Seminar:
Digital engineering
for the plastic industry

CAE moulding simulations are often used in a reactive manner when there is already a warpage problem on a moulded part. However, screening for and predicting warpage tendencies up front via simulation can help resolve many moulding issues before a mould is even made. This presentation looks at the automation journey CAE at The LEGO Group have been on and how we developed and automated a new “Quick Warpage” simulation type.

When molten plastics solidify, materials can experience up to 30% volumetric shrinkage. Traditionally, injection moulding tools compensate for this shrinkage using a single global shrinkage factor, -despite the well-known anisotropic behavior of shrinkage.

In this presentation, we share the journey that allows us to better account for differential shrinkage and achieve moulded components closer to nominal dimensions. Initially, we struggled to predict shrinkage accurately in Moldflow simulations and relied on experimental correction factors and local cross-section shrinkage investigations. Over time, this evolved into today’s automated Design for Manufacturing (DFM) tool. The shrinkage tool delivers optimal shrinkage values (x, y, z) within minutes, works by comparing simulations or moulded components to nominal dimensions, while decoupling warpage from the equation to avoid inaccuracies.

Data-driven modeling and deep learning are revolutionizing industrial operations many sectors, and injection molding is no exception. This presentation introduces a data-driven framework integrating Finite Element Analysis (FEA), Design of Experiments (DOE), and Deep Neural Networks (DNNs) to model and optimize injection molding of thermoplastics. The developed algorithm correlates melt temperature, mold temperature, and injection speed with key outputs—part weight, cavity injection time, and injection pressure for several polymer grades. Material-specific viscosity and pressure-specific volume-Temperature model coefficients are embedded into predictive models, achieving accuracy across unseen materials beyond the training dataset. The approach demonstrates superior performance over traditional regression methods, enabling robust digital twins for process optimization and defect minimization in precision polymer manufacturing. Whether you’re working with ABS, PEEK, or PLA, this session offers actionable insights for engineers aiming to elevate precision and efficiency in polymer processing.

Using simulations for virtual testing in product development offers a great opportunity with respect to building early insight and knowledge by enabling rapid design evaluations. However, the adoption of virtual testing comes with new challenges: from culture changes to data collection, storage, and analysis. The presentation will showcase examples on how simulation helps increasing the innovation height in Coloplast, while at the same time trouble shooting when the simulations do match reality.

The presentation introduces an innovative material characterization workflow that overcomes traditional limitations in rheological measurements for injection molding simulations. By combining laboratory experiments with advanced computational fluid dynamics (CFD) and machine learning technique, we achieve improved viscosity predictions across diverse processing conditions. Our approach eliminates common measurement inaccuracies and reduces the need for extensive experimental trials, delivering more reliable simulation inputs.

System balance plays an important role in the optimization of an injection molding process and can be the limiting factor in the production of high-quality parts from every cavity. In this presentation, Husky will present the process fundamentals of system and hot runner balance and its impact on mold qualification. By utilizing rheologic, structural and thermal simulation and analysis, we will look at the factors that enable well-designed, balanced hot runners and the causes of unbalanced systems. Attendees will learn about key areas of hot runner analysis, the factors that influence short shot balance, how to measure system balance and how to set system balance expectations based on specific applications.

Key Takeaways:

• Process fundamentals of system and hot runner balance as well as impact on mold qualification
• Simulation and analysis tools and processes
• How to assess the factors to balance short shot factors
• How to measure system balance
• How to set up balance expectations

A Metal Replacement into a Polymer Valve started as an unsuccessful design, not reaching our approval demands. It was therefore decided to stop the project. A negative outcome but based on the learnings and the possibility of teaming up with material supplier and moulder/tool designer in a closer cooperation it was decided to restart the project as generation 2 polymer valve a few years later. Based on a combination of structural and injection moulding simulations the design was successful improved, and the polymer valve was released.

The use of Computer-Aided Engineering (CAE) tools in the development of plastic parts plays a crucial role in reducing time to market and minimizing trial-and-error iterations. However, applying simulation effectively across different development phases requires careful consideration to avoid common pitfalls. In this presentation, we will explore how simulation can be integrated throughout the design process, highlight best practices, and share examples from the packaging industry.