CAST-DESIGNER Automatic Optimization | C3P Software

CAST-DESIGNER Automatic Optimization

Cast-Designer Automatic Optimizer is a unique software package opening up new opportunities for the market of “complex” practical problems. It is used to improve the performance of complex systems, technical facilities and technological processes and to develop new materials based on a search for their optimal parameters.

Cast-Designer optimizer can execute multi-criteria non-linear optimization based on Genetic Algorithm as known in Artificial Intelligence.

Multiple Criteria Targets: All physical problems could be optimized, including the flow, thermal, stress, warpage, microstructure, material properties etc. For Example: Single or multiple targets of the simulation, what the foundry engineer is trying to achieve like to maximize the yield, minimize shrinkage or the minimize gas entrapment and balance the flow during the filling process.

 Multiple Design or Process Variable: Design elements that are allowed to vary, could be the Parametric CAD Geometries like runner dimensions, inner gate locations or riser diameter, even original casting part, or process parameters, like pouring temperature, velocity, or the HTC.

 CAD driven optimization: All geometry dimensions could be optimized whatever it was parametric geometry or not or STL dataset. This is unique.

 Genetic Algorithm in Cast-Designer Optimizer, checks the results of each optimization iteration like shrinkage porosity volume and intelligently sets the new value for the design variables like size and location for the riser. Such iterations are continued until the targets are achieved.

 Complex user formulas are supported.

Cast-Designer Automatic Optimization applications

Cast-Designer Automatic Optimization applications

 

Benefits:

 Cast-Designer Optimizer enables lower research expenditures and shorter implementation time. The main purpose of the system is to relieve a designer or researcher of the sufficiently complex and very labour-intensive process of searching for optimal system design parameters which simultaneously meet a great number of sometimes controversial requirements.

Cast-Designer Optimization supports parallel automatic optimization, this is a breakthrough technology to reduce the lead time of optimization.

Cast-Designer Optimization supports parallel automatic optimization, this is a breakthrough technology to reduce the lead time of optimization.

 

Design Of Experiments (DOE)

 Design of Experiment, to study a process window and its robustness so as to obtain a set of ideal parameters which provide good result inside an user defined goal.

 For Example:

Yield:  To find best location and size of risers to minimize shrinkage porosity.

Sensitivity: To identify the sensitivity of the influential process parameters like increasing the mould temperature vs. adding insulation sleeve.

Cost:  Compare costs for maintaining higher melt temperatures vs. rejection/production losses.

 Feasibility: Determine the optimization goals, manufacturable aspects during production, in-line with the known production constraints of a foundry

The Taguchi DOE method provides best possible combination of variations. For Example: out of 512 combinations, it is possible to get desired results by trying 16 combinations

The Taguchi DOE method provides best possible combination of variations. For Example: out of 512 combinations, it is possible to get desired results by trying 16 combinations

Process Robustness

 Using stochastic approach to check that the given process is robust or not.

For Example:

Vary the furnace temperature within the known range to check that the process is robust, find out the maximum and minimum variations allowed in the process while still obtaining the desired good quality parts.

HPDC: Runner optimization

HPDC: Runner optimization

HPDC: Runner optimization

Gravity casting: Riser optimization

 Optimization setup:

Optimization setup:

Optimization setup:

Full combine models:   243 models             Taguchi DOE method:   27 models User defined condition could be applied to above two methods. Setup the basic simulation model in 30 min and 1 hour for optimization setup. The 27 runs token 3 hours in 5 token parallel optimization in 20 core Intel E5 machine.

Full combine models: 243 models Taguchi DOE method: 27 models
User defined condition could be applied to above two methods. Setup the basic simulation model in 30 min and 1 hour for optimization setup. The 27 runs token 3 hours in 5 token parallel optimization in 20 core Intel E5 machine.

To optimize the casting yield ratio and shrinkage porosity of an automotive part. Using the GA method to find the Pareto front points. The X axis was the total riser volume and the Y axis was the shrinkage porosity volume.

To optimize the casting yield ratio and shrinkage porosity of an automotive part. Using the GA method to find the Pareto front points. The X axis was the total riser volume and the Y axis was the shrinkage porosity volume.

 

 

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