Optimization and Design Space Exploration (DSE) enables new design concepts through the exploration of a larger design space with multiple constrains and conflicting objectives unlike traditional optimization with limited excursions of the variables.

The overarching goal of relying on virtual prototypes to reduce the number of physical prototypes requires robust simulation models. Stochastic simulation plays a greater role in the future, given the greater variability and uncertainty we can expect relating to input data.

Xitadel has extensive capability in all aspects of Optimization spanning Parametric & Non-Parametric Optimization and Multidisciplinary Optimization (MDO).

Topology and Parametric optimization

Topology and Parametric optimization can be used at the concept design stage to rapidly arrive at best design. These techniques create feasible non-intuitive and counter-intuitive structures by considering design parameters like expected loads, available design space, material and cost. Embedded early, it enables the creation of designs with minimal mass and maximal stiffness. Xitadel has expertise in applying optimization techniques to multiple domains, achieving 20-40% reduction in assembly weight. We have been able to rapidly arrive at the optimal system design by applying both parametric and topology optimization to the individual component in the initial design.


Material Calibration through Optimization

Material Model Calibration is crucial for the accuracy of CAE simulations. Material properties obtained from test data cannot be used directly in simulations. Extracting the material properties for complex material models like Plastics and Rubbers is tedious and time consuming. The goal is to provide a framework for calibrating surrogate Math Models to accurately simulate Observed Behavior. Xitadel has expertise in implementing optimization techniques for calibrating material properties for any given test data, allowing for the ease of CAE simulation.

Multi-disciplinary Design Optimization

Multi-disciplinary Design Optimization (MDO) incorporates the use of optimization techniques to solve design problems, considering relevant multiple domains simultaneously. Xitadel has expertise in developing a framework using MDO techniques that satisfy multiple constrains and conflicting objective functions from different domains like Crash, Durability, CFD, Thermal and Structural etc.


Reduced Order Modeling with Machine learning

Larger and more detailed models challenge traditional optimization approach, let alone stochastic applications. Emerging technologies such as ROM (Reduced order Modeling) leverage Machine Learning techniques drastically reduces the time required for Optimization.

Spot-Weld Optimization

Xitadel has developed together with a major Auto OEMs, Spot-Weld Optimization technology. With this technology, Xitadel achieves 10% to 15% Reduction of Spot-Welds in a spot welded body structure. This technology optimizes spot weld locations, weld pitch and applies stochastics for confidence. It also generates Schema for Plant. This reduces the total time for production and saves material cost for Spot-weld per Body-In-White (BIW).

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