Optimization is the search for one or more better solutions to a certain problem. Within this sector, an optimizer is a software able to identify, suggest and eventually verify the ideal set of input variables that provides the best design solutions among all those possible.
In most cases, the underlying relationships between the control parameters (called inputs) and the measured performances (called outputs) are unknown or difficult to solve. Sometimes, moreover, in order to obtain the answer of the system it is necessary to use complex numerical models that require a lot of time in order to be able to produce the desired output: a typical example is that of the use of simulators of foundry process, in which the result of the simulation, in function of the chosen parameters, is the fruit of a long and complex calculation of 3D thermofluid dynamics.
The IMPROVEit optimization software is able to interface with multiple applications, including the FLOW-3D® CAST (Flow Science inc.) process simulator, and connect them together to completely define a workflow that can be run repeatedly and automatically in order to get the best solution in the shortest possible time, understanding the nature and complexity of the problem.
Case study: Optimization of the injection phase
In this case study, courtesy of FORM S.r.l., during the design of the moulding for battery covers by HPDC, many areas were found in the structure where the amount of porosity from gas was high. It was therefore decided to use the optimization with the aim of reducing defects by acting on the design of the casting channels and optimizing the speed of the piston. For our purposes, the workflow inputs chosen were the values of the piston speed curve in the first phase and a wide range of geometric parameters of the channels managed by interaction between optimizer and parametric CAD software, while the objectives were the best calibration of the arrival of the metal at the casting connections and the reduction of the amount of air trapped in the alloy during this first phase of filling. The flow is structured as follows: the optimizer interacts directly with a parametric CAD software to automatically change the shape of the casting channels and then exports the geometries in STL format; the latter are then used by the process software to simulate the filling, after which the desired outputs are extracted and processed.
When there are two objectives to evaluate at the same time, it is possible to find a series of different optimal results of compromise between the two outputs sought, which is called front of Pareto. Since a workflow cycle takes an average of about 20 minutes, it was decided to perform the optimization on a total of 20 calls.
On the basis of these calls, the chosen configuration is positioned in the center of the Pareto front and therefore presents a good compromise to have a low and most uniform possible arrival time at the casting attacks, 10% better than the initial setup, and at the same time obtain a minimum quantity of trapped air, 13% lower than the initial data.
This case study therefore shows how the automation and numerical optimization of product design, simulation, interpretation of results and changes, help to save a lot of time and how it is possible to achieve important improvements even in the face of a limited number of calls.