CHAPTER 2. LITERATURE REVIEW. This chapter contains a brief review of literature which exists in

Transcription

1 CHAPTER 2. LITERATURE REVIEW This chapter contains a brief review of literature which exists in the field of casting and its simulation. Within this broad area, the present work involves simulations of mould filling and its effect on solidification behavior of metals/alloys. Accordingly, in line with the scope of the present study. It also covers the advantages of simulations in improving the quality of castings, and lists the major commercial applications currently available. 2.1 THE CASTING PROCESS The casting process starts from receiving an order from a customer which may include the design, physical properties, etc., then the foundry must plan how to make the castings, what methods must be used, then produce a prototype of the casting[4], modify the casting methods to get rid of the defects, produce the product, and last of all, send the final product to the customer. Fig. 2.1 shows the main procedure of a casting process, but the procedure in each casting facility may differ in detail. 7

2 Fig:2.1. Main procedure of casting [5] 8

3 2.1.1 New Casting Development From Fig:2.1, the area in the shaded box could be called the development of a new casting, which in detail, might be separated into three stages, product design, tooling development and foundry trials [8] Product design The three important considerations, which effects the technoeconomic value of a cast product, can be stated as: (i) Functional requirements (ii) Property requirements and (iii) Production and quality requirements. The above requirements are developed through three steps of the product design, which are conceptual design which focuses on the geometries of the product to accomplish the required functions, detailed design, which includes selecting the materials, defining the geometry and its tolerances and prototyping which basically is producing a prototype to test the form, fit and function of the product. Iterations may be done to these design steps to achieve optimality of techno-economic value of the component Tooling Development This stage involves setting the best orientation of the casting and the determination of the parting line or parting lines if there has to be more than two segments of moulds to produce the casting. 9

4 Also, some castings might have multiple cavities instead of just one. It also involves the internal cavities such as holes and undercuts which needs the design and incorporation of cores [6] The cores or dies must be easy to remove from the part. additional cavities comprise feeders or risers (number, location, shape, and dimensions), sprues, runners, the gating system which leads the molten metal into the mould. Other accessories include cooling, guiding and ejection systems (for die casting). The method for manufacturing the tooling depends on its material, complexity, quality and time/cost considerations Foundry Trials Trial castings are made to observe the flaws and defects that might happen in a casting which may occur from the previous stages. components must be tested by destructive and non-destructive techniques for finding surface and sub-surface flaws. Macro porosities and shrinkages may be seen by the naked eye while micro porosities and micro structural defects would require seeing through a microscope. Non-destructive methods include radiography, ultrasound, magnetic particle, dye penetrant, and eddy current testing[7]. Using the outcome these tests, the tooling, may be modified, and the process parameters, may also be modified to improve the casting quality. If the defects cannot be eliminated by modifying the process parameters or tooling design, then the product design may be modified. However, it is very expensive and time 10

5 consuming. Fig:2.2 shows the relationships of management time spent, the ability to influence the production process, the cost of rectifying mistakes in the process and the accumulated costs to each product development phase Fig:2.2. Cost and impact of product development phases [8] 2.2 CASTING SIMULATION SOFTWARE In this day and age, customers, especially in the automotive industry, would be more likely to request castings with high quality (Q), quick delivery (D) and at a low cost (C). A tool that foundries may use to achieve the three goals previously mentioned is to apply Computer Aided Engineering into their process, in this case is by using computer simulation software for casting. A generalized procedure for using casting simulation software may be explained as follows. 1) Build a model of the casting design including the gating system and all other material used with the casting, such as chills, cores, sleeves, 11

6 etc. This step may be done by using a CAD (Computer Aided Design) system. 2) Input required data needed for computation, such as the physical, mechanical and heat properties of the metal, properties of the mould or die, pouring temperature, pouring time, pressure, etc.[9] 3) Computation of the simulation, which different casting simulation programs may have different approaches in simulating the results. Some well known approaches, for example are, the numerical simulation approaches (Finite Element and Finite Difference Methods), the geometrical approach Sarfaraz, et al. [11], the mesh less method Lewis et al. [10] 4) Simulated results and interpretation of results. The results from the simulation program may be shown in the form of graphs or colored figures with numerical results depending on what criterion is used, such as the temperatures in each section of the casting at a given time, solidification times, hot spots, material density, etc. These results must be translated into useful information to evaluate if a casting is sound or not, or what must be done to improve the casting design and start from step 1 once again The Usefulness of Casting Simulation Software In the past, the foundry man has strived for ways to improve the casting process and eliminate the defects that occurred in the castings by trial and error and past experiences. The time needed to produce a particular product is a time-consuming process. Problems occurred in the casting may only be solved through trial and error. Scientists 12

7 throughout the years have studied the science of casting and metallurgy and developed theories and mathematical models to explain the properties of metals while going through the solidification process. Simulation programs were developed from these methods which are useful in predicting how the casting will come out. Defects and problems can be discovered before the actual casting is cast avoiding costly tests to prevent the problems. The process of manufacturing a new casting design in a foundry starts from receiving a design from a customer, which would include all dimensions and tolerances, what kind of material and additives, and may also include the strength, hardness or surface finish, etc. Then the foundry man or the foundry engineer would design the gating and risering system for the casting. The time used in designing and re-designing the gating and risering system might take a few days or up to several weeks before good castings can be made. Depending on the casting complexity and the skill of the foundry man or the engineer. Casting simulation software can predict where and what defects might occur in a casting and the time and material used in the trial stage may be reduced significantly Casting Simulation Methods The casting simulation programs have different approaches in calculating and predicting the outcome of a casting. Each method hold advantages and disadvantages compared to another. Some casting simulation methods may be shown below: 13

8 i) Numerical Approach such as : Finite Element Method (FEM), and Finite Difference Method (FDM) ii) Geometrical Approach - K-Contour Method. iii) Computer Wave Front Analysis, generally implemented as: Pourout Method - Cubic Spline Functions iv) Mesh less Method. v) Grid-based simulation system Pan et al. [12] Study of Casting Technology and Simulation Software s for Castings Metal casting has evolved throughout the ages. The techniques have been passed on and improved through generations. Metal casting, although having a history of thousands of years, still hasn t stopped evolving. The developments and findings of new casting techniques and technologies are made every day. Alloy steel sand casting technology is very unique and has a very long history. The research on how to simulate the sand casting process hasn t been found in the literature search during the study. Because alloy steel casting process experiences the similar mould filling and solidification as in other casting process, it implies that many approaches and methods conducted in the designs and analyses of the conventional casting process might be extended to the designs and analyses of alloy steel casting process. The methodologies of other casting process are evaluated to help in the development of an approach to simulate sand casting process, applied to alloy steels. 14

9 The casting simulation research has been actively carried out for around 20 years. During the early stage, the research work was focused on the thermal analysis of the casting process [13,14]. In the early 1990s, more and more work dealt with coupled thermal, radiative view factor and fluid-flow calculation [15,16]. But until very recent, the research work hasn t been focused on the integrated model of the whole casting process. The research topics may be categorized into following interrelated areas: o Heat Transfer Model o Mould filling analysis o Solidification analysis o Stress modelling o Casting process control and optimization Thermal Analysis of Casting Process Thermal analysis is the first research area of the computer simulation of the casting process. The early research work included the heat conduction and molten metal solidification in the casting process [17]. The radioactive heat transfer is added to the models of investment casting process [18, 19] since the heat transfer through radiation is significant in this process Mould Filling Analysis of Casting Process Mould filling has been one of the earliest areas of the casting simulation. Since the nature of the mould filling is influenced by the shape of mould cavity, the algorithm is developed based on the computing techniques for solving transitions in fluid flow. The earliest 15

10 algorithms used for the mould cavity flow filling are Marker-and-Cell (MAC) developed by J.E. Welch et al. [20] and the Simplified Markerand-Cell (SMAC) algorithm developed by A. A. Amsdem and F. H. Harlow et al. [21and 22]. Then another algorithm, Volume of Fluid (VOF) was developed by B. D. Nichols and C.W. Hirt et al. [23] this approach tracks the free surface by solving a transport equation of the pseudo-concentration function. Several modifications have been made to the original VOF algorithm, which were subsequently named as Donor-Acceptor approximation [24] and Van Leer approximation [25]. The SOLA-VOF [25, 26] which employs the Donor-Acceptor algorithm became a popular approach in the mould filling simulation research. In recent years, progress has been made in embedding VOFfilling type algorithms into FE (Finite Element) codes [27, 28, and 29]. The unstructured mesh facility of FE permits a casting mould to be represented more accurately using far less elements/cells than in the control volume case Solidification Analysis of Casting Process The central element in the solidification process model is in dealing with the nonlinearity associated with the latent heat release/absorption along with the solid liquid interface. A fully developed solidification model also needs to integrate the latent heat algorithms with algorithms dealing with fluid flow. For a pure metal and eutectic alloy, the solidification temperature is at one point. But for other alloys, there exists a solid-liquid region, the solidification starts at the liquidus temperature and ends at the solidus 16

11 temperature. To handle the latent heat release between the solid and liquid interface, two major classes of methods have been developed,viz.,1. Front tracking methods [30, 11] and 2. Fixed grid methods [31]. The inter-dendrite flow in the mushy region should be included in the fluid flow model, this is achieve on the addition of appropriate source terms, e.g., a Darcy like source term [33] to signify the porous nature of the mushy region. A range of examples of this approach can be found in the references Stress Analysis of Casting Process In the process of solidification, there is an interaction between the cooling behavior and the deformation of the solidified component of the metal in the mould. As the metal cools, it often shrinks and causes a gap to form at the metal surface-mould interface and this impact the subsequent heat transfer behavior. The solution of models of the solidification process based on assuming heat conduction only and using a FE method has been possible for some years. It is clearly demonstrated whenever the thermal behavior is independent of the deformation behavior, then the FE method is a straightforward approach [34,35and36], given the thermal history to predict the elastic deformation behavior as the phase change proceeds. Much work [37] on the stress development in continuously cast metals during solidification has also been pursued over the past decade. In a comprehensive review of the thermal stress development in metal casting processes, Dantzig [38] describes the formulation of elastic, plastic and creep behavior. 17

12 The researchers [34, 35] have been using the thermo-elastic FE code and heat transfer codes to model the impact of the interaction between heat transfer and deformation. The explicit thermo-elastic FE code enables the prediction of the separation between the metal and the mould. The heat transfer FE code is used to simulate the temperature during solidification. The heat transfer code pumps through the temperature distribution at every certain thermal time steps for the metal-mould geometry to thermo-elastic FE code to evaluate the distortion of the metal and the mould. The impact of this distortion on the formation of gaps is then fed back to the heat transfer code. The numerical methods used in computer simulation of casting process, are presented in detail in [39,33 and 40] Studies Useful for Casting Simulation Software In order for a casting simulation software program to predict the results of a casting, there must be studies and researches about the characteristics of each component in each process. Liu et al[41]. studied the effect of die pressure, time of loading and piston position of pressure amplification on the variation of pressure and the quality of casting because casting pressure conditions in die casting have immense effect on die casting defects, which may be gas porosity, shrinkage porosity and gas holes. Normally metals shrink when they lose heat, which is the same as a casting would shrink when it solidifies in a mould, but sometimes the casting may start to expand after it cools down to a certain temperature according to what material 18

13 it is made of. The heat transfer rate would depend on the heat transfer coefficient (h) between two types of surface, in the case of casting are the casting and the mould, but since a casting may shrink in the solidification process, the h value may change because of the gap of air formed by the shrinkage of the casting. Wang et al[42]. has conducted a study to measure the interfacial heat transfer coefficient (h) between high temperature casting alloys and moulds during the casting due to gap formation. It was found that a high value of interfacial heat transfer coefficient is generally obtained at the start of the casting, then the value drops abruptly and then rises to a certain value, and then the value gradually decreases. It was also observed that the heat transfer coefficient (h) value is not considerably affected by the casting alloys but rather by the mould material; castings with ceramic moulds would have an h value between 22W/m 2 K and 350W/m 2 K while sand moulds are between 40W/m 2 K and 90W/m 2 K.DeLooze et al. [43] has studied how the operating parameters of a low pressure die cast (LPDC) machine and the quality level of the aluminum melt precious the casting cooling rate and/or the microstructure of the aluminum. The arrangement and distribution of micro porosity in the castings was used as an indicator of casting quality and solidification conditions, and experimental data for the operation of burst feeding in low pressure die casting was detected. There were important improvements to the directional solidification and micro structural refinement were achieve with die cooling has done research on applying Campbell s ten casting rules 19

14 [44]to develop high quality aluminum castings quick cast process, Yang et al [45] studied the effect of casting temperature on the properties of gravity cast and squeeze cast aluminum alloy with 13.5 percent silicon and zinc alloy with 4.6 percent aluminum and found out that casting temperature had an effect on the mechanical properties of both the types of casting. Herman et al.[46] has done a study about implementing an optimization tool consisting in an optimization algorithm and casting process simulator. It was applied to an industrial casting machine where spray coolant flows were optimized. Casting process simulation has become an industry standard, casting simulation has helped foundries to point out the factors that have a significant effect on the quality and price of the casting, as observed by Jakumeit et al.[47]. Many a casting and casting related simulation software programs are created in order to achieve the most accurate predictions. Some may have more strength in some areas than the other. Sarfaraz, Ahmad Reza [48] have done a study of coupling two simulation programs, the CFD (Computational Fluid Dynamics) program FLUENT and casting simulation tool CASTS, for simulating a mould filling and solidification for an aerospace investment casting. Also, a paper by Moreira and Ribeiro et al.[49] discusse the advantages and limitations of the use of two software packages, FLOW-3D based upon the Finite Element Method and SOLIDCast 20

15 based upon the Finite Difference Method. The Finite Element and Finite Difference Methods are the two most well known approaches in casting simulation software. Both methods are mesh based simulation programs which may have some disadvantages in predicting hot spots and simulating the jetting and splashing effects during mould filling. Alter natively, the experienced foundry men, devised the techniques for predicting hot spots by using a geometric transformation method known as the medial axis transformation and a technique based on mesh-less method for simulating the mould filling process. 2.3 Implemented Casting Simulation Software Case-studies. Many casting manufacturers have implemented casting simulation software to their production and have been successful. Wright et al. [50] conducted two successful case studies of implementing casting simulation technology within the company, Walker Die Casting, which is a producer of complex aluminum castings. In 1995, a foundry named Raahen Teräsvalimo Oy in Finland was casting various valve components for Neles Controls. The foundry was experiencing some defects in a particular stainless steel that resulted in repair welding. The foundry considered investing in casting simulation software and this component was selected as a test case. The foundry used CastCAE to simulate the test model and the defects were predicted exactly as what the foundry experienced. Circular chill and insulating sleeves were added in the system and resulted in a sound casting. 21

16 Later, the real casting was made according to the new design and resulted in sound castings. A research by Alonso and Franco et al. [51] used SOLIDCast along with OPTICast to raise the yield of vertical gating systems in the investment casting process of a jewelry workshop. It was found that these two modules showed a great potential from improving the design of the filling systems. 2.4 CURRENT STATUS OF REASERCH So many recent investigators have discussed on a variety of issues in the terms of yield, shrinkage, and soundness of castings through simulation. Some of them were discussed here Solidification Modelling Review Kannan et al. [52] acknowledged that the present thrust of solidification modelling research lies in the enhancement of heat transfer and fluid flow techniques towards the goal of solving micro structural modelling problems. The current state of the art, which takes the form of 3D finite element and finite difference codes, fully coupled with a computational fluid dynamics simulation. Ghosh et al. [53] but since to a large extent leftovers unidentified about the process of nucleation in these materials, some level of ambiguity exists in their model. Brown and Spittle et al. [54] spot out that while finite element and finite difference analysis methods can offer commanding solutions to solidification problems, they should have a physical model of the solidification process upon which to support their analysis. Tu et al. 22

17 [56] make use of a typical, state of the art finite element application to examine the investment casting processes. Beffel et al. [57] their work reveal the extent of information available from a typical FEM-based solidification simulator. It also reveal a need for significant computing time. The investigations by Pehlke et al. [58] resulted in two commercially available solidification simulators, the 2D AFS Solid package and the 3D AFS Solidification System. Further the literature reveals that Estrin et al.[55] utilized built in mould and material databases in order to produce quality results. Brown et al. [54] observed that each FDM model requires complete reworking in order to accommodate the analysis of a new alloy. Further they realized an speed improvement over conventional FDM analysis with cellular automaton software. Hill et al. [60] as well use a cellular method to arrive at a quick, fairly accurate solidification time plot. The information gained from this analysis provided a basis for design of risers and gating using expert system routine, to quickly visualize the thick regions of a casting in the same way as a casting engineer does. Among the earliest solidification modelling techniques, Chvorinov s rule is quite popular [61] however Upadhya and Paul et al. [62] stated that most applications utilizing Chvorinov s rule used some form of feature-based modelling or sectioning of a solid model in order to break a full casting geometry into simple components. While some of these applications are capable of performing 3D calculations, most of them only apply to 2D simplifications of 3D models. Pei et al. [63] 23

18 presented an application to perform most of the work in dealing out a solid model for modulus calculations. Sirilertworakul et al. [64] presented solidification modelling details by sectioning a casting geometry automatically using an AutoCAD function to accurately predict localized differences in cooling rates for internal and external corners which influence micro structures and internal stresses in the castings. while Nieses et al. [65] also computed the section moduli of 2D sections based upon an area/perimeter value for section modulus, Sirilertworakul et al. [64] proposed a point modulus calculation which depends upon both the distance of a point to each edge of a section and a view factor for each edge with respect to that point. In an attempt to extend Chvorinov s rule to high alloy steels, cast irons and certain non ferrous alloys DeKalb et al. [66] combined this global section modulus formula with a considerable amount of experimental data. This code popularly known as SWIFT, can also calculate the start and end of the mushy zone of a long solidification range alloy at a given time. [62 and 67] used a distributed point modulus calculation to calculate the local section modulus of points in a finite difference mesh of a casting. A majority of the commercial packages available today, for solidification modelling, incorporate concepts discussed above. Such as SWIFT, MAVIS (cellular automaton), and ProCAST. In addition these commercial packages have extended capabilities to be run on PCs, and many of them can be operated on multiple platforms. Further, to facilitate solidification 24

19 modelling in investment casting,sandia National Laboratories has brought a commercial package, called FASTCAST [68] P.L Jain et al.[69] suggested the best suitable testing method for the identification of defects in the casting, as result of improper design of parting line or due to misplacement of cope and drag. Viswanathan et al.[70]described the extended utility of ProCAST software for avoiding shrinkage, improving cast metal yield, optimizing the gating system, optimizing mould filling, and finding the thermal stresses, as means to maintain the casting quality. Maria Jose et al. [71] reported that successfull solidification modelling of steel sand castings using ProCAST which resulted reducing the production costs and increasing of profits by improving yield. B.Ravi et al. [72] while reviewing CAD/CAM revolution for small and medium foundries, reported that mould filling has the greatest influence on casting quality. He substantiated by stating that the flow of molten metal being poured experiences turbulence for a variety of reasons. In his paper, he described the systematic procedure for design, analysis, optimization and validation in steel casting practice and compared the performance of various commercial packages. A.K.Tahari et al.[73] simulated the combined effects of the heat transfer; sand permeability, free surface pattern, and fluid flow during 25

20 the modelling stage of metal casting. Practically, such routine can be used to analyze a casting design through simulation. In an attempt to develop high quality aluminum castings, Wong et al.[74] had applied Campbell s 10 casting rules and the designs were validated by using CAE software. B.Ravi et al. [75] elucidated the role of simulation in design and analysis of casting, the benefits, bottle necks and best practices involved. It is further concluded that casting simulation tools has become essential for a better and efficient casting practice. Guharaja et al. [76] have done a study about obtaining the optimal settings of green sand casting to yield the optimum quality characteristics by using Taguchi s parameter design approach and verified by confirming with practical experiments. A paper by Moreira and Ribeiro et al.[77]discussed the performance of two software packages, namely FLOW-3D and SOLID Cast they concluded that SOLIDCast cannot be applied to complex geometries but gave the results in short span of time for simple geometries. Hu Hong-jun et al. [78] researched the influence of casting process on quality of casting using ProCAST. They reported significant improvement in casting yield by optimizing the pouring and riser system. 26

21 B.Ravi et al.[79] analyzed feedability using solidification simulation, and found that high temperature signifies fewer possibilities for solidification shrinkage. Prabhakar Rao et al. [80] analysed the stresses developed in straight and flanged bars in sand and die-casting of an aluminium alloy using X-Ray diffractrometry. It was concluded that due to hindrance in contraction, the flanged bars developed higher stress than straight bar. C.Monroe et al. [81] discussed the effect of mould and cast metal properties on distortion during casting of steel, and concluded that they have substantial influence on hot tear formation, during solidification of steel castings. 2.5 CONCLUDING REMARKS The research findings presented by each of the authors cited in the literature review were critically examined to establish a sound foundation for this research project and to provide a basis for measuring its contribution to knowledge. Starting from the middle of 1980s, due to the decreasing cost of computers and advances in computing methods, computer simulation of foundry process has been developed and improved by both academic and industry. Studies on casting defects and improvement on yield of castings have then stepped forward from experiment based investigations to computer simulation aided research. The advantages of the simulation of the castings and the factors that can be controlled in the simulations to 27

22 get the improved quality of the casting. So many researchers described success in modelling and simulation of solidification. By implementing the simulation skills to the casting process we can visualize the process and by giving the proper data to the simulation we can get the optimum parameters of the casting process. The results of this review revealed that there have been rapid advances in many areas of casting solidification simulation technology with regard to casting quality. This was mainly brought on by the requirements to deliver defect free castings to customers and increase the quality standards of casting products. An extensive search of the literature has also indicated that simulation of non ferrous die castings with FEM simulation has not been explored to the alloy steel sand castings to date. This is the topic of research addressed in this thesis. The above literature review reveals that the primary focus of earlier researchers was on the mechanism of casting defect formation and measures to control the same. Further, it can be noticed that the results of these studies were not confirmed with manufacturing industry. Added to this, there has not been any significant work reported in the literature on solidification modelling of alloy steel castings made by sand casting route. This literature search was focused on obtaining information on simulation of sand castings with complex shapes, by varying gating positions, reducing defects and improving yield of the castings. However, it was found that there was no published research addressing these issues within an integrated (a combination of earlier mentioned factors) framework. Hence, there is a 28

23 need to examine the performance of simulation applied to alloy steel castings, made by sand casting. The literature review indicated that simulation techniques had the potential to be used to detect sub surface defects in castings. It was clear that factors such as mould material and cast materials are critical variables in the simulation of castings. However, FEA approach has not been suitably explore for simulation of alloy steel sand castings. Therefore, the development of an effective methodology for numerical simulation of alloy steel castings made by CO2 strengthened sand moulds through a FEA simulation approach is investigated as part of this research program. 29