levels of C, Mn, Si and Cr were elevated at meltdown.The melt chemistry was also highly correlated to the block chemistry.Higher meltdown chemistries had higher block chemistries. Cleanercastings were obtained from heats with higher levels of oxidizableelements after decarburization. Table 6. Effect of Head Height on Dirt Length Table 10. Recovery Correlations Table 7. Effect of Block Silicon on Dirt LengthAFS Transactions 241
  6. 6. recoveries that had an influence on the dirt length were the recoveries High Level of Oxidizable Elementsof Mn and Si. As the recovery of the elements increased, the dirt Prior to Blocking the Heatlength decreased. The authors believe that the correlation between a high block chemistry and low amount of dirt must relate back to the oxygen The recovery of carbon showed a correlation with the level of availability in the bath after the blow. The recoveries were highercarbon just prior to blocking the heat only. The recovery of carbon when the block silicon was high. As the available oxygen waswas not correlated with the recoveries of Mn, Si or Al. Carbon lowered (at higher block chemistries), the recovery of the elementsrecovery was not associated with dirt severity. increased and the dirt length decreased. The recovery of Al was mildly correlated with the level of carbon Much research has stated that oxygen content after the boilin the bath. A separate analysis was made in order to examine what follows a steady-state curve, based on the carbon content.6,7 From thevariables were affecting the recovery of Al, since no correlations to data collected, one conclusion has to be drawn from the followingthe other recoveries were found. Aluminum recovery was correlated two choices: 1) the active oxygen in the bath was dependent on morewith the level of carbon in the bath, the tap temperature, the type of than carbon content or 2) the oxygen available that affected thecalcium wire used (solid calcium vs. Cal-Sil) and the temperature recoveries did not come from the steel bath. Work done by Fittererduring the wire injection. The authors suspected that the length of the states that the level of active oxygen in the bath, after the blow, istap, in seconds, would be correlated to the Al recovery, but no dependent on more than the carbon content.8 According to Svoboda,correlation was found. The shape of the stream during the tap would “Silicon content after the carbon boil will vary from about 0.02 toprobably explain some of the variation in Al recovery and in dirt 0.10%, and Mn from 0.07 to 0.15%. Recommended aims at this pointlength; however, a method to rate the stream consistently, from tap are 0.08% Si and 0.15% Mn. Manganese content greater than 0.15%to tap, was never found. tends to inhibit gas removal because the Mn causes the boil to be less vigorous.”9 Table 11. The levels of residual Si and Mn were not set by the level of the Relationship Between Carbon and Recoveries carbon in the bath. They were directly related to the temperature of the bath after the oxygen injection. Clean heats with high recoveries were made at all levels of block carbon (0.085–0.195% C) as shown in Table 11. Oxidizable Elements and Block Silicon Levels In order to further verify the importance of the block silicon, all of the heats collected in Phases I and II were divided into two groups, based on the block silicon level. The first group was all of the heats with 0.045 block silicon and higher (specified as a good melting practice). The second group was all heats with less than 0.045 block silicon (specified as a bad melting practice). Table 12 shows the values of some of the related variables. The primary difference between a good and a bad melting practice was the temperature of the bath, during and after the oxygen injection. The other differences are reflections of this variable. Carbon removal is more favorable at the higher bath temperatures.5 The higher meltdown chemistries created higher bath temperatures, Table 12. due to the heat released during the oxidation of Si and Mn. The higher Good Melting Practice vs. Bad Melting Practice chemistries required more oxygen to remove these elements. As a result of the higher temperatures, the slag became thicker and required more lime and ore additions to get the viscosity back within the specified limits. The increased levels of oxidizable elements at the block was believed to be related to lower levels of active oxygen in the system available to oxidize the alloy additions. As a consequence of the lower oxygen availability, the recoveries were higher. All of the block alloy additions had to be lowered to keep the chemistry near the aim points, due to these higher recoveries. Two approaches were used to achieve a better melting practice with higher temperatures during the oxygen injection. The first was to raise the amount of oxidizable elements in the bath, such that the excess heat from oxidation of these elements ensured that the bath temperature was high enough. The second was to make sure the bath temperature was high enough before oxygen was started. Both practices have worked successfully; however, a combination of both seemed to work the best to guarantee the proper temperature was reached. A balance was found between being hot enough to have a242 AFS Transactions
  7. 7. “good” melting practice and being too hot (which caused furnace Ladle Treatment Variableslining damage and a thick, chunky slag). Currently, the melters are Early in the course of these trials, there was a change from steel-cladobtaining the carbon, Mn and Si rather than just carbon before calcium wire to steel-clad Cal-Sil powder wire, for cost reasons. Aadjusting the temperature prior to oxygen injection. study was run in order to determine if there would be a detrimental effect from the change. It was found that the Cal-Sil powder wire Good melting practice was achieved with the following methods: produced a much less violent reaction than did the solid Ca wire. As 1. Started with higher meltdown Si (increased by 0.11% Si). a result of less steel exposed to the air during the injection process, 2. Started with higher meltdown Mn (increased by 0.15% Mn). the Al recovery improved from 38.2% to 40.1% (90% confidence 3. Started oxygen with a higher bath temperature to promote level). The Mn and Al losses per minute were also reduced with the oxidation of carbon. (Starting temperatures were set based on change in wire type (99.95% confidence level). the meltdown chemistry.) Results of a good melting practice were: The silicon recovery went from 87.6% to 94.7% (99% confidence 1. Finished with higher block Si (higher by 0.016% Si). level). Silicon is part of the addition with the Cal-Sil wire. Part of the 2. Finished with higher block Mn (higher by 0.038% Mn). Si addition was removed from the block and accounted for via the 3. Had higher recovery of Si (7.5% higher recovery). wire injection. By adding the last part of the Si addition to a fully 4. Had higher recovery of Mn (3.4% higher recovery). killed steel in the ladle, the recovery was much higher, as expected. 5. Had to add 26.2 lb less FeSi (also change in wire practice, Pouring Practice Variables shown later). From earlier analyses, it was found that head height, pouring tem- 6. Had to add 46.5 lb less Si-Mn. perature and block silicon account for a large percentage of the 7. Had to add 17.2 lb less Fe-Mn. variation in casting quality. The effects of the other pouring variables 8. Had a higher hardenability due to Mn recovery. are shown in Table 14. The flow rate was measured in pounds perDiscussion of Ability to second (gross weight poured taken from scales and divided by theMeasure Block Silicon Level number of seconds to pour mold). The stream rating was a visualAfter reporting these findings, Harrison’s capability of measuring Si rating of the stream quality. A numbering system was employed thatlevels in these ranges came under question. Two studies were rated the stream quality to a number from 1 to 5. Stream ratings of 1conducted to determine the capability to measure silicon at these were considered perfect streams with no slant or flaring. Slanted,levels. The spectrometer reports the value of the elements to multiple flared streams that were splashing out of the pouring cup onto the topdecimal places and then truncates them to five decimal places. of the mold were rated at 5. The first study was a comparison between the value originally The conclusions from this data were that the most importantreported for block silicon with a new analysis run on the same variables were still the pouring temperature, head height and blocksample. Ten heats were retested and the new value found for Si was silicon. The highlighted conditions show that the best castings werecompared to the original value. The average difference in the Si poured with a good quality stream into the pouring cup and low flowbetween the heats was 0.00136% Si. This accounts for about a 3% rates into the mold cavity.difference. Slag Thinner Variables The second test was run on one heat with a low reported block The number of bags of slag thinner that was added to the ladle wassilicon and one heat with a high reported block silicon. Replicate tests found to have a significant effect on dirt length. Table 15 shows thewere run on each heat to see how much spread existed in the readings. effect of the slag thinner. This product was used by the foundry toThe average and standard deviations are shown in Table 13. The table keep the ladles cleaner. When the ladles were dumped at the end ofshows that the level of the residual block silicon after oxygen the heat, this addition allowed almost all of the slag to be removed.injection was measurable to at least three decimal places and that a The specifications on the product are shown in Table 16. Thisdifference did indeed exist between heats. variable initially appeared due to differences in the addition practice between the day and night shifts. Figure 5 shows that the slag thinner Table 13. affects the dirt length regardless of where the castings are poured in Measurement Capability of Block Silicon the heat. Heats with three and four bags of added thinner produced cleaner castings than did heats with one or two bags. Table 14. Effect of Flowrate and Stream Quality RatingAFS Transactions 243
  8. 8. Table 15. 2-Factor, 2-Level Designed Experiments Slag Thinner’s Effect on Dirt Length Effects of Refractory Variables—There was an interest in what could be learned from 2x2 matrices, with regard to how the refractory variables interacted with other variables. The conclusion from this analysis was that the importance of some variables was dependent on the level of another variable. The effect of a variable was often masked by the levels of other variables. The effect of the number of heats on the furnace lining was found to be significant in the original observation trials. As more data was collected, this variable dropped out of the picture. The number of heats on the furnace lining may have (70% confidence Table 16. level) a very small effect on dirt length (less than 1 inch of dirt, on Slag Thinner Specifications average). Clean steel can be made with a high number of heats on the furnace lining. The number of heats on the ladle refractories did not have an effect on dirt unless other more important variables were at their optimum level. The number of heats on the walls and bottom of the ladle had a small influence on dirt in the cleaning room. At high head heights and pouring temperatures, the effect of the ladle refractory was masked. As castings became cleaner, the effect was more pronounced. Table 17 confirms that the number of heats on the ladle refractory was not a significant contributor to dirt unless the castings were poured at the end of the heat. Effects of Tapping Rate—No effect on the dirt due to the tapping rate was found. This variable showed as being a potentially signifi- cant variable in Phase I, but was found not to be a significant factor during Phase II. The tapping rate has an effect on other variables, but does not have an effect on the dirt found on the castings, as shown in Table 17. Table 18. 2x2 Matrices on Refractory Variables Distribution of Dirt Lengths When the most influential variables were set at a more optimal level, there was a wide distribution of dirt lengths. The average dirt length was shifted downward, but the standard deviation was unchanged. This wide distribution revealed that all of the variables that affect the cleanliness were not being closely controlled. Figure 6 show the distribution of dirt lengths from Phases I and II. Phase III Phase III came about from an effort to separate the effect of head height from pouring temperature. Since bottom pour ladles were used, castings poured at the end of the heat had a low head height and were also the coolest, due to the longer times in the ladle. By putting a pouring basin on all of the molds, the effect of head height in the Table 18. ladle could be reduced. A better comparison could then be made 2x2 Matrices on Tap Rate between castings poured at the start of the heat (hotter) and castings poured at the end of the heat (colder). In order to see if this was feasible, one heat was poured with a pouring basin on two of the five castings in the heat. The pouring time doubled on the molds with the basins, and the castings were severely misrun. In order to get a similar pouring time, the foundry engineers had to open up the choke in the gate. The problem was that the choke was the entire gate system (approximately a 1:1:1 gating ratio). The casting was rerigged using a streamlined gate with a 1:3:3 gating ratio. The new gating system is shown in Fig. 7. Castings were then poured with the new gate, without using pouring basins. Nine of these heats were indexed and the average dirt length dropped to 2.2 inches, as shown in Fig. 8.244 AFS Transactions
  9. 9. Fig. 5. 2x2 matrix on the effects of slag thinner. Fig. 6. Phases I and II dirt length distributions. Fig. 7. New gating system. Phase IV After data from nine heats with the new gate was collected and the improvement was verified (2.2-in. average dirt length), the calcium wire injection requirement was removed. This phase of the trial existed for two reasons. The first was an academic interest in the effect of calcium wire on the job. The second reason was an effort to offset the additional cost to produce the casting after the rigging was modified, which added 200 lb to the gating system. The effect of Ca wire is shown in Fig. 9. After data from four heats with the new gate without wire treatment was collected, the requirement of calcium wire was rein- stated. The average dirt length for the four nonwired heats was 9.3 inches. Figure 10 shows the distribution of dirt lengths for the four phases of trials. One interesting note on this portion of the trial was that the head height and pouring temperature have the same effects found earlier. The two heats where the castings were poured at theFig. 8. Phases I, II and III dirt length distributions. start of the heat had more dirt, as shown in Table 19.AFS Transactions 245
  10. 10. Phase V Table 19. No Wire, Head Height and Pouring Temperature EffectsData collection is continuing with the new gate system with thecalcium wire requirement, to see whether or not the variation incleanliness can be explained with the new gate using the samemelting and pouring variables. Oxygen probe analysis will also bemade on heats, in order to verify the suspected relationship betweenresidual block silicon and soluble oxygen in the bath.CONCLUSIONS1. It appears that the magnitude of the cleanliness was set by thegating system (new gate vs. old gate). This was the most importantvariable that determined how clean a casting was going to be, beforethe first casting was poured.2. A large variation in average casting quality was seen from heat toheat, with a particular gating system. Some heats were cleaner thanothers, in terms of casting quality. The heat-to-heat variation ofaverage casting quality was partially explained through both meltingand pouring variables.3. The largest variation from heat to heat was correlated to varia-tions in the head height and pouring temperature. Fig. 9. Effect of Ca wire.4. The second largest variation from heat to heat was due todifferences in the melting practice that can be associated with theresidual block silicon level.5. The large within-heat variation indicated that melting practicecould not account for all of the variation. Some of the variation mustbe explained by the pouring variables. A similar conclusion wasdrawn in a paper comparing acid vs. basic melting: A high variationwithin a heat relative to the variation between heats suggests thatgating and pouring operations are the most important factors.106. The large within-heat variation, after controlling the head height,pouring temperature and melting practice, was attributed to variablesassociated with the filling of the mold cavity (flow rate and streamquality). Fig. 10. Phases I, II, III and IV dirt length distributions.SUMMARYData collection and analysis of this type will continue to be an REFERENCESimportant part of daily operations at Harrison Steel. The knowledgegained through these type of studies is invaluable to remaining a 1. J.E. Parr, “Clean Heat/Dirty Heat, The Practical Implications of Datacompetitive supplier of steel castings. The data collected also serves Collection,” Transactions of the 49th SFSA T&O Conference (1995).as a valuable base of information to find relationships between 2. J.A. Griffin and C.E. Bates, “Development of Casting Technology toseemingly unrelated variables. We will continue to pose questions Allow Direct Use of Steel Castings in High Speed Machining Lines,”that this data might answer. SFSA Research Report No. 100, May 1987. 3. E.T. Turkdogan, “Deoxidation of Steel—What Happens From Tap to Further data collection will continue. If the melting and pouring Solidification,” Electric Furnace Proceedings (1966) pp 22-28.practices have an effect on all gating systems, an emphasis will be 4. M. Blair, R. Monroe and C. Beckermann, “The Effect of Pour Time andmade on improving and more closely controlling both areas, in order Head Height on Air Entrainment,” Transactions of the 47th SFSA T&Oto get a better product. If the melting and pouring practices are only Conference (1993).significant on certain gating arrangements, the gating practices will 5. BOF Steelmaking, Volume 2, Design Part II, Operation, Special Topics, Process Technology Division, Iron & Steel Society of AIME (Chapterhave to be analyzed for their effects on cleanliness. 13). Future trials will try to use an oxygen probe to determine if more 6. D.L. McBride, “The Physical Chemistry of Oxygen Steelmaking,”consistent recoveries and better control of deoxidation can be accom- Journal of Metals, vol 12, No. 7, July 1960, p 531.plished. The probe will also be used to try to correlate the amount of 7. D.A.J. Swinkels, S.R. Richards, C.J. Cripps Clark, C.W.P. Finn and A.T.active oxygen in the bath with heat cleanliness. Hart, “Oxygen Probe Applications in Steelmaking,” Open Hearth Pro- ceedings, vol 25 (1972), pp 80-93. 8. G.R. Fitterer, “Some Current Concepts of the Oxidation and Deoxida-ACKNOWLEDGMENTS tion of Liquid Steels,” AIME Electric Furnace Proceedings, vol 35, 1977 pp 302-307.The authors would like to thank Harrison Steel for allowing us the 9. J.M. Svoboda, “Melting and Deoxidation of Cast Steels” Steel Castingopportunity to work on this project. Special thanks should also be Metallurgy, Steel Founders’ Society of America, 1984.extended to B. Trimble, S. Hughes and T. Hanson for their efforts in 10. R. Monroe, M. Blair and J. Griffin, “Variations in Casting Qualitythe data collection. Analysis” Transactions of the 49th SFSA T&O Conference (1995).246 AFS Transactions
  11. 11. APPENDIX Warm Up Variables Warm up Time . Time in minutes that it takes to warm up the heat to the desired tap temperature after the block addition has been made.Variables Collected for the Tap Temperature Tap temperature is taken just prior to rolling the furnaceClean Steel Project over for tapping. The temperature is taken with an immer- sion thermocouple.Background Variables Pit VariablesTrial Number . . Sequential number to uniquely identify each trial Seconds toDate . . . . . . . . . Date castings were poured Tap Heat . . . . Clock was started as soon as the first material came off theHeat Number . . Heat identification end of the spout. The clock was stopped when the material flowing from the spout was all slag and had tapered offHeat Code . . . . . Coded identification for customer considerably. The tap rate was calculated based on theSerial Number . Uniquely identified each casting poured in a heat steel weight tapped and the tap time.Steel Grade . . . . Harrison Steel designation Ladle Chemistry The ladle chemistry was dipped from the furnace with a spoon. No additions were made to the test. The test wasChemical Variables dipped out as soon as the ladle was filled. The test was aChemical “Go” or “No-Go” test for elemental ranges. Recorded Analysis . . . . C, Mn, P, S, Si, Ni, Cr, Mo, Cu, B, Sn, Al, Ti, V, Zr, Dl elements were C, Mn, P, S, Si, Al.Melting Variables Pit Temperature Pit temperature was taken as soon as the ladle test had been taken by an immersion thermocouple.Furnace . . . . . . . Identification of melting furnaceMelter . . . . . . . . Melter’s name Wire Feeding VariablesHeats on Furnace Number of heats on furnace refractory Before Wire Cool The number of minutes the heat was cooled in order to getRepairs After . . Whether or not the furnace lining was repaired after the to the aim wire feeding temperature. heat was tapped After Wire Cool The number of minutes the heat was cooled in order to getRepairs Before . Whether or not furnace lining had been repaired before closer to the desired foundry temperature. charging the heat Type of WireRefining Variables Used . . . . . . . Wire type was either Cal-Sil (which was a steel clad wire with solid Cal-Sil powder inside) or calcium (which wasO2 Volume . . . . Volume of oxygen injected into the bath, measured in steel clad solid calcium). standard cubic feet Length of WireO2 Time . . . . . . Minutes of oxygen injection Used . . . . . . . The length of wire added measured in feet. Used toC Blown . . . . . . Total C removed (%) and % removed per minute of calculate the amount of calcium added to the heat. oxygen Velocity of Wire The velocity of the wire into the ladle determined howMn Blown . . . . . Total Mn removed (%) and % removed per minute of deep the wire gets before the steel cladding melted and the oxygen reactive metal was released.Si Blown . . . . . . Total Si removed (%) and % removed per minute of After Wire Temp The temperature was taken immediately after wire injec- oxygen tion had stopped.Cr Blown . . . . . Total Cr removed (%) and % removed per minute of Calcium per Ton Weight of calcium added to the heat by the heat weight in oxygen tons.Block Chemistry Complete chemistry taken from dip sample just prior to adding the block addition to the furnace Ladle VariablesFurnace Addition Variables Ladle Number . . A serial number was used for each ladle as identification.Dust . . . . . . . . . Weight of the recycled furnace dust added to the charge. Empty Ladle Wt. This weight was read off of the scales after the heat was poured and the slag had been dumped from the ladle.Lime . . . . . . . . . Added to condition slag, estimated weight of lime (in pounds). Estimation is based on the average weight of a Heats on Walls . The number of heats that had been poured from the ladle shovel full of lime. since it was rebricked. Heats on Bottom The number of heats that were on the ladle refractory in theIron Ore . . . . . . Added to assist the Boil-Weight (in pounds) added, in bottom. Rammed material. The ladle nozzle was changed order to assist in getting a hard carbon boil in the furnace after every heat. during refining. Weight is based on the weight of an Nozzle Size . . . . Diameter of the nozzle used in inches (all heats had a 2 average shovel full of iron ore pellets. inch nozzle).Pig Iron . . . . . . . Added to raise carbon level when 5 or more points of Ladle Lining . . . All of the heats were poured with ladles that had been lined carbon are needed, pig iron is used. If less than 5 points of with bloating fireclay brick. carbon are required, graphite is added to the stream during Nozzle Material All heats were poured with clay nozzles. tapping of the heat. Bags of SlagSi-Mn . . . . . . . . Weight of silico-manganese added to the heat during the Thinner . . . . . Number of bags of slag thinner used to assist in cleaning block in the furnace. the ladle of slag after the heat has poured. If one bag wasFe-Si . . . . . . . . . Weight of ferrosilicon added during the blocking of the added, it was thrown on top of the slag after the heat was heat in the furnace. tapped. If two bags were added, one was thrown in at 1/2Med C Mn . . . . Weight of medium carbon manganese added during the ladle and the other after the slag was all tapped. blocking of the heat in the furnace.Extra Med C Mn When a long warm up time is needed, an extra amount of Foundry Variables medium carbon manganese was added to replace the Foundry Cooled Number of minutes that heat was cooled in order to get to manganese that was lost due to oxidation. the aim pouring temperature.Graphite . . . . . . Weight of graphite added to the tap stream to bring the Foundry Time . . Minutes from tap until the ladle arrived in the foundry. carbon level to the desired endpoint. Recorded to keep track of how long it had been since theAluminum . . . . . Weight of aluminum added to the tap stream to finish heat was tapped for temperature losses. killing the heat and leave enough free aluminum to meet Foundry the desired aim point. Temperature . Temperature in degrees Fahrenheit.AFS Transactions 247
  12. 12. General Foundry Variables Height of Steel . Based on the geometry of the ladles, the height of the steelHeat Director . . Name of the heat director in charge of the pouring opera- in the ladle was calculated before every pour. The height tion. was used for velocity. Since there was only a small changeLadle man . . . . . Name of person operating the stopper bar on the ladle. in height for each mold, the height at the start of the pourTap to Pour . . . . The time was recorded from the time the tap started to the was used each time. time the ladle was opened for each box. The time was Head Height . . . The head height, in this case, was the height from the recorded to the nearest tenth minute. bottom of the pouring cup (in the runner) to the steel level in the ladle.Pattern Specific Pouring Variables Volume of Steel Calculated volume of steel in ladle before each pour. TheMold volume was in cubic inches and converted into gallons. Identification The data was collected for all of the 2260 molds poured in Volume Poured . Volume poured for each box. the heat. Weight of Steel . Weight must be calculated because the scale weight doesPouring Order . . During the pouring of the heat, each event got numbered. not account for the weight of empty ladle. The weight of The first event was always 1 and was the pouring of the the slag was unknown until the end of the heat. initial ingot. The next event was the first mold poured. Slag Weight . . . Weight of the slag in the ladle. Burnouts and tests were also recorded as an event. All events got a time and weight recorded. Mid-Pour VariablesWeight Poured . Number of seconds to pour a mold. This time was recorded Mid-Pour Time . Minutes from the tao until tests were poured. by the heat director for each mold. Mid-PourFlow Rate into Temperature . Temperature in ladle at the time the tests were poured. Mold . . . . . . . This rate was calculated by dividing the gross pour weight Final Chemistry The final chemistry was the official analysis for the heat. by the seconds taken to pour the mold.Velocity of Oxidation & Reduction Variables Stream . . . . . . This was calculated based on the square root of 2gh, where Manganese Fade The amount of manganese lost due to oxidation. The loss g was 32.2 ft/sec2 and h was the height the steel falls before rate was calculated on a per minute basis to account for reaching the cup in the runner system. different testing times for each heat.Temperature at Aluminum Fade The amount of aluminum lost due to oxidation. The loss Pour . . . . . . . This temperature was found by extrapolation from known rate was calculated on a per minute basis to account for temperature and times. different testing times for each heat.Burnout of Silicon Pickup . Silicon pickup from the ladle test to the final test. This loss Nozzle . . . . . . If the nozzle was lanced with oxygen prior to the mold is also divided by time to account for different testing being poured, the mold was labeled as a burnout mold. If times. the nozzle was not lanced prior to the pour, the mold was marked as none. Miscellaneous VariablesVisual Stream Ladle Slag . . . . . A visual examination on the color of each slag sample was Rating . . . . . . Visual stream ratings were taken every 5 seconds during performed and the appearance of the ladle slag fracture the pour. Each rating was a number from 1 through 5. A 1 was recorded. was a perfect stream and a 5 was the worst stream. All Furnace Slag . . . A visual examination on the color of each slag sample was readings taken on each box were averaged to have one performed and the appearance of the furnace slag was number to represent the stream quality. A visual rating recorded. system was employed to characterize the integrity of the stream during the pouring of each mold. A number from Points Added . . The weight percent of C, Mn, Si, Al and Ca were calcu- 1 to 5 was assigned to rate the stream. A 1 represented the lated based on the alloy additions. The points of each best stream possible. A 5 represented the worst stream element were then divided by the heat weight to get the possible, as far as flaring. The stream was rated every 5 points of each element per ton of steel. seconds during the pour. At the end of the pour, all stream Response Variables (Index) ratings for one box were averaged. Crack Number . Number of areas on the casting that require repair due toVariables Measured or Used in Calculations cracks.Gap (Nozzle Crack Length . . Length of circled areas on the casting that require repair to Can) . . . . . This measurement was necessary to calculate the total fall due to cracks. of the stream for velocity calculations. After a few trials, Dirt Number . . . Number of areas on the casting that require repair due to the method was abandoned and the heat director was inclusions or dirt. responsible for keeping this gap between 4 and 5 inches Dirt Length . . . . Length of areas on the casting that require repair due to for every mold. inclusions or dirt.248 AFS Transactions

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