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ANR-25-ASTR-0001

Functionally Graded Piezoelectric Transducer

FunGPT

(Not an AI joke)

Design and development of lead-free piezoelectric gradient function transducers for vibration control

A research project aimed at the development and characterization of new architectural materials (FGPM), resulting from an innovative “low temperature” process, in order to design a more efficient, eco-responsible and sustainable transducer.

the project

FunGPT aims to develop new piezoelectric transducers. These are used in numerous civil and military applications as basic components for sensors and actuators, particularly in vibration control applications. The aim of the project is to make them significantly more efficient: reducing mass, improving electromechanical conversion performance, increasing the operating temperature range and extending service life, whilst adopting an ambitious eco-design approach through the use of lead-free compounds, in compliance with REACH and RoHS regulations, and the development of more energy-efficient manufacturing processes.

These new piezoelectric transducers are made from gradient functionally piezoelectric materials (FGPM). They combine 2 types of material, a piezoelectric ceramic (potassium sodium niobate (KNN)) and a metallic material. Their structure consists of a gradual evolution of the composition and properties in thickness, in order to overcome drawbacks of classical transducers (reducing the stresses at the interfaces existing in conventional transducers and eliminating the problem of debonding).

The objective and originality of the FunGPT project lie in conducting ambitious experimental investigations on lead-free FGPMs by pairing the development of an innovative, low-energy manufacturing process with an in-depth characterization of the materials’ electromechanical properties. Implementation within a fully integrated isolation/stabilization system will serve to validate the resulting materials. The findings will provide crucial data for numerical modeling, which, in turn, will lead to the optimization of the processing stage.