MV3022: Optimize a 4Bar Model
In this tutorial you will setup an optimization problem using MotionView's Optimization Wizard for a 4bar model.
 Defining point coordinates as design variables
 Defining a response type 'Root Mean Square Deviation' for matching curves
 Using the responses as objectives
 Running the optimization and postprocessing the results
 [Optional] Infeasible Designs in MotionSolve optimization
 Introduction
 In this tutorial, the locations of the joints of a 4bar mechanism are
optimized to obtain a desired motion of the coupler. MotionSolve's DSA (Design Sensitivity Analysis)
capability is used to calculate sensitivities.
The entire model is parameterized in terms of these four design
points: A, B, C and D. Since the model operates in 2D space (XY plane),
this leads to 8 Design Variables. The initial design is listed as in Table 1:
Table 1. Point X Y A 45 45 B 65 260 C 300 500 D 515 85
Review the Model
In this step, you will review the 4bar model in MotionView.
 Open the file mv_3022_initial_4bar_opt.mdl in MotionView.

Review the entities in the model.
Add Design Variables
Add Response Variables
Now you will add response variables to the optimization.
 Trajectory of Coupler CM – DX
 Trajectory of Coupler CM – DY
Add Objectives and Constraints
In this step you will add two objectives to the problem.
Run the Optimization
Now you will run the optimization.
PostProcess
In this step you will postprocess the results of the optimization run.
Identify Infeasible Designs/Possible Recovery (Optional)
In this step, you will use a powerful feature in MotionSolve to obtain the optimal design by recovering from a failed iteration.
It is not uncommon for a mechanical system to be limited by some sort of physical constraints. In this four bar example, the Grashof constraints have to be satisfied in order to make the input link a crank. When optimizer chooses a new design, those constraints are likely to be violated regardless of whether they are added as optimization constraints or not. The optimizer in MotionSolve has a recovery mode to help recover from such infeasible design and let optimization proceed. To demonstrate how the optimizer recovers in this tutorial, you will change the initial design and see how it works.