TY - JOUR
T1 - A statistical study on factors influencing piezoelectric properties of upside-down composites towards machine learning-driven development for recycling
AU - Anandakrishnan, Sivagnana Sundaram
AU - Yadav, Suhas
AU - Tabeshfar, Mohadeseh
AU - Nelo, Mikko
AU - Peräntie, Jani
AU - Bai, Yang
N1 - Publisher Copyright:
© 2025 The Author(s)
PY - 2025/6
Y1 - 2025/6
N2 - Using upside-down composites to recycle retired/discarded piezoceramics and then give them a second life in sensor applications paves the way towards sustainable production of piezoelectric materials. However, the piezoelectric properties of the recycled materials need to be significantly improved. The advancement of recycled materials should benefit from the recently developed AI-assisted methods, in order to minimize overconsumption of resources during the experimental trial and error. Previous works have identified obstacles to developing reliable models that can provide a systematic understanding of the contributors to the properties of the recycled materials. This work aims to overcome such obstacles by appropriately changing the fitting constants so that the models coincide with each experimental datapoint. These constants include descriptors of the microgeometry, size and orientation of the fillers, and the extent of polarization in the composites. By analyzing the variation of these constants between the datapoints, a clear perspective of the contributors to the properties of the recycled materials is established. This multi-variable approach is also extended to different fabrication techniques. The approach lays down a foundation for scaling up the optimization of the recycled materials by providing training and/or testing datasets for possible machine learning algorithms via potential high-throughput manufacturing routes.
AB - Using upside-down composites to recycle retired/discarded piezoceramics and then give them a second life in sensor applications paves the way towards sustainable production of piezoelectric materials. However, the piezoelectric properties of the recycled materials need to be significantly improved. The advancement of recycled materials should benefit from the recently developed AI-assisted methods, in order to minimize overconsumption of resources during the experimental trial and error. Previous works have identified obstacles to developing reliable models that can provide a systematic understanding of the contributors to the properties of the recycled materials. This work aims to overcome such obstacles by appropriately changing the fitting constants so that the models coincide with each experimental datapoint. These constants include descriptors of the microgeometry, size and orientation of the fillers, and the extent of polarization in the composites. By analyzing the variation of these constants between the datapoints, a clear perspective of the contributors to the properties of the recycled materials is established. This multi-variable approach is also extended to different fabrication techniques. The approach lays down a foundation for scaling up the optimization of the recycled materials by providing training and/or testing datasets for possible machine learning algorithms via potential high-throughput manufacturing routes.
KW - Composite modelling
KW - High-throughput manufacturing
KW - Lichteneker model
KW - Sustainable electronics
KW - Yamada model
UR - http://www.scopus.com/inward/record.url?scp=105004261178&partnerID=8YFLogxK
U2 - 10.1016/j.matdes.2025.114044
DO - 10.1016/j.matdes.2025.114044
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AN - SCOPUS:105004261178
SN - 0264-1275
VL - 254
JO - Materials and Design
JF - Materials and Design
M1 - 114044
ER -