BB3.12-Simulation
Belén Hernández Gascón
Description
Optimization of the PL materials / processes, through experimental /simulation data, to extract the optimum optimization strategy for each PL.
Simulation data to predict and optimize materials and processes.
Standardisation
The procedures and methodologies used in our machine learning-driven material property prediction services adhere to industry-recognized standards and best practices. While AI-driven material predictions are an evolving field, we ensure compliance with relevant international and European standards where applicable.
Additionally, our models and methodologies align with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, ensuring transparency and reproducibility in data-driven material property predictions.
Innovation features
Our AI-driven material and process optimization service integrates advanced machine learning algorithms and simulation-based optimization to enhance material performance and process efficiency. By leveraging data-driven and expert knowledge-driven models, we provide highly accurate predictions, minimizing the need for extensive physical testing and accelerating innovation in material science. The integration of digital twin technology allows for real-time simulation, enabling continuous improvement and adaptation of materials and processes before implementation. Our approach also aligns with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, ensuring transparency, reproducibility, and collaboration in research and industry applications. Additionally, the service contributes to the development of sustainable and high-performance materials, supporting the EU’s Green Deal and Circular Economy Action Plan by optimizing resources, reducing waste, and enhancing energy efficiency. These innovations position our service at the forefront of AI-driven materials research, providing a competitive edge in sustainable construction, advanced manufacturing, and green technology development.
Pricing
Case by Case
Our company does not apply cost-plus pricing but value based pricing. Therefore the expected cost of delivery will both depend on the scope of the requested service as well as the value the project will bring to the customer.
Service delivery timeframe
30-60 Working Days
Relevant Industry
RTD focused on materials development civil engineering
Civil engineering companies.
Providers
AiDEAS
POLITO, ITA
Supporting Services
Value proposition
Depend on the value of the project
Environmental and social impact
Our AI-driven material and process optimization service directly supports the EU’s sustainability, energy efficiency, and socio-economic objectives by improving resource efficiency and reducing environmental impact. By optimizing material use, we help industries minimize waste and enhance sustainability, contributing to a circular economy. Our approach also facilitates the development of eco-friendly materials with improved durability and recyclability, aligning with global sustainability goals. Furthermore, by integrating life cycle analysis (LCA), our solutions ensure that environmental considerations are embedded in the decision-making process. From an energy perspective, our AI-driven simulations help reduce energy consumption in manufacturing by minimizing trial-and-error processes, enhancing the performance of energy-efficient materials, and ultimately contributing to decarbonization efforts. Socially, our service fosters workforce upskilling by equipping professionals with AI and data-driven tools, strengthening European industrial competitiveness, and supporting the growth of green jobs and sustainable innovation. Through these advancements, our service plays a crucial role in accelerating the transition towards a more sustainable and resilient industrial ecosystem.
Geographical and/or infraestructure restrictions
Service Support
Our service includes comprehensive support to ensure seamless implementation and maximum value for our clients. We provide training sessions tailored to the needs of users, helping them understand and effectively utilize AI-driven material prediction models. Additionally, we offer technical support and consultancy throughout the project lifecycle, assisting with data integration, model interpretation, and optimization strategies. Post-delivery, we ensure continued assistance through follow-up consultations and updates, enabling clients to refine and adapt models based on evolving requirements. Our team remains available for customized enhancements, troubleshooting, and ongoing advisory services, ensuring that the solution remains aligned with industry advancements and client objectives.