Design / process optimization

Design and Process Optimization: Automating Engineering Workflows

In modern engineering, designing an optimal product requires the simultaneous use of multiple software platforms. Moving from Computer-Aided Design (CAD) to meshing, and finally to advanced Computer-Aided Engineering (CAE) solvers often demands significant time just to transfer data and results between programs.

This manual transfer is not only time-consuming but also highly susceptible to human error. To overcome this bottleneck, engineering teams rely on Process Integration and Design Optimization (PIDO) software. PIDO solutions automate the flow of data between different engineering tools, allowing for the seamless execution of complex design optimizations without the need for manual coding or intervention.

The Power of iSight Software

SIMULIA iSight stands out as a premier PIDO software designed to integrate a wide array of engineering applications. By creating a unified simulation workflow, iSight empowers engineers and researchers to make their simulation processes highly flexible.

Instead of running single simulations one by one, iSight automates the exploration of the design space. It systematically adjusts design parameters to determine the most optimal configuration for peak performance, drastically reducing the product development cycle.

Advanced Design of Experiments (DOE)

Before diving into full optimization, it is crucial to understand the behavior of the design space. Design of Experiments (DOE) helps engineers identify which variables have the most significant impact on product performance. iSight offers a robust suite of DOE methodologies, complete with built-in visualization tools to analyze the results effectively:

• Traditional Methods: Full Factorial, Fractional-Factorial

• Response Surface Methods: Central Composite, Box-Behnken

• Space Filling Methods: Latin Hypercube, Optimal Latin Hypercube

• Additional Techniques: Orthogonal Array (Taguchi), Dependent Variable Sampling, and basic Parameter Studies.

Robust Optimization Algorithms

Once the key variables are identified, iSight employs powerful algorithms to find the absolute best design. Depending on the complexity and nature of the problem, you can define the optimization using the following classes of algorithms:

• Gradient-Based (For smooth, continuous problems): NLPQL, MMFD, LSGRG2

• Pattern-Based (For non-smooth problems without derivatives): Hooke-Jeeves, Downhill Simplex, Adaptive Simulated Annealing

• Mixed Integer / Real (For discrete and continuous variables): MISQP, MOST

• Genetic Algorithms (For global search in complex spaces): Evolution, Multi-Island GA

• Multi-Objective (For balancing competing goals): AMGA, NSGA II, NCGA, Particle Swarm

• Specialized Methods: Stress-Ratio Method, Pointer I & II Automatic Optimizer, Multi-objective approximation Loop.

Key Capabilities and Engineering Benefits

Integrating a PIDO tool like iSight into your R&D department provides a massive competitive advantage. The core capabilities include:

• Total Workflow Automation: Automate a sequence of functions across the entire simulation lifecycle, from initial geometry to final stress analysis.

• Software Coupling: Seamlessly couple diverse CAD and CAE software, completely eliminating manual data entry.

• Broad Compatibility: Direct integration with industry-standard software including Abaqus, Ansys, LS-Dyna, StarCCM, ADAMS, NASTRAN, and even Microsoft Excel.

• Scalability: Easily increase or decrease design components within the loop as the project evolves.

• Advanced Methodologies: Implement not just optimization, but also reliability and robustness techniques like Monte Carlo simulations, Six Sigma (6Sigma) methods, and Taguchi robust design.

By leveraging process optimization, engineering teams can shift their focus from repetitive data management to true innovation, ensuring that the final product is not just workable, but mathematically optimal.