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Home > News > Auxiliary

AIMPLAS promotes use of AI to predict properties of plastic materials

Source：Adsale Plastics Network Date ：2026-02-09 Editor ：RC
Copyright： This article was originally written/edited by Adsale Plastics Network (AdsaleCPRJ.com), republishing and excerpting are not allowed without permission. For any copyright infringement, we will pursue legal liability in accordance with the law.

AIMPLAS launches POLY-ML project, an R&D initiative that applies advanced machine learning techniques to predict material properties based on their composition and processing conditions, making it possible to optimize formulations, reduce the need for experimental testing and improve the efficiency of R&D processes.


AIMPLAS_POLY-ML project.jpg

AIMPLAS’ POLY-ML project applies AI to material properties prediction.

 

The project is with the participation of Tyris AI, which specializes in artificial intelligence applied to industry, and FAPERIN, a plastic processing company, mainly polypropylene injection molding for the automotive sector. FAPERIN provides data from its processes to train models and draw conclusions while Tyris AI contributes its knowledge in the application of AI in the industrial sector.

 

POLY-ML project focuses on the development of predictive models capable of anticipating the mechanical, thermal or physical properties of materials, which will enable faster and more accurate decisions to be made in the early stages of development. This data-driven approach helps to reduce costs, time and waste generation, while improving the traceability and sustainability of processes.

 

For sustainability and safer work environment

 

The project generates significant benefits in terms of environmental sustainability and occupational well-being. From an environmental perspective, it contributes to reducing laboratory waste and the use of hazardous solvents and additives by avoiding inefficient formulations. In the field of occupational health, it reduces the exposure of technical staff to chemicals and reduces the risks associated with experimental testing.

 

Furthermore, it is aligned with the RIS3-CV strategy in key areas such as digitalization, sustainability, the circular economy and collaboration between agents in the industrial and research ecosystem, thus consolidating the Valencian Community's position as a benchmark in the application of artificial intelligence to the design of plastic materials.

 

 


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Source：Adsale Plastics Network Date ：2026-02-09 Editor ：RC
Copyright： This article was originally written/edited by Adsale Plastics Network (AdsaleCPRJ.com), republishing and excerpting are not allowed without permission. For any copyright infringement, we will pursue legal liability in accordance with the law.

AIMPLAS launches POLY-ML project, an R&D initiative that applies advanced machine learning techniques to predict material properties based on their composition and processing conditions, making it possible to optimize formulations, reduce the need for experimental testing and improve the efficiency of R&D processes.


AIMPLAS_POLY-ML project.jpg

AIMPLAS’ POLY-ML project applies AI to material properties prediction.

 

The project is with the participation of Tyris AI, which specializes in artificial intelligence applied to industry, and FAPERIN, a plastic processing company, mainly polypropylene injection molding for the automotive sector. FAPERIN provides data from its processes to train models and draw conclusions while Tyris AI contributes its knowledge in the application of AI in the industrial sector.

 

POLY-ML project focuses on the development of predictive models capable of anticipating the mechanical, thermal or physical properties of materials, which will enable faster and more accurate decisions to be made in the early stages of development. This data-driven approach helps to reduce costs, time and waste generation, while improving the traceability and sustainability of processes.

 

For sustainability and safer work environment

 

The project generates significant benefits in terms of environmental sustainability and occupational well-being. From an environmental perspective, it contributes to reducing laboratory waste and the use of hazardous solvents and additives by avoiding inefficient formulations. In the field of occupational health, it reduces the exposure of technical staff to chemicals and reduces the risks associated with experimental testing.

 

Furthermore, it is aligned with the RIS3-CV strategy in key areas such as digitalization, sustainability, the circular economy and collaboration between agents in the industrial and research ecosystem, thus consolidating the Valencian Community's position as a benchmark in the application of artificial intelligence to the design of plastic materials.

 

 


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AIMPLAS promotes use of AI to predict properties of plastic materials

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