From aircraft to cars, industrial machinery, ships, and white goods, complexity of products and the systems that keep them running is steadily increasing. CATIA enables you to model and compose complex products, while defining and executing their driving systems to manage this complexity and deliver products on time?
CATIA Product Engineering Optimization
Accelerates design alternative exploration and optimization for better design in less time
Product designs that continue to use physical prototypes are more prone to errors, delays, and increased costs. However, choosing a solution with built-in knowledge ensures your product conforms to various standards, helping you to work faster and design right.
CATIA Product Engineering Optimizer provides an interactive environment for modeling and solving complex engineering problems. Early in the conceptual design stage, it is often necessary to find solutions to an engineering problem by trying to satisfy a set of engineering constraints, while fulfilling the requirements for the product definition and behaviors. CATIA Product Engineering Optimizer allows users to model those constraints as a set of equations and inequations, as well as to interact with the model in order to discover solutions to a given problem.
- Perform geometry-based optimizations.
- Run finite element-based optimizations.
- Execute “Design of Experiment” techniques to provide useful information about the product's behavior to help solve compromises.
- Capture optimizations within the model’s feature tree to launch the optimization again after any design changes.
- Solve problems with constraints satisfaction.
Accelerate exploration of design alternativesWith the Design of Experiment (DOE) function, users can quickly and easily carry out virtual design experiments and test a variety of parameters at any stage of the design process. Using an interactive panel, the user selects the parameters to alter and observe. The number of input and output parameters allowable is infinite. This panel also guides the designer through the various validation stages to obtain a result, which will ultimately lead to the final design. Define cause-effect relationships between parameters. The DOE function allows the designer to perform a large number of experiments and the software determines the relationship between the various parameters based on the experimental results. Thus, it guides the user towards improvements and provides accurate predictions of values. All of the results can then be backed up for future re-use. Identify key parameters. From these experiments, the designer can learn which parameters are the most influential, facilitating better alternative design searches and optimizations.
Multi-discipline and Multi-goal design optimizationCapture optimization intent through a variety of interactions. Parameters can be specified as a target for optimization with different possible outcomes. Constraint satisfaction can be mixed with any of the other optimization parameters to perform a multi-goal design optimization. Some parameters can be selected as free and their domains can be constrained by range. Users can not only specify parameter ranges (minimal and maximal boundaries) to be considered during optimization but also constraints to be fulfilled by the optimal solution provided. All the information used is saved as a feature and can be reused later.
Multiple optimization options and computation termination criteriaTwo algorithms are available, the simulated annealing and conjugate gradient. Users can employ both to iterate optimization. Termination criteria can be defined to stop evolutions. Both algorithms consider time and maximum number of evolutions. The number of evolutions without improvement affects simulated annealing, while precision is only relevant for the gradient. In any case, termination criteria have default values that can work in most circumstances.
Progress bar displayed with optimization information during the optimizationThe user can stop optimization at any time if the resulting solution is convenient or reached before the time limit. Users get immediate feedback during optimization, including convergence, update counts and geometry changes in the 3D view.
Real-time feedbackReal-time feedback and customizable outputs deliver an immediate assessment of the optimization. The initial result is provided by a dialog box that allows validation and direct application of new values in design. If needed, optimization data can be stored in text or Excel files. The graphical editor allows visualization of the evolution of variables and the objective value through curves, with different color scales. Users can save the result and restart from this point for the next optimization.
Batch mode capabilityUsers can run an optimization in batch mode. This can be done through the automation of the optimization feature (VB access), and is especially useful for time consuming problems.
PLM integrationAll of the knowledge relations including Optimizations, Design of Experiments and Constraint Satisfactions can be stored in engineering specification representations associated to the product managed in ENOVIA VPLM. Users can then take advantage of the ENOVIA V6 VPM capabilities in terms of storage security, BOM management, versioning, impact management, rerouting of relationships, and concurrent engineering while keeping all the knowledge previously captured in the design.