Casting Solutions. Intelligent Software for Modern Foundries

Transcription

1 Casting Solutions Intelligent Software for Modern Foundries

2 Intelligent Software The SOLIDCast family of products assists the foundry in all stages of process design: 1. Initial conceptual design of gates and feeder systems can be performed in just minutes using the Gating Design Wizard and the Riser Design Wizard. 2. Complete, accurate CFD and FDM simulation of flow, heat transfer, solidification and volumetric change is then used to verify the design. 3. Multi-Variable Response Surface Optimization can then be used to maximize both part quality and yield, in a completely automated process utilizing multiple simulations. 4. Compatibility with modern production concepts such as Industry 4.0

3 Intelligent Software In recent years, computer simulation of the casting process has become increasingly widespread; however, a significant drawback of many such programs is they require an initial design of gating and feeding systems to simulate. It is ironic that many foundries, even those using the most sophisticated simulation software still use a traditional approach based on experience or simple calculations to develop the initial design to submit for simulation.

4 Intelligent Software In more recent times there is an increasing tend by the producers of simulation software to develop features that accurately determine the position and size of feeders and gating systems prior to running an initial simulation. The starting point for this development is the idea that the modulus approach is essentially an attempt to estimate solidification times of various parts of the casting. Using modern casting simulation software, the solidification time of every point within a casting can be calculated very quickly using the Finite Difference Method and does not need to be estimated.

5 Intelligent Software After determining solidification times of various casting sections and knowing the volume of such sections the software is able to automatically determine the correct size and position of feeders. Using this methodology, what amounts to an almost automatic calculation of required risering is achieved.

6 Intelligent Software Having determined the rigging requirements, the software is generally capable of adding features such as feeders, gating components, chills etc, simply by selecting the appropriate parts from a library of preformed shapes stored within the software and nominating the location on the casting model with a click of the mouse.

7 Intelligent Software This first example focuses on the use of casting simulation software as a practical tool for the foundry to develop an initial casting method design and easily build a model for simulation.

8 Computer modeling of the casting process has become widely accepted in industry

9 Based on an initial naked simulation, modern systems are capable of recommending the position and size of feeders, as well as gating requirements.

10 Model of Proposed Casting Method

11 Simulation system pours, solidifies & examines results of design

12 However How do we know that OPTIMUM process designs are actually being achieved?

13 What do we mean by OPTIMUM? The highest quality casting at the lowest cost. The most efficient way to produce a casting.

14 The current process

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16 A new paradigm

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18 What is Optimization? Optimization is a mathematical method for finding the best solution to a given problem. Automates the search for a design solution Frees the engineer s time Provides a more thorough and repeatable design process.

19 Steps Required for Optimization 1.) Develop an Initial Design. 2.) Define three types of elements: Design Variables Constraints An Objective Function 3.) Launch the Optimization

20 Design Variables These are elements that are allowed to vary when the computer is searching for an optimum process design. Examples: Height and diameter of a feeder (riser) Size of a feature on the casting Pouring temperature Shell preheat temperature

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22 Another Type of Design Variable: Initial Temperature

23 Constraints A constraint is some aspect of a design that determines whether that design is acceptable or not. Typical Constraints: Macroporosity Level Microporosity Level Yield Percentage Minimum Cooling Rate Minimum Thermal Gradient

24 The Objective Function The Objective Function is the single result which you are trying to either maximize or minimize. Typical Objective Functions: Maximize Yield Percentage Minimize Macroporosity Level Minimize Microporosity Level Maximize Cooling Rate Maximize Directional Solidification

25 The Optimization Process The Optimization Engine varies each Design Variable within the Design Space to create a series of process models. Each design is evaluated as to whether it violates any Constraint. Each design is then evaluated to determine if the Objective Function has been achieved, through the use of convergence criteria.

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27 Requirements of the Modeling System for Application of Optimization Must be able to automatically modify geometry Must be able to handle shape interference as shapes are modified Must be able to automatically re-mesh each design Must be able to process multiple simulations as quickly as possible

28 A Simple Example Let s look first at a very simple example a casting with a single top riser.

29 Now, we tell the system that we want to optimize this casting by selecting Create New Optimization Project from the Mesh menu.

30 This creates a blank Optimization Project. All we have to do is fill in the blanks. First, we select the shapes which comprise the riser, and designate these as a Design Variable by clicking on the Add Variable button.

31 The Vertical Scale and the Horizontal Scale of the riser are now Design Variables. In this example, we are allowing these to vary up to 1.5 times or as low as 0.5 times our initial design.

32 Now we need to select a Pin Point. This is the attachment point of the riser to the casting. This point will remain at a constant position while the dimensions of the riser are scaled up or down. To select the Pin Point, we can hide the casting and click on the bottom center of the riser. This establishes our attachment point for this geometric feature.

33 For this example, we have only one riser to design. This means that we have two design variables: the height and the diameter of the riser.

34 We could also select other items of process data such as the fill time or the initial temperature of the casting or mold materials as design variables. (In this simple case we won t select these.)

35 Now we may want to specify a constraint. As you can see, there are numerous items that we could pick. Here, we have selected Material Density (a measure of macro-shrinkage porosity) as a constraint, with a Constraint value of 1. If we can achieve this, our casting will be substantially free from macro-shrinkage.

36 Finally, we select an Objective Function. In this case, we have elected to maximize the yield. In effect, we re telling the system to find the smallest riser which produces a casting with no macro-shrinkage.

37 Later, when the Optimization Run is complete

38 we can view the results. OPTICast can display a series of graphs to show us how it arrived at the final result. To view the graphs, we select View Graphs from the menu.

39 The first graph shows the value of the Objective Function for each simulation which was run. In this case, we started with a yield of about 58% and ended up with a yield of about 70% after 25 simulations were run.

40 This graph shows the values of the constraint (Material Density) for each run. The final value was , which indicates a sound casting.

41 Here the system has plotted the values which it tried for the Vertical Scale (the height) of the riser. The final value was 62% of the original value.

42 And a final plot shows the Horizontal Scale values which were tried. The riser ended up about 94% of its original diameter.

43 The result?

44 Pre OPTICast. After OPTICast. After OPTICast Macro-porosity plot Process yield was increased about 12%, and quality was maximized, in 25 simulations run automatically by OPTICast.

45 Stainless Steel Investment Casting

46 A 3-D solid model of the cast shape is imported into the system and the alloy type and mold material are selected from the database.

47 The system can automatically apply the ceramic shell mold of the required thickness and preheat temperature.

48 A naked solidification simulation is then run.

49 From the naked simulation, the system can identify the feed areas and recommend feeder bar sizes based on the solidification thermal modulus.

50 The recommended gating and feeding components are modeled and again the system can automatically add the ceramic shell to the required thickness and preheat temperature.

51 The software then pours and solidifies the casting.

52 . and examines the results. These plots are of macro-porosity at a value of 1 and indicate the casting is free of macro-porosity.

53 Process Design Optimization for: Stainless Steel Investment Casting Pour Temperature: 1,580ºC Shell Preheat: 1,000ºC Shell Thickness: 6 mm Alloy: SS Grade 304

54 The feeding and gating system are to be optimized. The various components (pouring cup, cross-runner and contacts) are modeled separately so that the geometry of the parts can be varied independently. Pouring cup highlighted on right.

55 Cross runner on left and 4 individual contacts on right.

56 The Design Variables are highlighted and added individually. The Pouring Cup is the first DV and we are allowing the height to reduce to 0.5 and diameter to reduce to 0.75 the original sizes. Note Cup protrudes into Cross-Runner

57 The Cross-Runner is the second DV and we are allowing the height to reduce to 0.5 the original size but the horizontal scale to remain unchanged.

58 One of the 4 Contacts is the third DV and we are allowing the diameter to reduce to 0.5 the original size but the height to remain unchanged.

59 We now select Linked Geometric Design and individually select the 3 remaining Contacts and Link them to Contact 1 so that they change in response to changes in Contact 1. Each Contact must have its individual Pick Point selected.

60 We now select Linked Geometric Design and individually select the 3 remaining Contacts and Link them to Contact 1 so that they change in response to changes in Contact 1. Each Contact must have its individual Pick Point selected.

61 We now select Linked Geometric Design and individually select the 3 remaining Contacts and Link them to Contact 1 so that they change in response to changes in Contact 1. Each Contact must have its individual Pick Point selected.

62 The Objective Function is selected as: Maximization of Yield.

63 The Constraint is selected as: Minimum Material Density of 1. i.e. No macro-porosity accepted.

64 The Simulation Parameters are: 1. Single Cycle 2. Stop Point 100% Solidified 3. View Factor Calculation run on each mesh

65 Model Data Design is not selected as a DV

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67 Later, when the Optimization Run is complete

68 The first graph shows the value of the Objective Function for each simulation which was run. In this case, we started with a yield of about 42% and ended up with a yield of about 54% after 25 simulations were run.

69 Here the system has plotted the values which it tried for the Vertical Scale (the height) of the Cup. The final value was 69% of the original value.

70 Here the system has plotted the values which it tried for the Vertical Scale (the height) of the Cross-Runner. The final value was about 69% of the original value.

71 The Vertical Scale (the height) of the Contacts was selected to remain unchanged.

72 This graph shows the Horizontal Scale values which were tried for the Pouring Cup. The final value is about 84% of its original diameter.

73 The Horizontal Scale of the Cross-Runner was selected to remain unchanged.

74 This graph shows the Horizontal Scale values which were tried for the Contacts. The final value is about 72% of the original diameter.

75 This graph shows the values of the constraint (Material Density) for each run. The final value was 1 which indicates the casting is free of Macro-Porosity.

76 The result?

77 Pre OPTICast. After OPTICast. After OPTICast Macro-porosity plot Process yield was increased about 12%, and quality was maximized, in 25 simulations run automatically by OPTICast.

78 Compacted Graphite Diesel Engine Crank Case Casting

79 Compacted Graphite Iron Crank Case Existing casting method

80 Stages of Mold Filling

81 Solidification Simulation

82 X-Ray plot of Material Density at a value of 1, indicates casting is free of macro-porosity

83 Casting is sound, however, can yield be improved by reducing the size of highlighted feeders?

84 Highlighted Feeder is selected as a Geometric Design Variable. Lower Limit of Vertical & Horizontal Scales are allowed to reduce to 0.

85 Highlighted Feeder is selected as a Linked Geometric Design Variable to Riser 1.

86 Highlighted Feeder is selected as a Linked Geometric Design Variable to Riser 1.

87 Highlighted Feeder is selected as a Linked Geometric Design Variable to Riser 1.

88 Highlighted Feeder is selected as a Linked Geometric Design Variable to Riser 1.

89 The Objective Function is selected as: Maximization of Yield.

90 The Constraint is selected as: Minimum Material Density of 1. i.e. No macro-porosity accepted.

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92 Later, when the Optimization Run is complete

93 The first graph shows the value of the Objective Function for each simulation which was run. In this case, we started with a yield of about 49% and ended up with a yield of about 56% after 16 simulations were run.

94 Here the system has plotted the values which it tried for the Vertical Scale of the feeders. The final value was 0.07% of the original value.

95 Here the system has plotted the values which it tried for the Horizontal Scale of the feeders. The final value was 0.07% of the original value.

96 This graph shows the values of the constraint (Material Density) for each run. The final value was 1 which indicates the casting is free of Macro-Porosity.

97 Solidification Simulation of Optimized Design. Feeders have been removed.

98 X-Ray plot of Material Density at a value of 1, indicates casting is free of macro-porosity

99 Process yield was increased about 7%, and quality was maximized, in 16 simulations run automatically by OPTICast.

100 Conclusion The application of optimisation technology to solidification modelling offers the potential to add intelligence to the casting modelling process, by allowing the system to make decisions about modifying an initial design until a given objective is achieved, without human intervention. By reducing the amount of trial-and-error that is necessary in traditional modelling, the foundry engineer s time can be devoted to other critical aspects of the process.

101 Thank You Done for the day