Machinery
Machinery
Multidisciplinary optimization to improve the design process
The design of complex vehicles requires the involvement of various engineering fields and numerous work teams. We have helped an important player in the Italian aerospace sector to adopt methodologies that allow a unitary approach to manage complexity and to find the optimal trade-off between different objectives while respecting the constraints dictated by the project specifications.
- Description and benefits
The main pain point diffused in all the client's divisions is linked to the great engineering complexity of the products and to the interaction that is created in the design process between the different working groups.
In particular, different teams of engineers find themselves working on the same component optimizing different characteristics and specifications; the interaction between these groups is often long and often leads to a large number of iterations.
In order to create opportunities for innovation and efficiency in the design process, we introduced multidisciplinary optimization methodologies.
Through a dedicated training workshop, the engineers were able to understand the concrete benefits of this methodology in different industrial contexts. This allowed for the identification of use cases relating to the client's specific needs on which these innovative techniques could be applied to improve the efficiency.
New methodology
Introduction of a new optimization methodology in different contexts
Reduced iterations
By adopting a multidisciplinary approach, it is possible to carry out design tasks by reducing the number of interactions required between teams dealing with different issues
Cross-functional teams
Working in isolated silos increases the risk of being inefficient. The proposed methodologies encouraged collaboration between the different teams to define common ways of approaching design
Deepening case study
Finding an optimum in the presence of numerous trade-offs
Multi-disciplinary optimization methodologies provide theoretical support for dealing with complex situations where there are several competing factors and the criteria for identifying an optimal solution are not unambiguous.
For example, it is possible to optimize the shape of a component by considering its aerodynamic performance and structural requirements simultaneously.
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