Artificial intelligence in plastics – Artificial Intelligence Transforms Plastic Innovation as AIMPLAS POLY-ML Project Accelerates Sustainable, Faster and Smarter Materials Design Across Industry 05-02-2026
Artificial intelligence in plastics
Artificial intelligence reshapes plastic materials development
Artificial intelligence is rapidly redefining industrial research, and the plastics sector is no exception. With the POLY-ML project, AIMPLAS, the Plastics Technology Center, is taking a decisive step toward a more digital, efficient and sustainable approach to materials design. By integrating artificial intelligence into the early stages of formulation and processing, the initiative aims to fundamentally change how plastic materials are developed, tested and optimized. artificial intelligence in plastics
The core objective of POLY-ML is to apply machine learning models capable of predicting the mechanical, thermal and physical properties of plastic materials based on their composition and processing conditions. This approach allows researchers and manufacturers to anticipate performance before physical prototyping, significantly improving decision-making speed and accuracy.
Artificial intelligence plays a central role throughout the project, enabling a shift from trial-and-error experimentation to data-driven development. As a result, the plastics industry gains access to smarter tools that reduce uncertainty while increasing competitiveness. artificial intelligence in plastics
Predictive models that reduce time, cost and errors
Traditional plastic material development often requires extensive laboratory testing, repeated formulation adjustments and long validation cycles. POLY-ML addresses these challenges by using artificial intelligence to build predictive models trained on real industrial data.
These models can forecast how a material will behave under specific conditions, helping engineers identify optimal formulations early in the R&D process. By minimizing the need for experimental testing, artificial intelligence reduces development times, lowers costs and decreases the likelihood of formulation errors. artificial intelligence in plastics
The use of artificial intelligence also improves traceability. Each decision is supported by structured data, making it easier to document development pathways, validate outcomes and comply with quality and sustainability requirements. For companies operating in highly regulated markets, this represents a substantial competitive advantage.
Collaboration between research and industry
A defining feature of the POLY-ML project is its collaborative structure. Alongside AIMPLAS, the initiative involves Tyris AI, a specialist in artificial intelligence applied to industrial environments, and FAPERIN, a plastic processing company focused on polypropylene injection moulding for the automotive sector. artificial intelligence in plastics
FAPERIN contributes real production data from its industrial processes, which is essential for training reliable and robust machine learning models. Tyris AI provides expertise in artificial intelligence, ensuring that advanced algorithms are effectively adapted to industrial use cases. AIMPLAS coordinates the research effort, validating models and aligning them with sector needs.
This collaboration ensures that artificial intelligence solutions are not confined to laboratories but are designed for real-world industrial environments. The goal is practical applicability, not theoretical experimentation.
Democratizing artificial intelligence in the plastics sector
One of the most innovative aspects of POLY-ML is its focus on accessibility. The project is developing a tool that allows predictive models to be created without requiring programming knowledge. This lowers the barrier to entry for artificial intelligence adoption across the plastics industry. artificial intelligence in plastics
By simplifying model development, AIMPLAS aims to encourage small and medium-sized enterprises to integrate artificial intelligence into their workflows. This democratization of technology supports digital transformation across the sector, enabling companies of all sizes to benefit from data-driven innovation.
Artificial intelligence becomes a practical instrument for engineers and technicians, not an exclusive resource for data scientists. This approach accelerates digital maturity and strengthens overall industrial competitiveness.
Sustainability and environmental impact
Sustainability is a central pillar of the POLY-ML project. Artificial intelligence contributes directly to environmental goals by reducing laboratory waste, minimizing the use of hazardous solvents and additives, and avoiding inefficient material formulations.
Predictive modeling helps eliminate unnecessary experiments, lowering energy consumption and material usage. By optimizing formulations from the outset, companies can design plastic materials that meet performance requirements with fewer resources.
Artificial intelligence also supports circular economy principles by enabling better control over material properties, recyclability and durability. In the long term, these capabilities contribute to more sustainable product lifecycles and reduced environmental impact across the plastics value chain. artificial intelligence in plastics
Benefits for occupational health and workforce safety
Beyond environmental gains, POLY-ML delivers tangible benefits for occupational health. Reducing experimental testing lowers the exposure of laboratory staff to chemicals and hazardous substances. Artificial intelligence-driven simulations replace many physical trials, decreasing risks associated with handling materials and operating equipment.
This shift enhances workplace safety while allowing skilled professionals to focus on higher-value analytical and design tasks. Artificial intelligence thus supports both technological progress and human well-being. artificial intelligence in plastics
Strengthening the local and regional economy
The economic impact of POLY-ML extends beyond individual companies. By reinforcing innovation capabilities within the plastics sector, the project strengthens the industrial fabric of the Valencian Community.
Artificial intelligence adoption drives productivity, supports the creation of skilled jobs and enhances technological autonomy. Companies gain the ability to develop advanced materials locally, reducing dependence on external technologies and knowledge.
The project aligns with the RIS3-CV strategy, addressing priorities such as digitalization, sustainability, circular economy development and collaboration between research centers and industry. This strategic alignment positions the Valencian Community as a reference point for artificial intelligence applied to materials science. artificial intelligence in plastics
Public funding and strategic alignment
POLY-ML is supported by the Valencian Institute of Competitiveness and Innovation through its industrial R&D promotion programs, with co-funding from the European Regional Development Fund. This backing reflects the strategic importance of artificial intelligence for industrial modernization and sustainable growth.
Public funding enables risk-sharing and accelerates innovation, ensuring that advanced artificial intelligence tools reach the market faster and deliver tangible benefits to industry and society. artificial intelligence in plastics
A data-driven future for plastic materials
With POLY-ML, AIMPLAS demonstrates how artificial intelligence can transform plastic materials design from a reactive process into a predictive, efficient and sustainable system. By combining machine learning, industrial data and collaborative expertise, the project sets a clear direction for the future of the plastics industry. artificial intelligence in plastics
Artificial intelligence is no longer a distant concept but a practical enabler of smarter materials, safer workplaces and more competitive industries. POLY-ML stands as a concrete example of how data-driven innovation can deliver real-world impact.
More…
