--- title: "Digitalization and Simulation: Redefining What is Possible" id: "10981" type: "post" slug: "digitalization-and-simulation-redefining-what-is-possible" published_at: "2026-04-03T13:10:19+00:00" modified_at: "2026-03-25T13:12:53+00:00" url: "https://www.plasticsengineering.org/2026/04/digitalization-and-simulation-redefining-what-is-possible-010981/" markdown_url: "https://www.plasticsengineering.org/2026/04/digitalization-and-simulation-redefining-what-is-possible-010981.md" excerpt: "AI-aided polyurethane simulation reduces modeling time from days to seconds. Digital material twins optimize tool design and predict foaming behavior." taxonomy_category: - "Artificial Intelligence" - "Automotive & Transportation" - "Business" - "Education & Training" - "Industry" - "Industry 4.0" - "Injection Molding" - "Materials" - "Polyurethane" - "Process" - "Software" - "Strategy" - "Thermoforming" - "Trending" taxonomy_post_tag: - "AI" - "Covestro" - "digital twin" - "foam molding" - "High-performance computing" - "Industry 4.0" - "Material Science" - "polyurethane" - "predictive modeling" - "R&D" - "RIM" - "Simulation" --- [Home](https://www.plasticsengineering.org/) » [News](https://www.plasticsengineering.org/news/) » [Trending](https://www.plasticsengineering.org/c/trending/) » [Artificial Intelligence](https://www.plasticsengineering.org/c/trending/artificial-intelligence/) » Digitalization and Simulation: Redefining What is Possible # Digitalization and Simulation: Redefining What is Possible Moving from traditional multi-day computations to 10-second AI-aided predictions for real-time production troubleshooting.### AI-aided polyurethane simulation reduces modeling time from days to seconds. Digital material twins optimize tool design and predict foaming behavior. In this third article of our series, we review how digitalization partners with simulation to empower material development and troubleshoot production parameters. While simulation has supported plastics processing for over three decades, the introduction of AI marks a new revolution. Specifically, this new era combines high-performance computing with machine learning to reduce simulation times from days to mere seconds. ## **Why Simulate Polyurethane (PU)?** PU is formed through a reaction between isocyanates and polyols, commonly processed via reaction injection molding (RIM). Because this process is highly complex, manufacturers must avoid air traps and achieve a homogeneous density distribution. Furthermore, engineers often need to tailor foam density to achieve specific rigidity levels within a single mold. In complex automotive parts, it is necessary to identify where air traps may occur to implement appropriate venting. Additionally, external conditions such as ambient humidity or altitude can require immediate adjustments to the production setup. By using a predictive model, processors can anticipate variations in chemistry and curing, effectively reducing scrap. ## **Building a Material Digital Twin** Simulation software predicts the foaming process in an instrument panel. The model can predict the foaming process and material behavior. Image courtesy of Bayfill® technology from [Covestro.](https://solutions.covestro.com/es/highlights/articles/stories/2025/polyurethane-foaming-simulation) Companies like Covestro have developed proprietary material models coupled with powerful computing to create a “Digital Twin” of the foaming process. Notably, predicting reactive PU flow involves multi-physics computational fluid dynamics based on Navier-Stokes equations. Consequently, the model must track CO2 formation, volume changes, and chemo-viscosity over time and space. Through this digital twin, processors identify the best tool design and define process parameters early. In this way, they calibrate the optimum processing window, including filling points, tool temperature, and venting layout, without conducting expensive physical experiments. ## **How to Generate a Digital Twin** The generation of a Digital Material Twin follows a four-step process: 1. **Formulation:** Understanding the specific PU mixture. 2. **Experimentation:** Analyzing how the system behaves under changing production conditions. 3. **Validation:** Comparing the model against real-life applications. 4. **Optimization:** Adjusting the model through high-performance computing until it mirrors observed behavior. ## **A New Era: Combining AI with Simulation** While accurate, conventional Digital Material Twins can require up to three days of computation across hundreds of cores. To solve this, the next goal is to implement AI systems that predict outcomes instantly. Specifically, Covestro generated an automated workflow to create simulation training sets for an AI model. In a 2D pilot study, researchers performed 300 simulations to train the AI. As a result, the AI model can now predict air traps and flow front growth in just 10 seconds—a task that previously took hours. Ultimately, this speed enables real-time troubleshooting on the production floor. If an operator faces a quality issue, they can use an AI tool on a laptop to get results within seconds, drastically changing the pace of decision-making in plastics processing. By **[Laura Florez](https://www.plasticsengineering.org/author/lauraf/)** | April 3, 2026 ##### [Laura Florez](https://www.plasticsengineering.org/author/lauraf/) [+ postsBio ⮌](#) ### Other posts you could read [May 14, 2026Carbon Fiber Production from End-of-Life Automotive Polymers](https://www.plasticsengineering.org/2026/05/carbon-fiber-production-from-end-of-life-automotive-polymers-011254/) [May 8, 2026Redefining “Premium” Appearance in Sustainable Plastics](https://www.plasticsengineering.org/2026/05/redefining-premium-appearance-in-sustainable-plastics-011191/) [May 5, 2026Assessing the Limits of Circularity in Healthcare Plastics](https://www.plasticsengineering.org/2026/05/assessing-the-limits-of-circularity-in-healthcare-plastics-011180/) [May 13, 2026The Logistics Challenge of Scaling Mechanical Recycling](https://www.plasticsengineering.org/2026/05/the-logistics-challenge-of-scaling-mechanical-recycling-011251/) [May 12, 2026Why Recycled Polymers Still Cost More Than Virgin Resin](https://www.plasticsengineering.org/2026/05/why-recycled-polymers-still-cost-more-than-virgin-resin-011239/) [May 21, 2026Rheological Additives for Low-Roughness Aerospace Coatings](https://www.plasticsengineering.org/2026/05/rheological-additives-for-low-roughness-aerospace-coatings-011279/) [May 19, 2026Human Chaos: The Design Rebellion Against Algorithmic Perfection](https://www.plasticsengineering.org/2026/05/human-chaos-the-design-rebellion-against-algorithmic-perfection-011268/) [May 20, 2026Chopped Fiber Compounding Enters Injection Molding](https://www.plasticsengineering.org/2026/05/chopped-fiber-compounding-enters-injection-molding-011294/) ## Share Your Thoughts [Cancel reply](/2026/04/digitalization-and-simulation-redefining-what-is-possible-010981/#respond) - [https://twitter.com/share?url=https://www.plasticsengineering.org/?p=10981&text=Digitalization+and+Simulation%3A+Redefining+What+is+Possible&via=Plastics_Mag](https://twitter.com/share?url=https://www.plasticsengineering.org/?p=10981&text=Digitalization+and+Simulation%3A+Redefining+What+is+Possible&via=Plastics_Mag) - [https://www.facebook.com/sharer.php?u=https://www.plasticsengineering.org/?p=10981&t=Digitalization+and+Simulation%3A+Redefining+What+is+Possible](https://www.facebook.com/sharer.php?u=https://www.plasticsengineering.org/?p=10981&t=Digitalization+and+Simulation%3A+Redefining+What+is+Possible) - [https://www.linkedin.com/shareArticle?mini=true&url=https://www.plasticsengineering.org/?p=10981&title=Digitalization%20and%20Simulation:%20Redefining%20What%20is%20Possible&summary=AI-aided+polyurethane+simulation+reduces+modeling+time+from+days+to+seconds.+Digital+material+twins+optimize+tool+design+and+predict+foaming+behavior.&source=Plastics+Engineering](https://www.linkedin.com/shareArticle?mini=true&url=https://www.plasticsengineering.org/?p=10981&title=Digitalization%20and%20Simulation:%20Redefining%20What%20is%20Possible&summary=AI-aided+polyurethane+simulation+reduces+modeling+time+from+days+to+seconds.+Digital+material+twins+optimize+tool+design+and+predict+foaming+behavior.&source=Plastics+Engineering) - [https://www.reddit.com/submit?url=https://www.plasticsengineering.org/?p=10981&title=Digitalization+and+Simulation%3A+Redefining+What+is+Possible](https://www.reddit.com/submit?url=https://www.plasticsengineering.org/?p=10981&title=Digitalization+and+Simulation%3A+Redefining+What+is+Possible) - [/cdn-cgi/l/email-protection#2d125e584f47484e59107d414c5e59444e5e0d68434a444348485f44434a0d000d69444a44594c4144574c594442430d4c43490d7e444058414c59444243170d7f4849484b444344434a0d7a454c590d445e0d7d425e5e444f41480b4f424954104559595d5e1702025a5a5a035d414c5e59444e5e48434a444348485f44434a03425f4a02125d101c1d14151c](/cdn-cgi/l/email-protection#2d125e584f47484e59107d414c5e59444e5e0d68434a444348485f44434a0d000d69444a44594c4144574c594442430d4c43490d7e444058414c59444243170d7f4849484b444344434a0d7a454c590d445e0d7d425e5e444f41480b4f424954104559595d5e1702025a5a5a035d414c5e59444e5e48434a444348485f44434a03425f4a02125d101c1d14151c) - [#comments](#comments)