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 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.
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.
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
Using stochastic approach to check that the given process is robust or not.
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
Gravity casting: Riser optimization