Modeling and characterization of materials and manufacturing processes
Optimal mechanical design of machines and components
Design, analysis and theoretical and experimental evaluation of machines and components, adding value from an idea's conception to the manufacturing process through calculation (resistance, fatigue), dynamic analysis (vibrations), redesign, design of detail, prototyping and even the construction of test benches when required. Specialization in machines with rotating elements.
Life prediction based on mechanical reliability
Experimental characterization and modeling of fracture and fatigue (and creep-fatigue) under complex states of mechanical and thermal load, including residual stresses. Design of experiments for early crack detection and component testing. Analytical models and models based on finite elements (cohesive models, XFEM and others developed by Ceit) to estimate the remaining life of components and to establish strategies for predictive maintenance and decision-making.
Mathematical models of materials and simulation of cold forming processes
The trend in various industrial sectors (automotive, energy, construction, etc.) is to reduce the weight of components and the use of raw materials by using a higher-grade (stronger) and thinner starting material.
Using these grades requires finer control over the forming process and a tighter process window to avoid problems in formability and/or tool breakage. We design the forming process for these new grades based on previous experience and a methodology based on trial-and-error, which decreases prototyping costs, shortens lead times and guarantees optimal processing conditions. We study robust computational tools that allow for the accurate prediction of springback and formability phenomena in forming processes.
The construction of physics-based material models that accurately describe the elastic-plastic anisotropy of the starting material.
The construction of anisotropic damage models that allow the conformability of the material to be predicted.
The development of computational tools based on reverse engineering that leads to optimal shaping strategy and tooling design.