The Pennsylvania State University. The Graduate School. College of Engineering VALIDATION OF CONTROLLING BATCH MULLER BY ENERGY DOSE.

Transcription

1 The Pennsylvania State University The Graduate School College of Engineering VALIDATION OF CONTROLLING BATCH MULLER BY ENERGY DOSE A Thesis in Industrial Engineering by Di Wang 205 Di Wang Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science December 205

2 ii The thesis of Di Wang was reviewed and approved* by the following: Robert C. Voigt Professor of Industrial Engineering Thesis Adviser Nanyin Zhang Associate Professor of Biomedical Engineering Janis P. Terpenny Peter and Angela Dal Pezzo Head of Industrial Engineering Head of the Department of Industrial Engineering *Signatures are on file in the Graduate School.

3 iii ABSTRACT Foundries control batch muller of green sand system by controlling mulling cycle time. It is believed that decreasing mulling time will cause sand property degradation and casting quality issues. However, it is also possible to control mullers using batch-to-batch mulling energy dose. In this study our analysis of sand properties, sand related scrap and before and after mulling with batch energy control rather than mulling time control will be presented. Decreasing mulling time resulted no degradation in sand properties or casting quality. Mulling to energy implementation increased the throughput of muller by more than 20%. In addition, for further research strategies that use various mulling energy dose to control batch muller (Mulling to Asymptote) and use advanced statistical method (ARIMA) to analyze energy and other sand properties will be discussed.

4 iv LIST OF CONTENT LIST OF FIGURES... vi LIST OF TABLES... viii ACKNOWLEDGEMENTS... ix Chapter Introduction.... Problem Statement....2 Neenah Foundry Background Data Collection and Scope of Work... 3 Chapter 2 Literature Review Green Sand Composition Green Sand Molding Cycle Why Use Energy Dose to Control Batch Muller Instead of Time Standard Sand Test Variables and Casting Scrap... 0 Chapter 3 Analysis Procedure Normality and Stability Checks Check Correlation sample t Test Levene s Test Mulling Analysis Mulling to Asymptote Development Chapter 4 Results Sand Properties Analysis Result Sand Scrap Data Analysis Mulling to Energy Analysis Result Mulling to Asymptote Analysis Result Chapter 5 Conclusion and Future Work REFERENCES APPENDIX A: AFS Green Sand Test [2] APPENDIX B: Common Sand Related Casting Defects Sorted by Sand Characteristics, Excerpted from Analysis of Casting Defects [54]... 58

5 v APPENDIX C: Sand System Control Changes at Neenah Foundry during MtE Implementation APPENDIX D: Sand Properties Data Sample, Neenah Foundry Sand Control Files APPENDIX E: Sand Related Scrap Data Sample, Neenah Foundry Sand Control Files APPENDIX F: Compactability Controller Date Sample (Mulling Time, Mulling Energy SetPoints), Neenah Foundry Sand Control Files... 64

6 vi LIST OF FIGURES Figure General Metal Casting Process Flow [8]... 7 Figure 2 A Typical Mulling Energy HP Curve... 8 Figure 3 Mulling Energy Variation under MtT Control... 0 Figure 4 Variation in Energy Dose for a Mulling Cycle Controlled by Fixed Mulling Time Figure 5 Logic of Mulling to Asymptote Control Algorithm Figure 6 Before/After Capability Comparison Diagnostic Report for Compactability Figure 7 Before/After Capability Comparison Diagnostic Report for Meth Blue Figure 8 Before/After Capability Comparison Diagnostic Report for Density... 3 Figure 9 Before/After Capability Comparison Diagnostic Report for Split Tensile Strength... 3 Figure 0 Before/After Capability Comparison Diagnostic Report for Present Moisture Figure Before/After Capability Comparison Diagnostic Report for TM/MB Clay Figure 2 Before/After Capability Comparison Diagnostic Report for Green Compression Strength Figure 3 Before/After Capability Comparison Diagnostic Report for Permeability Figure 4 ACF PACF Plots of Compactability after MtE Figure 5 ACF PACF Plots of Compactability before MtE Figure 6 ACF PACF Plots of Density after MtE Figure 7 ACF PACF Plots of Density before MtE Figure 8 ACF PACF Plots of Split Tensile Strength after MtE Figure 9 ACF PACF Plots of Split Tensile Strength before MtE... 36

7 vii Figure 20 ACF PACF Plots of Present Moisture after MtE Figure 2 ACF PACF Plots of Present Moisture before MtE Figure 22 ACF PACF Plots of Methylene Blue Clay after MtE Figure 23 ACF PACF Plots of Methylene Blue Clay before MtE Figure 24 ACF PACF Plots of TM/MB Clay after MtE Figure 25 ACF PACF Plots of TM/MB Clay before MtE Figure 26 ACF PACF Plots of Permeability after MtE Figure 27 ACF PACF Plots of Permeability before MtE Figure 28 ACF PACF Plots of Green Compression Strength after MtE Figure 29 ACF PACF Plots of Green Compression Strength before MtE Figure 30 Time per Batch before and after MtE Figure 3 Time per Batch before and after MtA Figure 32 8-Week Average kwh/ton Mulled... 48

8 viii LIST OF TABLES Table Sand Test Variables and Test Frequency... Table 2 Summary of Empirical Parameters Developed by Heine et al. [6-54], as summarizes by Land [4]... 4 Table 3 Muller Efficiency Calculations (%ME) []... 8 Table 4 Models Indicated by the Shape of the Autocorrelation Function [57] Table 5 Before/After Capacity Analysis Summary Comparing Sand Properties before and after MtE Implementation Table 6 Sand Properties 2-Sample t Test Result Table 7 Sand Properties Levene's Test Result... 4 Table 8 Sand Related Scrap 2-Sample t Test Result Table 9 Sand Related Scrap Levene's Test Result Table 0 Time per Batch Changes before and after MtE Table Energy Saving under MtE... 48

9 ix ACKNOWLEDGEMENTS It would not have been possible to finish my thesis without a great deal of guidance and support from the people around me. I would like to deeply thank those people who, during the several months in which this project lasted, provided me with everything I needed. First of all, I would like to express my deepest appreciation to my committee chair, Professor Robert C. Voigt for his guidance, advice and support during the entire project. I have benefited greatly from his outstanding insight and rigorous research attitude. Without his guidance and persistent help, this thesis would not have been possible. Then, I would like to give special thanks to the members of my thesis committee for their support: Professor Nanyin Zhang and Professor Janis P. Terpenny. I would also like to thank Neenah Foundry and WE Energies for providing data and aiding this project significantly. Finally, I owe huge gratitude to my parents for their financial and mental support of my graduate study. Without their selfless and endless help, I would not have the chance to study in the Pennsylvania State University.

10 Chapter Introduction. Problem Statement In the past, foundries using green sand system have controlled batch muller using fixed muller cycle time []. Mulling control was based on fixed mulling time which did not vary from batch to batch. It is believed that decreased mulling time will cause casting scrap issues. Recently, Neenah Foundry, Neenah WI has tried to control mulling by controlling energy dose per batch instead of using fixed cycle time per batch. This is referred to as Mulling to Energy (MtE). Neenah Foundry began Mulling to Energy control for every batch of sand on one of their production lines early in 205. The sand system performance data after Mulling to Energy has been compared with sand system performance data before Mulling to Energy. Based on this validation, Neenah Foundry is implementing Mulling to Energy on another production line late in 205. A 20% energy saving without increases in casting scrap as decreases in sand properties have been under the implementation of Mull to Energy. During mulling, when the sand stops meaningfully increasing in strength and becomes stable, the energy demand during mulling stops increasing. We can therefore create a control algorithm to let the sand properties tell us when it is the best time to discharge the sand from muller with energy monitoring. Mulling to Asymptote (MtA) is an advanced mulling energy control strategy which is being evaluated at one plant in Neenah Foundry at the present time. Further advanced statistical analysis of the impact of Mulling to Asymptote implementation on green sand control will also be evaluated.

11 .2 Neenah Foundry Background 2 Neenah Foundry is located in Neenah, Wisconsin, they are a supplier of municipal iron castings including manhole frames, lids and grates, trench castings, decorative tree grates, to name a few. Since 872, Neenah Foundry has been a consistent leader in producing durable castings that are used across the country [2]. Neenah Foundry operates with a strict adherence to process standards, which assure consistent quality. They closely monitor each step of the operation during the manufacturing process. The quality of Neenah castings is the result of painstaking control and attention to detail for all foundry processes. Currently there are 4 green sand production molding lines at Neenah Foundry; mulling to energy has already been implemented in two of them. Our analysis has mainly focused on the first MtE implementation. This production line has two molding centers : ) A 203B, MK4 Disamatic flaskless molding machine with PLC control and hydraulic upgrades including ultrasonic position measuring, automatic core setting, and quick change pattern changer. 2) A 2070B, MarkII Disamatic flaskless molding machine with automatic core setting and quick pattern change. The return system sand includes magnetic separator, a G.K. fluidized bed cooler and a rotary screen. New sand and 70% of the make-up premix (00% western bentonite, seacoal, cuastisized lignite, cereal and soda ash) is added by K-tron units at a Hartley sand cooler where water is also added for further cooling and blending. A black water system is also used for recycling of baghouse dust and VOC environmental control. The sand is mulled by two B.P. 00 B speed mullers with a batch size of 5800 pounds. Approximately 30% of the premix is added at the muller employing a dynamic air addition system. There is an intermediate 35 ton sand storage bin and two 90 ton final storage bins-one over each muller. Sand batch compactabilites are controlled by Hartley compactability control units.

12 The bond and water additions to the green sand system at the muller are controlled by both 3 casting parameters and the state of the current sand system, which is determined through automatic testing and additional sand lab testing green sand properties on a regular basis. Mold and casting size determine bond additions, while the automatical compactability determines water additions for future batches of sand. Specifically, the compactability of specific muller batch is used to determine the water addition for next batch [3]. The monitored variables from the sand lab test results are used to make long-term decisions on target compactability, mulling time, and other sand system performance measures..3 Data Collection and Scope of Work To validate the effectiveness of Mulling to Energy control, we collected system data from 8/8/204 to 9/27/204 before Mull to Energy implementation and from 6/8/205 to 7/3/205 after Mull to Energy implementation. Both sand lab test data and sand related scrap data for these two periods was used to validate that controlling muller by energy dose per batch is an efficient way to reduce average mulling time without decreases in green sand properties or increases in green sand related scraps. We also collected average, maximum and minimum mulling time and mulling energy (HP-sec setpoint) for each mulled sand batch during these two periods to analyze how much time and energy could be saved by MtE implementation. Many green sand characteristics were monitored. Neenah Foundry automatically controls bond additions, water additions, compactability, moisture levels, temperature, and muller mixing time. Simultaneously, they monitor the sand with the results of fifteen different tests completed in the sand lab. Some of the collected sand lab properties are monitored directly, while other characteristics used for monitoring are calculated from measured properties.

13 The frequency of sand testing differs depending on the type of test. The sand tests from the 4 foundry lab are conducted as frequently as every two hours (the approximate sand system turnover time) or as infrequently as -2 times a week. Data for the automatically controlled variables are recorded every minute or every batch. These sand tests well be discussed in more detail in Chapter 2.

14 Chapter 2 5 Literature Review 2. Green Sand Composition Green sand casting is a low cost way to produce many of the engineered iron casting need in today's modern equipment and machinery. Green sands in foundry are usually mixtures of 80-85% silica sand, 8-0% bentonite clay, 3.5-6% seacoal, 3-4% water and other minor compositions [4]. Silica sand is widely used in most foundries because its low production cost, adequate hardness and abrasion resistance. Silica sand has a wide range of grain size, shape and size distributions to meet most foundries needs. All affect the properties of molds [5]. Round grain sand has good flowability but round grains reduce the interlocking strength between grains. Angular grain sand has sharp edges and corners which may break off during processing cycles creating fines during handling. Sub-angular sand is angular sand with smoothed edges which will not create many fines during processing. Most foundries use green sands that are sub-angular. There are two major types of bentonite clays that foundries use today: Western (sodium) bentonite clays increase the hot compression strength and dry strength of the sand. During recycling, less new clay is needed because of its high durability upon thermal cycling. Southern (calcium) bentonite improves the green sand compression strength as well as the rammability of molds. Its lower dry compression strength and hot compression strength make shakeout easier [5]. The clay activation during mulling is important to develop green sand properties, this will be discussed in the following chapter. The clay in sand mixture is activated by moisture during mulling. It develops the required plasticity to bond and hold sand grains together after compaction. Water in molding sand has two forms: Combined water is mixed and absorbed in the clay platelets or is absorbed by the other additives. Free water is not driven into clay structure during mixing and will evaporate readily as

15 the mold is poured. Water affects most properties of mulled green sand mixtures, excess free 6 water results in poor permeability, high dry strength, high hot compression strength, low flowability, low mold hardness and excessive gas evolution [5]. The amount of water needed for acceptable procedures in a green sand is a strong function of clay content. Seacoal is added to sand in order to counteract sand expansion mold cracking due to carbon expansion. It also helps to improve the surface finish and casting cleaning after shakeout. Seacoal burns at mold/metal interface consuming the oxygen producing both a reducing atmosphere and coating the mold surface with lustrous carbon when coal level are too high. Coal levels in green sand are measured in the sand lab by running the 200 volatile test and the 800 combustible test (loss on ignition or LOI). 2.2 Green Sand Molding Cycle A typical green sand cycle in a foundry consists of the following steps: molding, assembling and closing, pouring, cooling, shakeout, metal and refuse removal, cooling, storage, mulling with water, adding bentonite and seacoal or cereal and returning the green sand to molding [6]. Figure shows a typical green sand metal casting process flow. First of all, the mixing of sand, clay and water to form a moldable substance is performed by a muller or mixer. When molten metal is poured into a mold made of green sand, the heat creates free water and some of the water integrated with the clay evaporates. The clay near the casting surface becomes calcined dead clay and cannot be reactivated with new water addition. During casting the molten metal is solidified and cooled in the green sand mold. After the casting is removed from the mold by shakeout, it is cleaned and machined to satisfy customer surface finish requirements. After shakeout, the hot sand is cooled, then new coarse sand, water and clay are added to the used sand. A new sand use cycle begins with mulling to make distribute and coat the clay/water mixture around sand grains

16 [7]. Excess sand and fines are removed from sand system prior to mulling to keep it in balance. 7 The exact amounts of additions to the sand system at muller are based on clay burnout calculations and sand lab tests. Sand tests, such as Methylene Blue clay, compactibiliy, green compressive strength, dry strength, permeability, etc. are performed on the prepared molding sand. The subsequent muller additions are continually adjusted to maintain the system consistency and uniformity based on the results of these tests. Figure General Metal Casting Process Flow [8] 2.3 Why Use Energy Dose to Control Batch Muller Instead of Time Figure 2 shows a typical mulling energy horsepower (HP) curve of batch muller for one mulling cycle. In order to get detailed mulling energy variation, the mulling energy dose is logged 0 times per second so we can understand this mulling cycles which typically range from seconds in most foundries. The muller sand weight at Neenah Foundry varies around 5700 lbs

17 from batch to batch. The sand mixture is sent into the muller and the mulling cycle starts with 8 rapidly increasing energy input. The mixture keeps homogenizing and developing strength with increasing of mulling energy dose input. The energy curve tends to stabilize but still is slightly wavy when sand is well coated with clay and water. Energy input drops significantly when the whole mulling cycle ends and the mixture is discharged from the muller door. Then the next mulling cycle begins following the similar trend of mulling energy dose change during the whole cycle. Figure 2 A Typical Mulling Energy HP Curve Conventional sand wisdom suggests that most foundries under-mull their sands which leads to a poor distribution of ingredients and inconsistent sand []. As we all know, properties of molding sands are influenced by mulling time. Modern high efficiency mullers develop sand properties quickly during mulling. However there is batch-to-batch variation in the necessary mulling time needed to mull a batch of sand because the return sand can have considerable variability. It is believed that decreasing mulling time will cause casting quality issues [9]. New coarse sand when added to the muller will require longer mulling times to develop sand properties whereas a used returned system sand may not react the same way as new prepared sand. The uncontrolled

18 variations between each batch not only occurs in new prepared sand, but also in reused sand. 9 Because of these variations in clay activation conditions as well as variations in mulling intensity as mullers wear and get out of adjustment, it is difficult to achieve consistent mulling from batch to batch over time [0]. A fixed mulling cycle time from batch to batch will result in variable sand properties as well as variable resultant casting quality. If we can appropriately adjust mulling time for each batch, we can tighten the variation in sand performance from batch to batch. A successful green sand system is the one that delivers consistently mulled sand to the molding machines. Because of variation in the incoming sand to the muller, controlling a batch muller by fixed cycle time (Mulling to Time) actually results in significant variations in the mulling energy provided to each batch. Figure 3 shows that the energy dose of every batch varies significantly under Mulling to Time control. The weight of sand in each muller batch is strictly controlled for each of three batches, the degree the energy variation therefore causes variations represent time difference in the amount of mulling provided to each sand grain. Energy dose per batch varies by more than 30% from batch to batch. It is hard to tell if a single batches is undermulled or over-mulled in terms of the mulling energy applied in the muller. This makes us turn to the thought using energy dose to control mulling rather than mulling time, which is Mulling to Energy (MtE).

19 0 Figure 3 Mulling Energy Variation under MtT Control Mulling to Energy has been implemented at one plant in Neenah Foundry by installing a true power meter to monitor the energy in horsepower (hp) during the each mulling cycle and calculating the cumulative hp-second values in order to capture the amount of total energy dose applied to each entire batch during mulling. Each mulling cycle is terminated when the cumulative hp-second value arrives at a prescribed energy dose setting. 2.4 Standard Sand Test Variables and Casting Scrap Different foundries may use different sand test variables to control green sand system performance. Some of the data are tested directly in sand lab at specific frequencies, others are calculated for measured values sand control. Table gives the green sand variables and testing frequency used by Neenah Foundry in their sand lab.

20 Table Sand Test Variables and Test Frequency Sand Test Frequency of Test AFS Test Ref Compactability (Sand Lab) 2 times a day S Moisture Rapid Test (Sand Lab) 2 times a day S Methylene Blue (Sand Lab) 2 times a day S S Green Compression Strength 3 times a day S (Sand Lab) Density (Sand Lab) 2 times a day Split Tensile Strength (Sand Lab) 2 times a day S Permeability (Sand Lab) 2 times a day S Dry Comp. (Sand Lab) times a day S LOI at 800 (Sand Lab) time a day S VCM at 200 (Sand Lab) time a day S Compactability measures the decrease in volume of a sand bonded with clay and water after it is squeezed by a fixed load. Each sample is rammed three times, and the percent reduction in volume is measured with a compactability scale as per AFS procedure S. The compactability drops as sand travels from the muller to the molding machine due to the water evaporation and continuing absorption of water into the interior of the clay platelets. These compactability differences increase with time, temperature of sand, clay addition and decrease with increased degree of mulling [3]. Density is the most basic sand property as it influenced by practically all other sand properties. The test of density comes directly from the compactability test result. The weight of sand from the reading of a previous density test is placed in a standard specimen tube by a pedestal cup.

21 After ramming 3 times, the density of the rammed sand is read on a scale. If the reading is too 2 low, a new sample is rammed with larger weight until the reading is on-scale equals. The density of a rammed specimen decreases as the increase of degree of mulling []. Permeability is the measurement of how easy it is for air, gases, or steam to pass through a rammed sand sample. The openings between the sand grains in a mold give sand its permeability. Permeability is expressed as a number that increases with an increasing openness of the sand as per AFS procedure S. The degree of mulling also influences permeability because it affects the distribution of the clay and additives on the sand grains. Usually, the higher the degree of mulling, the higher the permeability of the sand. Low permeability produces a smoother casting surface finish because the voids between the sand grains are smaller. Low permeability, however, increases the likelihood of problems with blows, pinholes and other gas-related defects. Low-permeability sands also can produce expansion defects if the permeability is low as a result of high packing density of the sand grains. Green compression strength refers to the stress required to rupture the sand specimen under compressive loading. Green compression strength testing is the key property need to characterize green sand strength. In this test, a rammed specimen is loaded in compression and the maximum load to failure is recorded as per AFS procedure S. Green compression strength plays an important role in determination of mulling efficiency and estimation of the mulling energy required for each batch. Fines, wettability of sand grains, sand grain distribution and other factors will all play a role in how the green sand s strength develops during mulling. If green compressive strength is low, the sand will have good flowability and the cost to maintain the sand system will be lower. If green compression strength is too low, however, broken molds and poor draws will become a problem. Low green strength indicates low clay content and/or poor mulling. If the green compressive strength is too high, the molds will be stronger, shakeout

22 3 will be difficult, poor casting dimensions, poor flowability and high ramming resistance are likely problems. Also, the cost to maintain the system will be higher due to use of excessive bond. Split Tensile testing is perhaps a more realistic measure of green sand strength for estimating mold performance as per AFS procedure S. In the splitting strength test, the AFS standard specimen is loaded across its diameter. When the specimen fails, it actually fails in tension. Most mold failures, such as in pattern stripping, are actually tensile or shear failures rather than compressive failures. Splitting strength tests relate to the degree of mulling. The ratio of split strength to green strength increases as the degree of mulling increases [4]. Moisture content is measured as the mass percentage of total water in green sand. This test is simply a quantitative measure of the amount of water in sand which is measured by placing a sample of the molding sand in a pan and drying it at as per AFS procedure S. Within this temperature range, constant sample weight will be reached in about five minutes, without loss of volatile organic material such as seacoal [5]. The weight loss upon drying is used to calculate the percent moisture. The Methylene Blue Test is conducted to determine the amount of active clay present in a green sand as per AFS procedure S and AFS procedure S. This test is not a direct measure of clay content but it is very useful tool to estimate the amount of active clay. The test is based on the principle that cationic methylene blue dyes will exhibit a base exchange in proportion to the active clay content in a dispersed clay suspension. The number of exchangeable ions present is determined by replacing them with a methylene blue dye. This is a critical test since not only is the value of MB clay used directly for sand system control, it is also the premier value in the determination of other calculated values of sand system performance. Other sand tests such as dry compression strength, LOI at 800 and VCM at 200 are also significant sand lab test but will not be discussed fully here since they do not directly impact the mulling characteristics of green sands. Further descriptions of the sand tests are given in

23 54] and summarized by Land [4], that are used by foundries to control green sand system. Table 2 Summary of Empirical Parameters Developed by Heine et al. [6-54], as summarizes by Land [4] Directly Measured Test Properties:. Methylene Blue (%MB) 2. Test Moisture (%TM) 3. Test Compactability (%TC) 4. Test Green Strength (GCS, psi) Reference Properties: 5. Available Clay or Available Bond (%AC, %AB) 6. Effective Clay or Working Bond (%EC, WkB) 7. Moisture Green Strength (%MGS) %AC=0.05 GCS+(.36 %TM) Percent clay based on %TC and GCS curves developed from new clay and silica sands. Estimates the %clay in the sand that is available for developing molding properties for a given %TM and GCS. %EC=(5.29 GCS)/(32.-%TC) Percent clay based on %TC and GCS curves developed from new clay and silica sands. Estimates the actual %clay in the sand that is producing the molding properties for a given %TC and GCS. 4 Appendix A. There are also empirical sand system performance factors developed by Hennie [6- %MGS=(4.52 0^(/ log(%tm) log(gcs)))+4.075)/6 Percent clay that is based on an extension of the %AC equation to include a wider % clay range, seacoal additions, and improved mulling. Estimates the %clay in the sand that is available for developing molding properties from a given %TM and GCS. (equivalent to available clay) 8. Compacted Green strength (%CGS) %CGS=[(GCS %TC)/( %TC)] 00 Percent clay that is based on an extension of the %EC equation to include wider % clay range, seacoal additions, and improved mulling. Estimates the actual %clay in the sand that is producing the molding properties from a given %TC and GCS. (equivalent to effective clay)

24 9. Equilibrium Moisture (%EM) 5 7<%MB< and %TC>47.5: %EM=%MB/( %TC) 7<%MB< and %TC<47.5: %EM=%MB/( %TC) The moisture in fully processed sand at a specific %TC. In fully processed sand, %TM=%EM 0. Equilibrium Compactability (%EC) 7<%MB< and %TC>47.5: %EC=(%MB/%TC -3.23)/( ) 7<%MB< and %TC<47.5: %EC=(%MB/%TC )/( ) The compactability of a fully processed new sand at a specific %TC and %MB.. Equilibrium Clay- Water Ratio (ER) ER=%MB/%EM %TC<47.5: ER= %EC %TC>47.5: ER= %EC The ratio required to produce a specific %TC for a fully processed new sand at a specific %MB and %TC. A good measure of the availability of moisture for producing compactability. 2. Equilibrium green Strength (EGS) 7<%MB< and 30<%EC<60: EGS=224.5/(%TM log%ec)+(4.3 %MB-33.). 7<%MB< and 30>%EC: EGS=( %MB)/(%TM log%ec)+(2 %MB-2) 4<%MB<6.5and 30<%EC<50: EGS=(2.76 %MB-3.5).333 [/(%TM log%ec)]+( %MB) 4<%MB<6.5and 50<%EC: EGS=(2.76 %MB-3.5).333 [/(%TM log%ec)]+( %MB).6 4<%MB<6.5and %EC<30: EGS=(2.76 %MB-3.5).333 [/(%TM log%ec)]+( %MB) Strength of a fully processed new sand at a specific %MB, %TM, and %EC.

25 3. Test Ratio (TR) TR=%MB/%TM 6 The tested clay-moisture ratio. 4. Moisture Compactability Clay (%MC) %MB>6.8 and %TC<47.5: %MC=%TM ( %TC) %MB>6.8 and %TC>47.5: %MC=%TM ( %TC) Calculated Parameters: 5. Mulling Efficiency (%ME) 6. System (Strength) Processing Efficiency (%SPE) 7. Compactability Efficiency (%CE) 8. Green Strength Efficiency (%GSE) 9. Moisture Index (%MI) 20. Compactability Index (%CI) %ME=%EC/%AC or- %ME=%CGS/%MGS Percent efficiency based on the extent of clay activation as defined by %EC or %CGS approaching %AC or %MGS. Measures the extent to which the properties of the processed sand approach the equilibrium properties of fully processed new sand. %SPE=[%MGS/%MB] 00 Percent efficiency based on the extent of clay activation as defined by %MGS approaching %MB. Measures the extent to which the properties of the processed sand approach the equilibrium properties of fully processed new sand. %CE=[%TC/%EC] 00 Percent efficiency based on the extent of compactability development (activation) as defined by %TC approaching %EC. Measures the extent to which the properties of the processed sand approach the equilibrium properties of fully processed sand. %GSE=[GCS/EGS] 00 Percent efficiency based on extent of green strength development (activation) as defined by GCS approaching EGS. Measures the extent to which the properties of the processed sand approach the equilibrium properties of fully processed sand. %MI=[ER/TR] 00 or- %MI=[%TM/EM] 00 An index to describe the moisture of the sand at any given compactability as: moisture starved (MI>88%), moisture deficient (MI<00%), or moisture saturated (MI=00%) sands. Compares the %TC to the moisture of a fully processed new sand mixture of equal compactability. %CI=[%TC/%EC] 00 Compares the %TC with the compactability of fully processed new sand mixtures of equal percent moisture. Similar to moisture index.

26 2. Equilibrium Clay Parameter (ECP) ECP=%TM log%ec EGS 4<%MB<6.63: ECP=50.2 %MB <%MB<9.0: %MB>9.0: ECP=22.6 %MB+46 ECP=27.85 %MB-.25 Measure of the extent to which the clay has been activated by moisture and processing. 22. Test Clay Parameter (TCP) TCP=%TM log%tc GCS Test parameter, related to the amount of %MB clay activated 23. Available Methylene Blue (%AMB) 4<%MB<6.63: 6.63<%MB<9.0: %MB>9.0: %AMB=(TCP+37)/50.2 %AMB=(TCP+46)/22.6 %AMB=(TCP+.25)/27.85 Closely related to %MGS but is a better measure of clay activation because it includes %TC in its calculation (see TCP). Will always be lower than %MB, except in fully processed sand. 24. Effective Methylene Blue (%EMB) 25. Clay Processing Efficiency (%CPE) 40: Clay/Cereal Ratio: %EMB=0.933 %MB % Cereal: %EMB=0.667 %MB Percent clay corrected for the effect of cereal on %MB %CPE=[%AMB/%MB] 00 An improved efficiency based on a measure of clay activation by %AMB. A possible improvement on %SPE. The Mulling Efficiency of the mulled green sand is a good indicator to determine whether mulling delivers sand with expected sand mix properties. To obtain mulling efficiency, the effective clay value is divided by the available clay value, and expressed in percent. Effective clay (EC) is derived from green compression strength (GCS) and test compactability (TC). It indicates the actual amount of clay that is producing the bonding strength. The EC is estimated based on the MB clay value for a fully mulled sand at that compactability. Available Clay (AC) is calculated from green compression strength (GCS) and the moisture present (TM) to develop green compression strength. It indicates the presence of moisture absorbing materials, including

27 active clay. The AC value is the MB clay value when completed mulled. AC estimation is 8 affected by the moisture absorbing materials in the system as well as the amount of MB clay. The higher the mulling efficiency, the greater the clay utilization. It should be noted here that 00% mulling efficiency is not desirable or achievable in production green sand system. Mulling Efficiency can also be expressed as a ratio of Compacted Green Strength to Moisture Green Strength. Table 3 Muller Efficiency Calculations (%ME) [] %ME=%EC/%AC or- %ME=%CGS/%MGS Effective Clay or Working Bond (%EC, WkB) = %EC= (5.29 GCS)/(32.-%TC) Compacted Green strength (%CGS) = %CGS=[(GCS %TC)/( %TC)] 00 Test Moisture (%TM) Green Compressive Strength (GCS) Available Clay or Available Bond (%AC, %AB) = %AC= 0.05 GCS+(.36 %TM) Moisture Green Strength (%MGS) = %MGS=(4.52 0^(/ log(%tm) log(gcs)))+4.075)/6 Methylene Blue (%MB) As seen from the previous equations, effective mulling is not a direct function of time. Factors influencing green sand performance can be seen from these equations. The variation in the amount of inactivated clay and the percent of water will all affect how much mulling energy is needed for a specific batch. Green compression strength has an important effect on the mulling

28 energy required per batch so that factors impacting green compression strength should also be 9 considered when analyzing how green compression strength changes during mulling. Sand properties can strongly impact casting quality as well. The risk of scrap occurrence may happen in every step of metal casting. A defect-free casting needs well-designed pattern, core, gating system, properly prepared mold and correct melting metal, etc. Green sand properties are not the only reason for casting scrap. Defects may also be the result of improper pattern and gating system design, improper mold and core construction, improper melting and pouring practice, inadequate melting and pouring temperature, improper action on system control, and degree of mulling. Because mold performance is significant to casting scrap, most foundries separately evaluate sand-related scrap from other causes of scrap. In this thesis, we only analyze sand related scrap. Casting defects and their relationships to sand characteristics have been summarized by the American Foundrymen s Society [55] and are summarized in Appendix B. The most common defects are shown here. Crush: Indentations on the casting surface caused by the disruption of the mold surface due to external or internal forces or weight during mold closing. Cut: Rough spots and areas of excess metal caused by erosion of the mold or core surface by metal flow during mold pouring. Drop: Defect due to the fracture and dropping of a portion of sand from the cope or other overhanging portion. Erosion Scab: Expansion defect in which the loosened sand eroded away by the motion of metal results in a solid junction between the casting and the defect. Expansion Defect: Defects due to the expansion and contraction of the sand mold surfaces during the pouring of a casting. This includes expansion scabs, buckles, rat-tails, blackening scabs, pull downs (cope spalls). Fin: Defect due to a crack in the sand mold.

29 20 Fusion: Surface defect having a rough glossy appearance related to the penetration of the metal oxides acting as a flux on the sand. Gas Defect: Defect due to localized gas pressure that exceeds the metal pressure in any locality during solidification. This type of defect includes blows (gas holes), pinholes, blisters, body scars, and porosity. Metal Penetration: Condition in which the metal or metallic oxides have filled the voids between the sand grains without displacing them. Push-Up: Indentation in the casting surface caused by disruption of the mold surface due to external or internal force or weight Rough Surface: Defect in which the castings lacks the required degree of smoothness for a specific application. Sag: Increase or decrease in a metal section due to the sagging of the cope (decreased section) or core (increased section). Scar: Minor or shallow mark on a casting surface where the casting does not conform to the pattern by reason of mold-wall movement or gas entrapment. Sticker: Excess metal on the surface of the casting caused by a portion of the mold face remaining on the pattern during molding. Swell: Enlarged metal section related to mold-wall movement. Vein: Defect due to a crack in the sand mold. Warping: Undesirable or unintentional casting deformation that occurs during or after solidification. Wash: Rough spots and areas of excess metal caused by erosion of the mold or core surface by metal flow. Neenah Foundry can track 5 different forms of sand related casting scrap. This data can be used to analyze how MtE implementation impacts casting quality.

30 Chapter 3 2 Analysis Procedure Our analysis was divided into two parts: Mulling to Energy validation and Mulling to Asymptote development. To validate Mulling to Energy control, we checked before and after MtE sand property data, sand related scrap data and batch-to-batch mulling time data to decrease sand system changes brought by Mulling to Energy implementation. To analyze sand properties data, we checked normality, stability and correlation to previous test were evaluated conclude the sand properties data were highly correlated and not normal, we will fit these original data into ARIMA model and analyze the transferred data in the future. So far we used non-parametric 2-sample t test and Levene s test to compare mean and standard deviation of sand properties data. 2- sample t test and Levene s test were also used to test sand related scrap and mulling time change before and after MtE implementation. To develop Mulling to Asymptote control methods, 30 mulling energy HP curves of batch mulling cycle were evaluated and an algorithm to control batch muller by various mulling energy dose for every batch was developed. 3. Normality and Stability Checks In the real word, data may be incomplete with lacking attribute values and certain attributes of interests, containing only aggregate data, it may be noisy because of the errors and outliers. The major tasks in data preprocessing normally include data cleaning, data integration, data transformation, data reduction and data discretization. The data to be preprocessed and analyzed in this study were the sand lab test data. The sand lab test data was recorded manually and was typed into an Infinity QS system by hand. Typing errors were the main reason for outliers. For example, some data was missing a decimal point which caused factor of ten outliers.

31 An I-MR chart is an Individuals chart and Moving Range chart shown in the same graph. The 22 Individuals chart is typically displayed in the upper half of a plot whereas the Moving Range chart is displayed in the lower half. Both I and MR charts were compared together to track both the process levels and process variations simultaneously to determine any possible causes of unnormal data at the same time. The Anderson-Darling statistic test was used to measure how well the data follow a particular distribution. The better the distribution fits the data, the smaller this statistic will be. This test compares the empirical cumulative distribution function of sample data with the distribution expected if the data are normal. If this observed difference is sufficiently large, the test will reject the null hypothesis of population normality. The hypotheses used for the Anderson-Darling test are: H 0 : The data follow a specified distribution H : The data do not follow a specified distribution If the p-value for the Anderson-Darling test are lower than the chosen significance level (0.05), it can be concluded that the data does not follow the specified distribution. The detailed test results will be discussed in Chapter Check Correlation Because of the fact that green sand is continuously recycled, it is expected that the sand has memory and the reported sand data is autocorrelated. The degree of autocorrelation is dependent on many factors such as type of process control, sand system turnover time, the sampling schedule, sand lab test procedures, etc. Batch mulling can be expected to have less autocorrelation than continuous muller systems used in more foundries. Importantly, the compactability value from the previous batches is used by the sand system compactability

32 23 controller to determine water addition for subsequent batches. This control strategy results in high autocorrelation. A shorter sand turnover time leads to a decrease in autocorrelation whereas a shorter time period between two test samples affects the sand lab test result and leads to strong autocorrelation. Some sand lab procedures such as the Density Test and Methylene Blue may also have autocorrelation. As mentioned about the sand property test procedure in Chapter 2. Autocorrelation is the linear dependence of a variable with itself at two observations in time. Autocorrelation between any two observations depends on the length of time leg between them. Assuming there are t observations: y, y 2,..., y t, partial autocorrelation is the correlation between y t and yt h after removing any linear dependence on y, y 2,..., yt h [56]. The autocorrelation functions (ACF) are a set of correlation coefficients between the series and lags of itself over time whereas the partial autocorrelation function (PACF) are the partial correlation coefficients between the series and lags of itself overtime. They are used to determine if the variables are independent and stationary or not. Table 4 summarizes how one can use the sample autocorrelation function for model identification. Table 4 Models Indicated by the Shape of the Autocorrelation Function [57] Shape Exponential, decaying to zero Alternating positive and negative, decaying to zero One or more spikes, rest are essentially zero Indicated Model Autoregressive model. Use the partial autocorrelation plot to identify the order of the autoregressive model. Autoregressive model. Use the partial autocorrelation plot to help identify the order. Moving average model, order identified by where the plot goes to zero. Decay, starting after a few lags Mixed autoregressive and moving average (ARMA) model.

33 All zero or close to zero Data are essentially random. 24 High values at fixed intervals Include seasonal autoregressive term. No decay to zero Series is not stationary. By preprocessing sand properties data, there tests can determine if most of sand properties data are not stable, not random or fail to pass a population normality test sample t Test To compare the sand properties means before and after MtE implementation, the 2-sample t test was used. 2-sample t test relies on the assumption of independence which means the observations from the first sample must not have any relation with the observations from the second sample. The null hypothesis is to assume that the difference between the population means ( 2) equals the hypothesized difference ( 0 ) whereas the alternative hypothesis assumes the difference between the population means ( 2) does not equal the hypothesized difference ( 0 ). A significance level of 95% means that given a p-value of less than 0.05, we can be more than 95% certain that the change is true. 3.4 Levene s Test In the independent 2-sample t test, independence, normality and equal variances are assumed, but it may be difficult to determine whether the equal variance assumption is appropriate. Under normality assumptions, it is expected to compare 2 and 2 2 using 2 S and S 2 2, but such tests are

34 highly sensitive to nonnormality so they must be avoided in this analysis since most of the 25 collected data were not normally distributed. Levene s test is a nonparametric test which is used to test if samples have equal variances. The main idea of Levene s test is to test equal means using transferred data instead of testing equal variances using the original data. Equal variances across samples is called homogeneity of variance or homoscedasticity [58]. A homogeneity of variance test is less dependent on the assumption of normality than other tests, while still assuming independence. By preprocessing data, it was found that the two data sets are not normal but they were independent to each other because they were from totally two different time periods which conforms to the assumption of Levene s test. To perform Levene s test in Minitab, the null hypothesis is to assume the ratio of standard deviation of two datasets is which means the two datasets have same standard deviation. The Alternative hypothesis is to assume the ratio of standard deviation of two datasets is not equal to which means the two datasets have different standard deviations. In this case, the significance level is Mulling Analysis Mulling time data was collected from compactability controller system data by batch. We calculated average, maximum and minimum mulling time of about 200 batches every day during data collection period. We did two similar daily batch-to-batch average mulling time comparison in this analysis: ) before and after Mulling to Energy implementation and 2) before and after Mulling to Asymptote implementation. We used 2-sample t test to check mean and Leven s test to check the standard deviation of daily batch-to-batch average mulling time.

35 26 Mulling energy setpoint data was also collected from compactability controller system data by batch. The energy usage measurement of Mulling to Asymptote was made over an 8-week period by an outside firm, We Energies. In this 8-week period, this production line changed the mulling control method from controlling by fixed mulling time to controlling by mulling asymptote. There was a two week period for system to stabilize after installing an energy meter on the production line. After stabilization, Mulling to Asymptote control was implemented on this production line. 3.6 Mulling to Asymptote Development By carefully observing HP mulling curves from the start to the end of mulling cycles, additional strategies for reducing mulling time level on MtE concepts can be evaluated. Based on observations of a typical mulling HP curve (Figure 4), it appears that there are two distinct portions to the mulling curve. The area when the mixture is homogenizing and developing strength, and the point at which this increasing HP stops for a given batch and reaches an asymptote with some fluctuant waves. Mulling beyond this point does not seem to provide any increase in properties. Validating the results of mulling to energy asymptote which is called mulling to asymptote (MtA) will be part of future work.

36 27 Figure 4 Variation in Energy Dose for a Mulling Cycle Controlled by Fixed Mulling Time The main idea of Mulling to Asymptote control is to find a maximum energy dose in a prescribed period and observed for several seconds to make sure if the energy dose is still the largest in this time period then the sand could be dumped from muller. A MtA mulling control algorithm has been developed based on the following sand control:. hp energy dose is sampled 0 times per second. 2. hp-sec value is calculated every second. 3. A hp-sec control minimum is selected based on previous batch data gathered. At Neenah Foundry a 5000 hp-sec minimum energy does is used. 4. The size of stack is 30, it follows first in first come policy. The hp-sec minimum and size of stack could be changed based on the muller type and level of safety guard. The logic of algorithm is shown in Figure 5:

37 28 Figure 5 Logic of Mulling to Asymptote Control Algorithm The logic is triggered when integral hp-sec reading reaches a specific hp-sec minimum setting, which is assumed 5000 here. If the hp reading is larger than the maximum hp reading in the previous second which is 0 data logging, the count will be always equal to zero until the HP reading is less than the maximum HP reading in the previous second (0 data logging points), the count will be triggered to automatically change from 0 to. This logic loop will be recycled until the incremental count reaches an assumed length of time which is 3 seconds or 30 counts. After that the sand will be discharged and a whole mulling cycle controlled by Mulling to Asymptote ends. An appropriate MtA algorithm was written in PLC language and was implemented at one molding line in Neenah Foundry in late 205. It was expected that MtA should become an even more effective than MtE to impact mulling control in green sand foundries using batch mullers.

38 Chapter 4 29 Results 4. Sand Properties Analysis Result 4.. I-MR charts and Normality Test Figures 6-3 indicate the analysis results using I-MR chart and running Aderson-Daring test foe key sand test properties before and after MtE implementation. These results are summarized in Table 5. By preprocessing sand properties data, it is concluded that the original compactability data failed both the normality test and the stability tests. MtE does not reduce the standard deviation of compactibility data significantly, however mean changes occured. Before and after the MtE Methylene Blue data both fail the normality and stability test. But the standard deviation of After MtE MB data was reduced significantly as well as the mean. This same trend was observed on other before and after implementation MtE sand properties such as density, moisture and split tensile. They were all abnormal and unstable. The out of control points and root cause of the unstable variation still needs to be investigated. TM/MB Clay, permeability and green compression strength partially passed the before/after capability analysis. However, the sand properties test frequency of permeability and green compression strength testing was less than the other property measures for the number of observation less than 00. There was not enough data to obtain reasonably precise estimates. The precision of the estimates decreases as the sample size becomes smaller. Diagnostic reports indicate that most of the before and after sand properties data failed in the normality test. Robust capability estimates based on Mulling to Energy process changes require additional data.

39 30 Before: Compact After: Compact 2 Before/After Capability Comparison for Compact vs Compact 2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Before After Individual Value Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Fail Fail P-value < < Figure 6 Before/After Capability Comparison Diagnostic Report for Compactability Before: Meth Blue After: Meth Blue 2 Before/After Capability Comparison for Meth Blue vs Meth Blue 2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Before After Individual Value 0 9 Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Fail Fail P-value < < Figure 7 Before/After Capability Comparison Diagnostic Report for Meth Blue

40 3 Before: Density After: Density 2 Before/After Capability Comparison for Density vs Density 2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Individual Value Before After 4 Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Fail Fail P-value < < Figure 8 Before/After Capability Comparison Diagnostic Report for Density Individual Value Before: Split Tens_ After: Split Tens_ Before/After Capability Comparison for Split Tens_ vs Split Tens_2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Before After Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Fail Fail P-value < Figure 9 Before/After Capability Comparison Diagnostic Report for Split Tensile Strength

41 32 Before: Moisture After: Moisture 2 Before/After Capability Comparison for Moisture vs Moisture 2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Before After Individual Value Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Fail Fail P-value < < Figure 0 Before/After Capability Comparison Diagnostic Report for Present Moisture Before: TM/MB Clay After: TM/MB Clay 2 Before/After Capability Comparison for TM/MB Clay vs TM/MB Clay 2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Before After Individual Value Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Pass Fail P-value < Figure Before/After Capability Comparison Diagnostic Report for TM/MB Clay

42 33 Before: Green Comp. After: Green Comp 2 Before/After Capability Comparison for Green Comp. vs Green Comp 2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Before After Individual Value Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Pass Fail P-value Figure 2 Before/After Capability Comparison Diagnostic Report for Green Compression Strength Before: Perm After: Perm 2 Before/After Capability Comparison for Perm vs Perm 2 Diagnostic Report I-MR Charts Confirm that the Before and After process conditions are stable. Before After Individual Value Moving Range Before Normality Plots The points should be close to the line. After Normality Test (Anderson-Darling) Before After Results Pass Pass P-value Figure 3 Before/After Capability Comparison Diagnostic Report for Permeability

43 34 Table 5 Before/After Capacity Analysis Summary Comparing Sand Properties before and after MtE Implementation Compactability Meth Blue Density Split Tensile Moisture TM/MB Clay Green Comp Permeability Stability Normality Before Fail Fail After Fail Fail Before Fail Fail After Fail Fail Before Fail Fail After Fail Fail Before Fail Fail After Fail Fail Before Fail Fail After Fail Fail Before Fail Pass After Fail Fail Before Fail Pass After Pass Fail Before Pass Pass After Fail Pass 4..2 Auto Correlation and Partial Auto Correlation Figures 4-29 show the ACF and PACF plots of sand properties tested before and after MtE implementation. After fitting the autocorrelation function shape into the Table 4, for the sand properties which was sampled 2 times a day, it was found that they are all highly correlated with the result of previous test which was taken 2 hours before. An autoregressive characteristic is predominant with some seasonal autoregression. For the properties which were sampled 2 or 3 times per day, such as green compression strength and permeability, as the sampling frequency decreased, these properties showed less correlation. But most of the properties are not stationary or random.

44 35 Autocorrelation Function for Compact after MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Compact after MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 4 ACF PACF Plots of Compactability after MtE Autocorrelation Function for Compact before MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Compact before MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 5 ACF PACF Plots of Compactability before MtE Autocorrelation Function for Density after MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Density after MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 6 ACF PACF Plots of Density after MtE

45 36 Autocorrelation Function for Density before MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Density before MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 7 ACF PACF Plots of Density before MtE Autocorrelation Autocorrelation Function for Split Tensile after MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Split Tensile after MtE (with 5% significance limits for the partial autocorrelations) Partial Autocorrelation Lag Lag Figure 8 ACF PACF Plots of Split Tensile Strength after MtE Autocorrelation Autocorrelation Function for Split Tensile before MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Split Tensile before MtE (with 5% significance limits for the partial autocorrelations) Partial Autocorrelation Lag Lag Figure 9 ACF PACF Plots of Split Tensile Strength before MtE

46 37 Autocorrelation Function for Moisture after MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Moisture after MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 20 ACF PACF Plots of Present Moisture after MtE Autocorrelation Function for Moisture before MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Moisture before MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 2 ACF PACF Plots of Present Moisture before MtE Autocorrelation Autocorrelation Function for Meth Blue after MtE (with 5% significance limits for the autocorrelations) Lag Partial Autocorrelation Partial Autocorrelation Function for Meth Blue after MtE (with 5% significance limits for the partial autocorrelations) Lag Figure 22 ACF PACF Plots of Methylene Blue Clay after MtE

47 38 Autocorrelation Function for Meth Blue before MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Meth Blue before MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 23 ACF PACF Plots of Methylene Blue Clay before MtE Autocorrelation Function for TM/MB Clay after MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for TM/MB Clay after MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 24 ACF PACF Plots of TM/MB Clay after MtE Autocorrelation Autocorrelation Function for TM/MB Clay before MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Partial Autocorrelation Function for TM/MB Clay before MtE (with 5% significance limits for the partial autocorrelations) Lag Lag Figure 25 ACF PACF Plots of TM/MB Clay before MtE

48 39 Autocorrelation Function for Perm after MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Perm after MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 26 ACF PACF Plots of Permeability after MtE Autocorrelation Function for Perm before MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Perm before MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 27 ACF PACF Plots of Permeability before MtE Autocorrelation Function for Green Comp after MtE (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Green Comp after MtE (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 28 ACF PACF Plots of Green Compression Strength after MtE

49 40 Autocorrelation Function for Green Comp. (with 5% significance limits for the autocorrelations) Partial Autocorrelation Function for Green Comp. (with 5% significance limits for the partial autocorrelations) Autocorrelation Partial Autocorrelation Lag Lag Figure 29 ACF PACF Plots of Green Compression Strength before MtE Sample t Test Table 6 shows the results of 2-sample t test before and after ME sand properties mean values. Based on the test result, all p value were less than the significance level of 0.05, which suggests the mean changed after MtE. Knowing that the data are highly correlated and observing the percent of change, it is important to understand the other changes during the data collection period which might impact the results. For example, as discussed in Chapter, the monitored variables from sand lab test results were used to make long-term decisions on target compactability. Clay Moisture is the primary control parameter for sand systems at Neenah Foundry. The water addition for a batch of sand is influenced by the target compactability from the previous batch. Clay Moisture changes are used to adjust compactability along the way. During the after MtE data collection period, controller compactability changes were made to intentionally change the clay moisture levels. This contributed to the wider mean range of some of the important sand properties. Table 6 Sand Properties 2-Sample t Test Result Mean Mean Change% P Value (Before MtE) (After MtE)

50 Green Strength(psi) % Compactability % 0 Density(g) % Permeability % 0 Split Tensile(psi) % 0 Moisture% % 0.00 M B Clay% % 0 TM/MB* % Levene s Test Table 7 indicates the results of Levene s test before and after ME sand properties standard deviations. The results of Levene s Test again prove the conclusions of the I-MR charts: After controlling the mulling cycle time by energy dose per batch, for all properties with p values of less than 0.05, the standard deviation of after MtE sand properties changed significantly compared with that of before MtE. Most sand properties had less standard deviation, which means that the mulling became more consistent after MtE implementation. Compactability is a control variable which could be adjusted based on the sand system performance instead of a response of sand test. We should interpret the compactability change by taking the system control changes during MtE implementation (APPENDIX C) into account carefully. Table 7 Sand Properties Levene's Test Result StdDev (Before MtE) StdDev (After MtE) Change% P Value Green Strength(psi) % 0.895

51 Compactability % 0 42 Density(g) % Permeability % 0.9 Split Tensile(psi) % 0 Moisture% % 0 M B Clay% % 0 Moisture/Clay* % Sand Scrap Data Analysis The sand scrap data for the before MtE period and the after MtE period was collected daily by custom job number, and calculated daily by percentage. The sand of scrap properties analyzed included: Burn in, core scab, crush, dirt, pinholes, blow, mold crack, scab, shrink, slag, sticker, swell, tear up, warp, wash, other, etc. Taking dirt for example, the percent value could be calculated as following: DailyCountofDirt Dirt% DailyCastingMadeWhenDirtHappens By running 2-sample t test and Levene s test, the mean and standard deviation changes before and after MtE implementation could be evaluated sample t Test The results of 2-sample t test to compare means of sand related scrap data are shown in Table 8. Because scrap levels are low for each category, it was not always possible to collect sufficient

52 43 data for analysis. Statistic tests failed to give a significant effect of MtE on burn in. Going back to the original data, we could find Neenah Foundry had little to no burn in scrap, before and after MtE implementation. Dirt was the most common scrap category. The significant change in dirt scrap also control total molding scrap. These were the only two factors of scrap with a low p value. The mean of other scrap categories did not change significantly which supports the conclusion that changing the mulling time in a MtE mulling control system did not increase sand related scrap. Table 8 Sand Related Scrap 2-Sample t Test Result Mean (Before MtE) Mean (After MtE) % Change P Value Burn In Core Scab 0.04% 0.07% 65.0% Crush.89% 0.97% -48.7% Dirt 4.44% 2.63% -40.8% 0.06 Pinholes 0.02% 0.03% 25.0% Blow 0.0% 0.3% 35.% 0.76 Mold Crack.07% 0.49% -54.2% Scab 0.06% 0.08% 33.3% Shrink 0.45% 0.48% 7.6% 0.86 Slag 0.82% 0.50% -39.6% 0.8 Sticker 0.96%.75% 82.3% Swell 0.52% 0.67% 28.8% Tearup 0.86% 0.37% -56.9% 0.86

53 Warp 0.07% 0.0% -8.6% Wash 0.06%.07% 845.5% Other 0.4% 0.% -24.% Total Molding Scrap 3.65% 2.00% -45.2% Levene s Test Levene s test was also used to compare the standard deviation of sand related scrap data with a significance level of 95% meaning that given a p-value of less than 0.05 so that we have more than 95% confidence to be sure the change is true. The results of Levene s test are shown in Table 9. Table 9 Sand Related Scrap Levene's Test Result StdDev (Before MtE) StdDev (After MtE) % Change P Value Burn In Core Scab 0.20% 0.40% 00% 0.72 Crush 3.70% 3.50% -5% Dirt 3.70% 2.20% -4% 0.05 Pinholes 0.0% 0.20% 00% Blow 0.30% 0.60% 00% Mold Crack 3.0%.40% -55% 0.29 Scab 0.20% 0.30% 50% Shrink 0.60%.0% 83% 0.607

54 Slag.00% 0.60% -40% Sticker.70% 7.70% 353% Swell 2.90% 4.00% 38% Tearup.90% 0.90% -53% 0.63 Warp 0.30% 0.0% -67% 0.8 Wash 0.20% 5.80% 2800% 0.32 Other 0.40% 0.30% -25% Total Molding Scrap 3.40%.80% -47% 0.02 Mull to Energy did not lead to an increase in sand related scrap. Many of sand properties are highly correlated and more analysis is needed to confirm if changes other than Mulling to Energy could be impacting the numbers. 4.3 Mulling to Energy Analysis Result Figure 30 shows the daily minimum, average, maximum mulling before and after Mulling to Energy was implemented.

55 Before Before Before Before Before Before Before Before Before After After After After After After After After After After After After 46 Mulling Time (MtE) Ave Mulling Time Max Mulling Time Min Mulling Time Figure 30 Time per Batch before and after MtE Statistical tests of average mulling time will clarify mulling vs cost saving for MtE. Table 0 shows the results of changes in mean and standard deviation before and after MtE implementation. Table 0 Time per Batch Changes before and after MtE Mean (2 Sample t Test) Std Dev(Levene's Test) Before(sec) After(sec) P-value 0 0 The mean and standard deviation of daily average mulling time per batch all changed significantly. The mean of daily average mulling time per batch decreased by 25% percent, which is a large energy saving. The time series data and standard deviation change told that the average mulling time per batch became more consistent day by day even though the muller was no longer controlled by fixed cycle time. Shorter mulling cycle will bring larger capacity for the molding

56 47 line assuming the same mulling energy setpoint is used. Other benefits of mull to energy include the ability to avoid the possibility that un-pour mold and very high sand to metal ratio jobs will cause the mullers to overload. Mulling by a fixed energy dose allowed the system to compensate for the retained strength in the incoming green sand in way that mulling by a fixed cycle time cannot [0]. 4.4 Mulling to Asymptote Analysis Result Mulling to Asymptote control has the potential to reduce the mulling time compared with Mulling to Energy control. Figure 3 shows nearly 50% of decrease in mulling time with the impact of Mulling to Asymptote as observed with initial time at Neenah Foundry Mulling Time (MtA) Ave:MixTime Max:MixTime Min:MixTime Figure 3 Time per Batch before and after MtA On the energy side, WE Energies, subcontracted by Neenah Foundry, reported a 40% decrease in energy consumption under Mulling to Asymptote control as shown in Figure 32. Week was in the period of mulling by fixed mulling time with an average KWh/ton mulled of After the

57 48 muller was instrumented with a true power meter to capture the energy intensity and stabilized from week 2 to week 7, the average KWh/ton mulling energy decreased from 2.64 to.48. WE Energies also reported the comparison of cost and energy saving by implementing Mulling to Asymptote control in Table : When mulling an average of 900 tons of sand each day, the estimated annual energy consumption controlled by fixed cycle time is 866,000 kwh whereas the estimated annual energy consumption controlled by Mulling to Asymptote is 486,000 kwh. There is a significant annual mulling energy saving of 380,000 kwh resulting in an annual cost saving of $ 30,500. Neenah Foundry reported that because the additional mulling capacity due to shorter muller cycle times was not needed, they could shut down one of the three mullers and only ran two mullers under Mulling to Asymptote control. Figure 32 8-Week Average kwh/ton Mulled Table Energy Saving under MtE Average Sand Mulled Energy Consumption - 00 second Energy Consumption - asymptote 900 tons/day 2.64 kwh/ton.48 kwh/ton

58 Baseline Annual Energy Asymptote Annual Energy Annual Savings 866,000 kwh 486,000 kwh 380,000 kwh 49 Annual Cost Reduction $ 30,500 Percent Reduction 43.9%

59 Chapter 5 50 Conclusion and Future Work By comparing sand properties and sand related scrap before and after Mulling to Energy implementation, we found the sand properties did not change significantly. There was a significant decrease in sand related scrap, meanwhile the variability of sand related scrap also reduced after MtE implementation. However, there were slight sand system setpoint during the data collection period which may affect the scale of Mull to Energy benefits slightly. The mulling time and mulling energy comparison showed significant saving on cycle time per batch and energy dose per batch without degrading sand properties and increasing sand related scrap issues. The 25% mulling time savings decreased without loss of sand properties and casting quality suggest that control of batch mullers by energy dose per batch will both decrease the average mulling cycle will improve sand consistency while reducing sand related scrap. Mulling to Asymptote muller control development also showed great additional benefits resulting in a 44% decrease in mulling energy consumption annually, a 40% decrease in mulling time as well as $ 30,500 saving in annual energy cost. Further study to collect and analyze relevant sand properties and casting quality data will be the next step to verify that Mulling to Asymptote control will not cause sand properties and casting quality problems. However, it must be pointed out that many sand properties are highly correlated. Changes in a one particular sand property should be considered carefully, because sand properties are highly autocorrelated and are not independent. To go on with more complex statistical analysis, it is necessary to analyze normally stable data with stationary series without correlation, thus fitting ARIMA model will support a more robust assessment of system dynamics. An ARIMA(Autoregressive Integrated Moving Average) model can be defined as an ARIMA(p,d,q) model, where p is the number of autoregressive terms, d is the number of

60 5 differences needed for stationary residuals, and q is the number of moving average terms. P and q can be decided by ACF and PACF results. If the PACF shows a sharp cutoff whereas the ACF decays slowly, the data series displays an AR signature. The lag at which the PACF cuts off is the indicated number of AR terms which is p. If the ACF of the differenced series displays a sharp cutoff or the lag autocorrelation is negative, it may be necessary to add an MA term to the model. The lag at which the ACF cuts off is the indicated number of MA terms which is q. Given that sand data collection in this analysis is neither stationary nor normal, by checking I-MR chart and correlation of each sand property, it is possible to estimate a form for the ARIMA model based on the calculation of ACF and PACF, then fit a model, and then calculate ACF and PACF for the residuals. If the ACF or PACF of residuals contain significant terms, it may be necessary to add new parameters to the original simple ARIMA model. This procedure continues until the ACF and PACF do not contain significant terms. A well-fitted ARIMA model should no longer have significant ACF and PACF in the residuals which are stationary and normaldistributed. Analyzing the normal and stable residuals fitted by ARIMA model will validate the success of this modeling strategy.

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66 APPENDIX A: AFS Green Sand Test [2] 57