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Silver Jubilee Seminar

Institute of Indian Foundrymen (Indore Chapter), 13 March 2008, Indore

Computer-aided Casting Method Design, Simulation and Optimization

Dr. B. Ravi, Professor

Mechanical Engineering Department, Indian Institute of Technology, Bombay

Powai, Mumbai 400076 b.ravi@iitb.ac.in

Abstract Zero shrinkage defect castings have become a reality owing to computer-aided design, simulation and optimization of their method layout (feeder and gating system). The latest generation software AutoCAST developed at IIT Bombay provides a host of functions to support method engineers, including part thickness visualization, core design, multi-cavity mold design with common gating and feeding, application of various feedaids (feeder sleeves, chills, padding, etc.), simulation of mold filling and casting solidification, automatic optimization of feeders and gating driven by the desired quality level, and what-if cost analysis. Improvements in graphical user interface and hardware have made the software extremely user-friendly and fast: foundry engineers can learn it within a day, and most castings can be simulated within an hour. Local support ensures successful integration of the technology into the current practices of a foundry. The technological as well as practical aspects of using casting software are illustrated with an industrial case study. Introduction The method layout of a casting is an important aspect of tooling development. It involves decisions regarding part orientation in mold, parting line, cores, cavity layout, feeders, feedaids and gating system. An improper method layout leads to either poor quality or low yield, affecting manufacturing costs and productivity [1]. At present, method design is carried out manually, using 2D drawings of the cast part. Then tooling is fabricated, trial castings are produced in the foundry, and inspected. If sample castings contain defects (such as shrinkage porosity, sand inclusions and blow holes), then the method layout is modified and the process is repeated. Each iteration can take up a week or more, and affects regular production. After 3-4 iterations, the foundry may resort to ‘safe’ method design (implying low yield), or continue with high rejection rates (implying high scrap or repair cost). This is especially true in the case of large castings, where the cost of a trial can be prohibitive. Assuming a typical foundry develops 20 new castings every year, each casting requiring at least 2 trials, and the average cost of each trial (pattern modification, pouring, inspection, lost production) as Rs.25,000, the economic loss of trials works out to be one million rupees per year per foundry. Further, taking the average difference in the price of a saleable casting and scrap metal as Rs.20/kg, and assuming average rejections in a foundry as 5%, the economic loss caused by defective castings works out to be Rs. 1000 per ton of production. Given that there are about 5,000 foundries in India producing 7 million tons of castings, the total avoidable economic loss of all Indian foundries works out to be Rs. 12 billion (1200 crores) per year.

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Casting simulation can overcome the above problems: virtual trials do not involve wastage of material, energy and labour, and do not hold up regular production. However, most of the simulation programs available today are not easy-to-use, take as much time as real trials, and their accuracy is affected by material properties and boundary conditions specified by the user. The biggest problem is the preparation of 3D model of the casting along with mold, cores, feeders, gating, etc., which requires CAD skills and takes considerable time for even simple castings. The AutoCAST software developed at I.I.T. Bombay in collaboration with Advanced Reasoning Technologies provides a single integrated easy-to-use environment for casting method design, solid modeling, and simulation [2]. The latest Release 10 incorporates multi-cavity mold layout, automatic modeling and optimization of method design, and a costing model to compare various layouts. Its key features are described here, illustrated by an industrial case study of a steel valve casting. Computer-aided Method Design The main input is the 3D CAD model of an as-cast part (without drilled holes, and with draft, shrinkage and machining allowance). The model file can be obtained from the OEM firm, or created by a local CAD agency. Various display options such as pan, zoom, rotate, transparency, and measure are provided to view and understand the part model (Fig.1). The cast metal and process are selected from a database. Part thickness distribution is displayed for verifying the model and evaluating part-process compatibility (Fig.2). Fig.1 Part property computation. Fig.2 Part thickness distribution. The method design involves cores, feeders and gating system (Fig.3). Holes in the part model are automatically identified for core design. Even intricate holes can be identified by specifying their openings. The print length is computed based on the core diameter and length (the user can change these if required), and the entire core model is automatically created. The program suggests the number of cavities depending on the mold size (selected from a foundry-specific library), considering both cavity-cavity and cavity-wall gaps. Then the part model is automatically duplicated in the correct locations as per the desired cavity layout. To facilitate feeder location, the program carries out a quick solidification analysis and identifies feeding zones. The user selects a suitable location close the largest feeding zone, and the program automatically computes the dimensions of the feeder using modulus principle (solidification time of feeder slightly more than that of the feeding zone). The feeder model is automatically created; the user

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can change its dimensions or apply feedaids such as insulating sleeves and exothermic covers. More feeders can be created by specifying their positions. The gating channels are created semi-automatically. First, the user indicates gate positions on the part or feeder model. Then the sprue position is decided, and it is connected to the gates through runners. Runner extensions are automatically created. Any type of gating system: horizontal, vertical, investment tree, and direct pouring can be created or modified within minutes. The program also suggests a suitable filling time (which can be changed by the user), accordingly computes the dimensions of the gating channels, and creates their solid model. Fig.3 Method design and model. Fig.4 Melt jet path and mold filling. Automatic Optimization The mold cavity layout, feeders, and gating are automatically optimized within minutes based on quality requirements and other constraints [3]. For mold cavity layout, the primary criterion is the ratio of cast metal to mold material. A high ratio such as 1:2 (cavities too close to each other) can reduce the heat transfer rate and lead to shrinkage porosity defects. A low ratio such as 1:8 (cavities too far from each other) implies poor utilization of mold material and reduced productivity. The program tries out various combinations of mold sizes and number of cavities to find the combination that is closest to the desired value of metal to mold ratio. The gating design is driven by the ideal mold filling time, which depends on the cast metal, casting weight and minimum wall thickness. Fast filling leads to turbulence-related defects (such as mold erosion, air aspiration and inclusions). On the other hand, slow filling may cause defects related to premature solidification (such as cold shuts and misruns). To optimize the gating design, the program simulates the mold filling and computes the total fill time (Fig.4). A simplified layer-by-layer algorithm is used, taking into account the instantaneous velocity through the gates (considering back pressure, if applicable), and the local cross-section of the mold cavity. This gives an accurate estimation of filling time, while being computationally efficient. If the difference between the ideal and simulated filling time is more than a specified limit, the program automatically changes the gating design, creates its solid model, and simulates the mold filling again. The feeder optimization is driven by casting quality, defined as the percentage of casting volume free from shrinkage porosity. The user indicates a target quality. The program automatically changes the feeder dimensions, creates its solid model, carries out solidification simulation, and estimates the casting quality (Fig.5). The solidification simulation employs the Vector Element Method, which computes the temperature gradients (feed metal paths) inside the casting, and follows them in reverse to identify the location and extent of shrinkage porosity (Fig.6). This has been found to be much faster

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than Finite Element Method, without compromising the accuracy of results. The feeder design iterations are carried out until the desired quality is achieved, or the number of iterations exceeding a set limit. The user can accept the results, or reject them and modify the feeder design interactively. Finally, the cost of the casting is computed in terms of amortized tooling, cast metal, other materials (mold, core, etc.), energy, and labour. The user can carry out ‘what-if’ analysis and compare different method layouts in terms of manufacturing cost. A detailed method report containing the dimensions of part, mold, core, feeders, gating, various results (yield, quality, cost), and an image of the entire casting is automatically generated, which can be printed or stored for future reference (Fig.7). Fig.5 Casting solidification simulation. Fig.6 Feed metal paths (gradients).

Fig.7 Cost analysis and method report. The software has been developed for standard Windows XP and Vista computers. The graphical user interface is designed to minimize the learning and operation time, and the user is gently guided through forgotten or wrong steps. Even those without any prior exposure to computers are able to use the software after a single day of training. All steps starting from part model importing to mold, core, feeder and gating system design, simulation and optimization can be completed within one hour for typical castings. Over 40 foundries regularly use the software today, and many others have engaged consulting services for troubleshooting critical castings in all major metals and processes. Direct benefits include at least 50% reduction in casting development time and shrinkage porosity defects. Other benefits include yield improvement, faster quotations, taking up more complex projects, ready reference and training [4]. Continuous interaction and feedback from industry over the last 20 years has helped evolve the software to its current state in terms of features, applications and benefits to the end-users.

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Conclusion Casting simulation can minimize the wastage of resources required for trial production. In addition, the optimization of quality and yield implies higher value-addition and lower production cost, improving the margins. For widespread application, simulation programs must be fast, reliable, and easy to use. This has been achieved by integrating method design, solid modeling, simulation and optimization in a single software program, and automating many tasks that otherwise require computer skills. In many benchmarking exercises, the software has proven its reliability in predicting internal defects (ex. shrinkage porosity) within minutes, often by senior method engineers who are first time computer users. With payback period as small as 1-3 months, and a network of local support centres, even SME foundries can take advantage of the technology to get their castings right first time, every time, in the shortest possible time. References 1. Ravi B, Metal Casting: Computer-Aided Design and Analysis, Prentice-Hall India, New Delhi,

2005, ISBN 81 203 2726 8.

2. Advanced Reasoning Technologies, AutoCAST software, http://www.autocast.co.in, 2008.

3. Ravi B, Joshi D, Singh K, “Part, Tooling and Method Optimisation Driven by Castability Analysis and Cost Model,” Proceedings of 68th World Foundry Congress, Chennai, Feb 2008.

4. Ravi B, “Casting Simulation and Optimisation: Benefits, Bottlenecks, and Best Practices,” Indian Foundry Journal, 54 (1), Jan 2008.