related Casting simulation 1988 to studies

Transcription

1 Technical Paper for 61 st Indian Foundry Congress, Kolkata, Jan 27-29, 2013 Casting Design and Simulation Software: 25-Year Perspective and Future Dr. B. Ravi, Chair Professor Mechanical Engineeringg Department Indian Institute of Technology Bombay, Mumbai Phone: , ABSTRACT Casting simulation helps visualize mold filling and casting solidification; predict related defects like cold shut, shrinkage porosity and hard spots; and optimize the casting design to achievee the desired quality with high yield. Flow and solidification of molten metal is however, a very complex phenomenon that is difficult to simulate correctly by conventional computational techniques, especially when the part geometry is intricate and the required inputs (like thermo-physical properties and heat transfer coefficients) are not available. For industrial application, we need alternate approaches that are fast, reliablee and user-friendly. We trace the history of such approaches, starting from the Modulus Concentration Method in 1988 to the recent Gradient Vector Method in 2012, as well as their incorporation in software programs for casting design and simulation. Key benefits and best practices, based on studies of industrial cases of casting defect prediction and feeding system optimization, are presented. Current research directions include integration of basic and advanced simulation techniques, useful for large foundries; and cloud-based simulation, useful for SME foundries. The paper is expected to benefit both researchers and foundry engineers, who are interested in developing and utilizing casting simulation technology. Keywords: casting, defects, mold filling, solidification, simulation, software, quality. 1

2 1. INTRODUCTION Casting process offers the widest variety in terms of metals and alloys, shape complexity (including internal features), size and weight, production quantity, and applications [1]. The apparent ease of producing a casting melting and pouring metal into a shaped mold hides the complexity of the physical phenomenon involved. All three modes of heat transfer are involved (conduction, convection and radiation), but their amounts depend on the thermo- do not physical properties of the cast metal and the surrounding mold. All regions of a casting cool at the same rate, which is driven by part geometry and boundary conditions imposed by various mold elements. Moreover, solidification is accompanied by volumetric contraction, manifesting in defects like shrinkage porosity (Fig. 1). Premature solidification (before complete filling of mold) can lead to misrun and cold shut. Metallurgica factors like grain size and shape, phase distribution and segregation affect mechanical properties (strength, ductility, hardnesss and fracture toughness). The large number of material and process parameters is exacerbated by the difficulty of controlling them. While foundry engineers struggle to produce high quality castings, researchers find it equally difficult to consistently replicate casting defects and properties even in laboratory conditions. Fig.1. Shrinkage defects in ferrous and non-ferrous castings The opacity of mold and difficulty of instrumentation are major obstacles to experimental studies of industrial castings. Hence foundries rely on indirect methods by trial and error to develop new castings with the necessary gating and feeding systems. Still, yields are low (40-60% for steel castings) and rejection rates are high (10-15% in jobbing foundries). This leads to considerable wastage of melting energy and other production resources, effectively increasing the cost per casting and reducing the production capacity of a foundry. 2

3 Computer simulation offers an attractive alternative to foundry experiments. One can virtually look inside the mold, and visualize the progress of metal flow and solidification. Velocities, temperatures and cooling rates at different locations inside the casting can be measured. Metallurgical models can be applied to predict casting defects and properties. The methods design (gating and feeding system) can be iteratively improved and verified by simulation. This can help in producing castings that are right first time (in shop floor trial) and right every time (consistent quality) with high overall yield (low manufacturing cost). Mathematical modeling of casting solidification, a non-linear moving-boundary problem, has been well documented by several researchers since 1980s. It includes equations for heat transfer by conduction, convection and radiation from the casting to the mold and surrounding air, as well as various boundary conditions, latent heat release and absorption, convection effects within the melt, and feeding mechanism driven by metal shrinkage. More complex models involve coupling molten metal flow with heat transfer and phase change. Since no analytical solutions exist for the mathematical equations, various numerical schemes have been adopted for casting simulation. These include the finite difference method, finite element method and finite volume method. These have been employed to develop casting simulation programs in many research institutes around the world; about a dozen of these are commercially available today [2]. The accuracy of numerical methods greatly depends on the database of temperaturedependent material properties and interfacial heat transfer coefficients. These values need to be generated for the specific combinations of materials and processes used in a foundry, through cumbersome experiments that need instrumentation and may take several months. Secondly, most of the current simulation programs are run in batch mode: CAD modeling of part and mold elements, followed by pre-processing to prepare the inputs, solver computation to generate the results, and finally post-processing to visualize the results. One complete cycle of simulation can take several hours; multiple iterations required for optimization can take several days to weeks. If any input is wrong or needs to be changed, then the whole sequence has to be run from the first step again. Moreover, the nature of inputs and interpretation requires expert-level users. It is therefore not surprising that most of the simulation programs available in market today, are mainly used by large production foundries who can afford the required technical manpower and research facilities. Their main application is to study the variations in casting quality, first by matching simulation results 3

4 with shop-floor observations, and then to incrementally improve quality and yield through changes in material and process parameters, supported by simulation. The SME and jobbing foundries on the other hand, require rapid development of new castings with minimal level of user expertise. This necessitates entirely new approaches to casting simulation, combining the simplicity and usability of geometry based methods (like Chvorinov s equation relating casting solidification time with its modulus) with the power and insight of numerical methods. This has been the major focus of our work over the last 25 years and is documented in this paper in a chronological manner supported by relevant references that contain more details. 2. ALTERNATE SIMULATION TECHNIQUES Our search started in 1987 based on the assumption that hot spots occur at locations of mass concentration inside a casting [3]. For this purpose, cross-sectional areas parallel to the orthographic planes were computed and plotted against the coordinate values; the location inside the casting where the peaks of the graphs intersected pointed to mass concentrations (Fig. 2) ). Fig.2. Section area graph for mass concentration [1988] The next stop in the journey was an algorithm for automatic computation of casting modulus. For this purpose, rays were projected in all directions from a given point inside the 4

5 casting, and their intersections with the casting boundary were computed. This was first performed in 2D, for any cross-section of a casting model. The ratio of the sum of triangular areas enclosed between the consecutive rays to the sum of distances between the intersection points gave the effective casting modulus [4]. Besides the ratio of the two sums mentioned above, the sum of the ratios for consecutive triangles was also computed. This matched the relativee temperatures observed in a casting and helped detect hot spots. The vector sum of modulus values (mapped to the medial line of each triangle) was observed to match the direction of the maximum thermal gradient at that location. The location where the resultant vector was zero, matched the local hot spot [5]. The results for a few 2D shapes were successfully validated by comparing with the shrinkage porosity observed in experimental castingss (Fig. 3). Fig.3. 2D modulus vector method and validation [1990] In , the modulus vector method was extended to 3D shapes, as part of the doctoral work at the Indian Institute of Science, Bangalore [6]. The necessary computational and graphical framework was developedd to sub-divide a 3D model of casting into pyramidal segments around a given starting point lying inside the model. The modulus vector was computed for each segment using the following equation: 3 2 Where, = solid angle subtended by the base of the j th pyramid at its apex, V ij = volume of the pyramid, A ij = base area of the pyramid, p i = position vector of the computation point (apex of the pyramid), and r ij = position vector of the centroid of the pyramid base. The vector sum of the modulus vectors of all pyramids yielded the magnitude and direction of the highest thermal gradient, given by:. The iterativee proceduree involved taking a 5

6 step in the direction of the highest thermal gradient, and repeating the modulus vector computation at the next point, given by: p i+1 = p i + G i. By early 1992, the 3D algorithm for the modulus vector method was developed in C language, and validated on lab castings, ready for industrial application. It was incorporated in a software program running on MS-DOS (16-bit) computers, whichh imported casting models from any CAD software, and displayed the trace of successive computation of the thermal gradient vectors (Fig. 4). Local hot spots were identified as the location where the thermal gradients reduced to zero. Fig.4. 3D modulus vector method for hot spot [1992] It was observed that the trace of modulus vectors computation represented the local feed- path in reverse. That is, the hot spot fed the previous point of computation n, and so on all the way tilll the start point (usually close to part surface). If the feed-paths from multiple starting points converged to a hot spot inside the feeder, then the feeder proved to be effective, and the connected regions of the casting were found to be free of shrinkage defects. Also the feed-paths, being essentially along the maximum thermal gradients, were perpendicular to the local isothermal contours obtained by Finite Element Method simulation of the casting. However, the generation of feed-paths without an explicit computation of temperature es inside the casting, that tooo at a fraction of the time required for the latter, opened up an entirely new approach for feeder design optimization [7, 8]. 6

7 The next step in the evolution was in handling the effect of feeding aids like chills and feeder sleeves (insulating or exothermic). These elements affect the heat content and local heat transfer rates, effectively increasing or decreasing the local casting modulus. The modulus extensionn factor (MEF) used in industry to specify the feed-aids was found to be the most suitable factor to compute the change in modulus vector length [9]. The modified modulus vectors fairly captured the effect of the mold elements on the feed-paths, which was ascertained by matching with a few industrial castings. 3. CASTING DESIGN AND SIMULATION At this point it became clear that widespread use of casting simulation is difficult, especially in small and medium foundries, unless two problems are tackled. The first is the unavailability of 3D CAD software and the necessary solid modeling skills, particularly where senior metallurgists are involved in casting development. The opposite is true in foundries where relatively novice engineers are involved in casting development: they are comfortable with 3D modeling but lack methoding experience. Fig.5. Prototype and release of AutoCAST [1997, 2000] The obvious solution was to develop a single integrated program for methods design, 3D modeling of feeders and gating elements, and casting simulation. Its prototype was created in (Fig. 5) using C++ + language for Windows 95 (32-bit) computers. All input and output data was combined in a single file, minimizing the need for repetitive inputs across design iterations. Its user interface was planned so as to minimize the time and effort required in completing a casting project. Several experts and industry associates provided the 7

8 relevant casting knowledge that was incorporated along with intelligen 3D modeling of casting elements [ 10, 11]. Over the next decade, several value-added features were added to the program. These included display of the maximum thickness location, automatic identification of cored features, and simple (layer-by-layer) mold filling simulation. Fig.6. Casting design & simulation by AutoCAST-X: [2008] Meanwhile, the modulus vector method was further evolved to handle the size effects of various mold elements, and other boundary conditions like convection. This was achieved by using cubic voxels to represent the part, mold and various methods elements, and by projecting each vector all the way to the outer boundary of mold. This also helped in greatly speeding up the computation, so finer voxels could be used for analyzing large castings with thin walls. The new Vector Element Method was applied to industrial castings, enabling a better understanding of the feed-paths and their interpretation [12]. It was benchmarked with FE based simulation programs [13] and validated by shrinkage defect prediction in industrial castingss [14]. The VEM was implemented in C# language for 32-bit and 64-bit Windows XP computers, with other improvements like part thickness mapping, automatic design of core prints, multi-cavity mold design, vertical parting, automatic optimization of feeder and gating, and methoding reports, along with a completely redesigned user interface (Fig. 6). This was named AutoCAST-X ( in) and launched at the World Foundry Congress and IFEX 2008, Chennai [15]. Visitors were invited to bring their own casting models and simulate them on the spot, creating an initial wave of interest. 8

9 Industry surveys showed that CAD and simulation reduced casting rejections from an averagee of 9.5% to 4.5%, as well as improved yield and customer satisfaction [16, 17]. It was also revealed that these technologies are severely limited by a lack of qualified technical manpower. Threee initiatives were launched to overcome the problem. First, casting consultants were invited to share their industrial case studies showing how casting defects are analyzed and minimized by methods design optimization supported by computer simulation. These were published in Indian Foundry Journal during ; later compiled into a single volume and published by the Centre for Education & Training of the Institute of Indian Foundrymen [18]. One such case study is reproduced in Fig. 7. They represented a range of cast metals (both ferrous and non-ferrous) and processes (both sand and metal molds) and helped build a repository of benchmarks to compare simulation and actual results. Fig.7. Industrial case study of casting simulation Second, a careful study of the industrial case studies was carried out to evolve guidelines and the best practices for minimizing the simulation time and effort, while ensuring accurate predictions. For example, as-cast (not machined) part models should be used for simulation; if this is not possible, at least drilled holes should be plugged before simulation. The 3D model size can be reduced by an orderr of magnitude by suppressing small fillets without 9

10 affecting the accuracy of results [19]. It also became clear that many defects originate from poorly designed parts. For example, correct design of wall thickness and junction parameters (including internal and external fillet radius) can greatly reduce the degree of shrinkage defect [20, 21]. Third, the rules for methods design available in technical literature were revisited in the light of industrial case studies, to find and address gaps in existing knowledge. For example, the well-accepted guideline of designing a feeder with 20% higher modulus than the modulus of connected portion of the casting, can lead to under-sized feeders. The simulation studies clearly showed how feeder increased the modulus of the casting, and hence an even bigger feeder was therefore required, especially for steel and short-freezing range alloys. The feeder neck size on the other hand, is over-estimated by modulus based calculations. The portion of mold around the neck becomes heated and effectively increases the neck modulus; hence a smaller neck is required in practice. Better ways of interpreting the temperatures (hot-spots) and gradients (feed-paths) were evolved, leading to even faster troubleshooting and optimization of new castings without any calibration of the software [22, 23]. 4. ADVANCED SIMULATION TECHNOLOGY Driven by the need for even faster ways to simulate, the Level Set Method (LSM) an Eulerian computational technique for capturing moving boundaries was studied for application to metal casting [24]. The related mathematical modeling also helped in better formulation of the interfacial heat flux and velocity, leading to a new approach named Gradient Vector Method (GVM) for feed-path computation [25]. With this method, it became possible to handle the effect of specific mold elements based on their relative heat diffusivity with respect to the bulk material of mold [26]. Both LSM and GVM were validated by comparing with experimental results, and benchmarked with Finite Volume Method. The GVM was found to be over 10 times faster than FVM for the same mesh size and accuracy of results. Thin wall castings, especially those poured in metal molds, require coupled simulation of metal flow and solidification within the overall framework for casting methods design and optimization. This was achieved by integrating an improved version of the Virtual Casting Solver developed at NIIST [27] with AutoCAST-X, giving birth to FLOW+ (Fig. 8). This 10

11 yielded twin benefits: employing GVM to quickly evolve the methods design through multiple iterations, and verifying the same through a coupled simulation before a shop-floor trial. The advancedd Solver also made it possible to generate other results like flow velocities, liquid fraction, and cooling rate, enabling prediction of other defects like cold shut, microporosity and hard spots. Fig.8. Mold filling, liquid fraction, solidification, and cooling rate results in FLOW + [2012] It is interesting to note that in the last 25 years, the computation power of a typical PC has increased 1000 times (from about 4 MHz to 4 GHz). There is an even bigger increase in RAM (from 256 kb to 8 GB), and graphics display (from 640x350 pixels x 16 colours to 1600x1200 pixels x million colours). These developments have helped in reducing the simulation time to minutes, even for large and complex industrial castings. Over this period, a body of knowledge was accumulated in the area of casting design and simulation. It needed to be shared with the casting community so that students can gain a better idea of industrial practice, and foundry engineers can improve the quality and yield of their castings with minimal effort. This led to the development of E-Foundry Academy ( a web-based resource that can be freely accessed by teachers, studentss and working professionals [28]. There are over 45 video lessons covering science, engineering, technology and applications of CAD and simulation in metal casting. 11

12 Fig.9. Freee online casting simulation at E-Foundry ( The E-Foundry includes an online simulation facility, where users can upload 3D models of their castings, and view the results in their web browser within minutes (Fig. 9). This has been possible by automating all the stepss involved, from generation of mesh to colour-coded temperature images, and using the GVM for the main computation. An interesting and growingg community of users is that of product designers in original equipment manufacturers, who use the facility for checking and improving the manufacturability of their cast parts before finalizing production drawings. 5. SUMMARY AND CONCLUSION For widespread application and benefit of casting simulation technology, it has to become fast, reliable and user-friendly. We have documentedd our efforts in this direction over the last 25 years, including development of alternate simulation techniques, their integration with casting design and 3D modeling, validation through industrial case studies, and knowledge sharing initiatives to expand the talent pool in metal casting field. The author and his team faced many challenges, including lack of funding in the initial years, but prevailed with their efforts and eventually succeeded in developing four successive generations of indigenous software products, mainly suited for SME foundries. It is hoped that metal casting researchers will gain an insight into the positive spiral loop between basic research, industrial application and continuous education, as well as many issues that still need to be tackled. For them, AutoCAST-X framework and E-Foundry Academy provide convenient platforms to test new technologies and to share domain knowledge with the casting community. 12

13 ACKNOWLEDGEMENTS Prof. M.N. Srinivasan provided the initial insight that led to the development of the vector methods. Several research associates helped in developing casting design & simulation software through different generations; Ramesh Dommeti, Koustubh Moharir, Suman Mohandas, Onkar Paradkar and Maxwel D souza deserve special mention. Three scientists: Roschen Sasikumar, Savithri Sivaraman and Elizabeth Jacob at CSIR-NIIST, Trivandrum developed the coupled simulation solver. The feedback from foundry engineers and knowledge shared by casting consultants greatly helped in continuous improvement of the simulation technology over the last 25 years. The E-Foundry is funded by the Ministry of Information and Communication Technology, New Delhi, as a model project of the National Knowledge Network mission. All these contributions are gratefully acknowledged. REFERENCES 1. B. Ravi, Metal Casting: Computer-aided Design and Analysis, PHI, New Delhi, ISBN: , S. Wetzel, Casting Process Modeling Round Up, Modern Casting, 101 (9), 2011, B. Ravi, Interactive Graphics for Pattern and Die Design, Master of Engineering Thesis, Indian Institute of Science, Bangalore, B. Ravi and M.N. Srinivasan, Hotspot Detection and Modulus Computation by Computer Graphics, Official Exchange Paper from India, Proceedings of the 56 th World Foundry Congress, Dusseldorf, Germany, May B. Ravi and M.N. Srinivasan, Hotspots in Castings - Computer-Aided Location and Experimental Validation, Transactions of the American Foundry Society, 98, 1990, B. Ravi, Manufacturability Analysis of Cast Components, PhD Thesis, Indian Institute of Science, Bangalore, B. Ravi and M.N. Srinivasan, 3D Casting Solidification Analysis and Feeder Design, Proceedings of the 40 th Indian Foundry Congress, Institute of Indian Foundrymen, Madras, Feb

14 8. B. Ravi, Solidification Analysis: Practical Applications of Modulus Vector Method, Proceedings of the International Workshop on Solidification, Trivandrum, November B. Ravi and M.N. Srinivasan, Casting Solidification Analysis by Modulus Vector Method, International Cast Metals Journal, 9(1), 1996, B. Ravi, R.C. Creese and D. Ramesh, Design for Casting A New Paradigm to Prevent Potential Problems, Transactions of the American Foundry Society, 107, R.G. Chougule, M.K.Jalan and B. Ravi, Casting Knowledge Management in a Virtual Foundry Environment, 19th International Conference on CAD/CAM, Robotics and Factories of the Future, Kuala Lumpur, July B. Ravi and D. Joshi, Feedability Analysis and Optimisation Driven by Casting Simulation, Indian Foundry Journal, 53(6), 2007, B. Ravi and D. Joshi, Comparison of Finite Element and Vector Element Method for Solidification Simulation, Indian Foundry Journal, 54(9), 2008, D. Joshi and B. Ravi, Evaluating Alternative Feeder Design Layouts for an Industrial Steel Casting Using Vector Element Method, Proceedings of the 57 th Indian Foundry Congress, Kolkata, Institute of Indian Foundrymen, Feb B. Ravi, D. Joshi, and K. Singh, Part, Tooling and Method Optimisation Driven by Castability Analysis and Cost Model, Proceedings of the 68 th World Foundry Congress and 56 th Indian Foundry Congress, Chennai, 7-10 Feb B. Ravi, Casting Simulation and Optimisation: Benefits, Bottlenecks, and Best Practices, Indian Foundry Journal, 54(1), 2008, B. Ravi and D. Joshi, 10-Year Survey of Computer Applications in Indian Foundry Industry, Indian Foundry Journal, 56(1), 2010, B. Ravi and others, Casting simulation case studies, Centre for Education & Training, Institute of Indian Foundrymen, Kolkata, B. Ravi, Casting Simulation Best Practices, Lead paper, Proceedings of the 58 th Indian Foundry Congress, Ahmedabad, Institute of Indian Foundrymen, 5-7 Feb D. Joshi and B. Ravi, Minimizing Porosity in Cast Junctions, Metal Casting Design and Purchasing, Jan-Feb 2010, B. Ravi, A Holistic Approach to Zero Defect Castings, Proceedings of the 59 th Indian Foundry Congress, Chandigarh, Institute of Indian Foundrymen, Feb

15 22. M. Sutaria, D. Joshi, M. Jagdishwar, and B. Ravi, Feeding System Design and Evaluation using Temperature Gradient (Feed Path) Maps, Transactions of the American Foundry Society, 119, M. Sutaria, D. Joshi, M. Jagdishwar, and B. Ravi, Automatic Optimization of Casting Feeders using Feed-paths Generated using VEM, Proceedings of ASME International Mechanical Engineering Congress, Denver, USA, Nov M. Sutaria, V.H. Gada, A. Sharma, and B. Ravi, Computation of Feed-paths for Casting Solidification using Level-Set-Method, Journal of Materials Processing Technology, 212, 2012, M. Sutaria and B. Ravi, Gradient Vector Method for Computing Feed-paths and Hotspots during Casting Solidification, 37 th MATADOR Conference, Manchester, UK, July M. Sutaria and B. Ravi, Computation of Casting Solidification Feed-paths using Gradient Vector Method with Various Boundary Conditions, Int. J. Advanced Manufacturing Technology, in review. 27. R. Sasikumar, E. Jacob, S. Savithri, M. Kumar and M. Ravi, Computer Simulation of Solidification processes, Metals Materials and Processes, 13, 2001, B. Ravi, Technical Manpower Training for Foundries: NKN E-Foundry Mission, Proceedings of the 60 th Indian Foundry Congress, Bangalore, Institute of Indian Foundrymen, 2-4 Mar