Parametric Studies &
Design of Experiments
Master the art of exploring multiple metallurgical scenarios virtually. Move beyond trial-and-error by systematically varying parameters to discover the perfect casting process before pouring a single drop of metal.
In traditional foundry environments, optimization is a grueling, physical process. A misrun occurs; the pouring temperature is arbitrarily raised. Shrinkage is detected; a riser is enlarged based on heuristic guesswork. This sequential, physical trial-and-error is not only economically ruinous due to wasted material, energy, and labor, but it is fundamentally unscientific. It observes the symptom without holistically mapping the problem space.
Enter the virtual Design of Experiments (DOE). Utilizing advanced platforms like PoligonSoft, metallurgists and casting engineers can execute parametric studies. This methodology involves identifying critical variables—such as pour temperature, gating velocities, mold pre-heating, and cooling conditions—and systematically simulating combinations of these variables across a defined multidimensional matrix.
By doing so, we transition from asking "Did this one change fix the defect?" to asking "What is the exact mathematical relationship between variable X and outcome Y, and where is the absolute optimal operating window?" This exhaustive guide will walk you through setting up, running, and analyzing a complete virtual DOE.
Phase 1: Defining the Parametric Space
The foundation of any successful DOE is the rigorous selection of variables. In a PoligonSoft parametric study, you are not simply running a simulation; you are programming a scenario generator.
Consider a high-value steel casting suffering from borderline microporosity and occasional misruns. Physical intuition suggests hotter metal might cure the misrun, but could exacerbate gas solubility and volumetric shrinkage. To test this virtually, we establish a parameter matrix. Instead of running one model, we instruct the software to permute through a range of designated inputs.
Common Permutation Variables:
Interactive Simulation Matrix Builder
Select parameters to vary. Watch how the total number of required simulations scales multiplicatively.
Phase 2: Automated Batch Execution
Once the parametric matrix is established, the computational burden begins. If a single complex thermal-fluid coupled simulation takes 4 hours to solve, a matrix of 12 permutations represents 48 hours of compute time. Manually setting up and triggering each of these 12 runs sequentially is inefficient and prone to human error.
Modern simulation environments overcome this through Batch Processing. Whether utilizing built-in macro scripts or dedicated batch runner utilities found in tools like PoligonSoft, the engineer configures the matrix once. The system then automatically duplicates the mesh, applies the unique boundary conditions for iteration n, executes the solver, saves the result data into a structured directory, and immediately begins iteration n+1. If hardware allows (e.g., multi-core HPC or cluster nodes), these jobs can run in parallel.
The Batch Execution Pipeline
Phase 3: Comparative Analytics & Visualization
Executing the batch is merely the computational prerequisite. The true value of a parametric study lies in the post-processing and comparative analytics. Reviewing a dozen 3D color plots individually is cognitively overwhelming. We must abstract the 3D data into 2D plots to identify trends.
By extracting specific metrics from the solver—such as Total Macro-Porosity Volume (cm³) or Percentage of Casting with Niyama Criterion < Threshold—we can plot these outputs against our input parameters. This visualizes the physical response curves of the casting. In the interactive chart below, explore how altering the Pouring Temperature affects Total Porosity, and how that entire curve shifts when the gating design (choke area) is modified.
Defect Sensitivity Analysis
Total Porosity Volume vs. Pouring Temperature
Phase 4: Multi-Objective Optimization
Finding the "best" parameter is rarely straightforward because foundry engineering involves conflicting objectives. A higher pouring temperature might reduce the risk of a misrun to near zero, but it simultaneously increases mold degradation, energy costs, and potentially the size of the required riser (due to higher total volumetric shrinkage).
This is the domain of Multi-Objective Optimization. Advanced users of platforms like PoligonSoft look for the Pareto Frontier—the set of parameter configurations where you cannot improve one metric (e.g., porosity) without worsening another (e.g., yield). For instance, automatic riser placement algorithms can iterate through hundreds of positional coordinates and modulus sizes to find the absolute minimum riser mass required to achieve a sound casting under specific thermal constraints.
Optimal Zone Reached!
Yield > 65% | Misrun = 0% | Soundness > 98%
⚙️ Real-time Parameter Tuning
The Virtual Paradigm Shift
The power of simulation is not merely in verifying a single design, but in providing an infinitely malleable virtual sandbox. Physical design of experiments involves making costly patterns, melting physical metal, pouring, destructive testing, and waiting weeks for results. It restricts innovation because failure is too expensive.
By adopting parametric studies within environments like PoligonSoft, failure becomes a cheap, virtual data point. An engineer can evaluate 50 configurations overnight. They can discover non-intuitive solutions—perhaps identifying that a slightly smaller riser, combined with a 10°C drop in temperature and a faster fill time, produces a completely sound casting with a 15% improvement in yield. This level of optimization is mathematically impossible to achieve on the foundry floor through human intuition alone.
Stop guessing. Start simulating.