💥💥💥 How to generate design of experiment (regression) in Ansys Workbench ?

To generate a design of experiment (regression) in Ansys Workbench, you can follow these steps³:

• 
  Create a parameter set with the input parameters that you want to vary. You can use expressions, variables, or constants to define the parameters.
• In the toolbox, expand Design Exploration and drag Response Surface Optimization to the project schematic. Drop it under the parameter set and connect them.
• Double-click Design of Experiments to open the setup window. Choose Regression as the DOE type and specify the number of design points, the sampling method, and the random seed.
• Click Update to generate the design points and run the simulations for each point. You can view the results in the table or the chart.
• To create a regression equation for the output response, go to the Response Surface tab and click Add. Choose the output parameter that you want to model and select the type of regression model. You can also adjust the model settings and the validation method.
• Click Update to fit the model and evaluate its accuracy. You can view the model equation, the coefficients, the R-squared value, and the residual plots.

What are the types of DOE in Ansys Workbench ?

There are several types of DOE (Design of Experiment) methods available in Ansys Workbench, such as¹:

• Regression: This method uses a mathematical model to fit the data and estimate the coefficients of the input parameters. It can be linear, quadratic, or cubic.
• Optimal Space Filling: This method maximizes the distance between the design points to cover the design space as uniformly as possible. It can use different sampling methods, such as Latin Hypercube, Halton, or Sobol.
• Central Composite Design: This method is a type of response surface methodology that uses a factorial design with center points and axial points. It can be used to fit a quadratic model and estimate the curvature effects.
• Box-Behnken Design: This method is another type of response surface methodology that uses a three-level design with no corner points. It can also be used to fit a quadratic model and estimate the curvature effects.
• Full Factorial Design: This method uses all possible combinations of the input parameters at each level. It can be used to fit a linear model and estimate the main effects and interactions.
• Fractional Factorial Design: This method uses a subset of the full factorial design to reduce the number of design points. It can also be used to fit a linear model and estimate the main effects and interactions, but with some confounding.

You can choose the type of DOE that suits your objectives and parameters. For more information, you can check out this video or this article. 

How do I choose the best DOE method for my simulation?

Choosing the best DOE (Design of Experiment) method for your simulation depends on several factors, such as¹:

• The number and type of input parameters that you want to vary
• The number and type of output responses that you want to measure
• The objective and scope of your study (e.g., screening, optimization, sensitivity analysis, etc.)
• The resources and constraints that you have (e.g., time, budget, computational power, etc.)

There is no one-size-fits-all solution for DOE, but some general guidelines are¹:

• If you have a large number of input parameters and you want to identify the most important ones, you can use a screening design, such as a Plackett-Burman design or a fractional factorial design.
• If you have a moderate number of input parameters and you want to fit a quadratic model and estimate the curvature effects, you can use a response surface design, such as a central composite design or a Box-Behnken design.
• If you have a small number of input parameters and you want to find the optimal settings for the output responses, you can use an optimization design, such as a simplex design or a mixture design.
• If you have a complex or nonlinear relationship between the input parameters and the output responses, you can use a space-filling design, such as a Latin hypercube design or a Sobol design.

You can also use a combination of different designs to achieve your goals, such as a two-stage design or a sequential design¹. 

How do I measure the quality of a DOE method?

There are different ways to measure the quality of a DOE (Design of Experiment) method, depending on the type and purpose of the experiment. Some common criteria are¹:

• Power: The probability of detecting a significant effect when it exists. Power depends on the sample size, the effect size, the significance level, and the design structure. A higher power means a lower chance of making a type II error (failing to reject a false null hypothesis).
• Efficiency: The ratio of the variance of an estimator based on a given design to the variance of an estimator based on an optimal design. A higher efficiency means a lower variance and a more precise estimate.
• Orthogonality: The property of a design that ensures that the effects of any factor or interaction are independent of the effects of any other factor or interaction. An orthogonal design allows for the estimation of all effects without confounding or aliasing.
• Resolution: The degree to which a fractional factorial design can separate the main effects and interactions. A higher resolution means less confounding and more information. Resolution is denoted by a Roman numeral, such as Resolution III, IV, or V.

You can use Minitab Statistical Software to evaluate the quality of your DOE method based on these criteria. For example, you can use the Power and Sample Size tool to calculate the power of your design, or the Evaluate Design tool to compare the efficiency and orthogonality of different designs². You can also check the resolution of your design in the design summary table³.

Can you show me how to use Minitab for DOE analysis?

Sure, I can show you some basic steps on how to use Minitab for DOE (Design of Experiment) analysis. 

• First, you need to create a DOE design in Minitab. You can use the Assistant menu to guide you through the process of choosing and creating a suitable design for your experiment. You can also use the Stat menu to access more advanced design options.
• Next, you need to run your experiment and collect the data. You can enter the data in the worksheet or import it from a file. Make sure that the data columns match the design columns in the worksheet.
• Then, you need to analyze the data and fit a model. You can use the Assistant menu to perform a simple analysis that includes graphs, tables, and reports. You can also use the Stat menu to perform a more detailed analysis that includes diagnostics, tests, and plots.
• Finally, you need to optimize the response and find the best factor settings. You can use the Assistant menu to generate a contour plot or a surface plot that shows the relationship between the factors and the response. You can also use the Stat menu to access more optimization tools, such as desirability functions, prediction intervals, and confidence intervals.

For more information and examples, you can check out these web pages: DOE Analysis - Minitab Engage, Design of Experiments (DOE) - Minitab, and Analyze a DOE with the Assistant in Minitab.

Is Minitab completely free?

Minitab is not completely free, but it offers a free trial for its potential customers. The Minitab free trial comes with all the features of Minitab Statistical Software and has no limitations except for the 30-day time limit¹. You can download the software directly from their website and sign up for an account to access the full range of features available during the trial period².

After the trial period expires, you will need to purchase a license to continue using Minitab. Minitab offers different pricing plans for individuals, students, educators, and businesses. You can check out their pricing page for more details.

Minitab is a powerful and user-friendly statistical software that can help you with data analysis, visualization, and optimization. It is widely used by businesses and organizations for quality improvement, Six Sigma, and Lean projects. If you are interested in learning more about Minitab and its features, you can check out this video² or this article³.