Injection molds are exposed to high mechanical loads and high pressures. Under-dimensioning can quickly lead to mold damage or quality defects in the components to be produced. A structural mechanical simulation can help to avoid potential economic damage in early design phases or to find solutions for existing faulty molds. 

However, it has been shown that simplified considerations of tools are not sufficient for structural mechanics simulations. The system boundaries must be extended to include the influence of the injection molding machine on the overall stiffness. In this presentation, this influence will be highlighted.

With the Green Deal, the European Union has set itself the goal of becoming climate-neutral by 2050. In 2020, the European Commission adopted a package of actions for the ongoing development of the circular economy in Europe with the new Circular Economy Action Plan. Some of the measures adopted at the time are currently being implemented politically, such as the European Packaging and Packaging Waste Regulation (PPWR) and the End-of-life Vehicle Regulation. The current drafts of these legal acts contain ambitious targets for the use of recycled materials, which will have to be taken into account in the processing of plastics in the future. An overview of the future circular economy requirements in the plastics sector is provided.

To fully deploy virtual validation of injection molded parts in the industrial context, we need to distribute professional tools for propagating the uncertainty of the design variables along the current simulation workflows. Today those simulation workflows are deterministic but do not estimate uncertainty, which is a natural feature in our physical world and is a key element in design for reliability. On top of that, the consideration of uncertainty appears to be particularly relevant when dealing with recycled materials, as there is a common concern about the higher variability of their properties. 

We propose a virtual framework for propagating uncertainty in injection molding simulation by employing Autodesk® Moldflow® and an internal Python-based tool for metamodel generation/exploitation. To provide a concrete example, we consider the case of mechanically recycled short-fiber reinforced thermoplastics as raw material for injection molding. First, we discuss the actual variation of fiber length and shear viscosity of an in-house mechanically recycled glass fiber reinforced PBT. Subsequently, we showcase a virtual workflow for transporting the uncertainty of those material properties in the estimation of fiber orientation, which is a pivotal input for the computer-assisted anisotropic mechanical design. Finally, we present an outlook to the software tooling that could be implemented for propagating the uncertainty further until the structural simulation domain.  

Post-Consumer Recycled (PCR) plastic materials will play a crucial role in order to fulfill the objectives of the upcoming End of Live Vehicle Regulation by the EU. According to this regulation 25% of plastics to build new vehicles must be recycled.

With sufficient experience in material sourcing and quality control of source material and final compound, PCR materials can be compounded to a virgin-like quality. However, uncertainty surrounding the quality of PCR materials often leads to reluctance in their application.

In this presentation, we take a look at batch variations of fossil-based PC/ABS and PC/ABS with 50% PCR content in comparison. An approach to estimate the effects of different batches early in the design phase with Moldflow is suggested based on the variations. The objective is to evaluate how the quality of PCR materials impacts quality criteria such as the dimensional accuracy of a part and to provide a range of expected variations. Ultimately, the designer shall be enabled to make more informed decisions when selecting sustainable materials.

Moldflow already offers many calculation options for various special processes, but there are often special aspects that cannot be simulated in the standard version. However, the Synergy API opens up enormous potential for advanced users to expand the simulation options themselves. This presentation will show how the Synergy API and custom Python scripts can be used to implement a damage mechanism for film back injection molding of a multi-layer film. For this purpose, existing results (temperatures, shear stresses) were exported from Moldflow and used in Python scripts for a proprietary empirically developed formula, which correlated the occurring shear stresses, temperatures and the degree of melting of a film component with the experimentally determined film deformation during overmolding. The degree of deformation thus determined could then be imported back into Moldflow and displayed as a color plot. The process was thus optimized and experimentally validated with regard to the lowest possible damage. The methodology illustrated by this example can also be applied to many other specific problems.

Electromobility requires new lightweight construction strategies due to the additional weight of the batteries. These solutions must be able to withstand both thermal boundary conditions, e.g. the thermal runaway of the battery, and mechanical boundary conditions, e.g. impact. One of these solutions are LFT tape sandwiches, such as those used in series production in the Q6 e-tron (see Figure 1). 

With their dimensions of between 1 and 3 m² and the many process steps involved, such components pose major challenges for process simulation. First, the tape is heated, then the extruded LFT plastic certificate is placed on the lower tape and covered with the second tape. This is followed by a transfer into the tool before the extrusion process begins. The temperature distribution in the tape and LFT at the start of pressing is decisive for the filling of the component and its component quality. For this reason, Simutence has developed a virtual process chain that uses the thermal simulation tool SimuTherm as a central tool to determine the initial temperature depending on the handling and to initialize it in Moldflow. This virtual process chain will be demonstrated and validated in this presentation on a tape LFT sandwich underbody, which was developed together with AUDI, ElringKlinger and the Fraunhofer ICT and IGCV as part of the publicly funded protECOlight project. 

For electronic housings, demanding dimensional tolerances lead to long tool correction loops. To improve part quality and reduce tool correction efforts an accurate warpage prediction is key. An experimental injection-moulding tool was built for the purpose of producing housings with different injection-locations and housing geometries.  

In this presentation, we take a look at housings produced from PA66 GF30% with different feed and geometry options. In a comparison between simulated warpage and measured warpage on moulded parts, we will put Moldflow to the test: How accurate is the warpage prediction?  

Finally, we provide insights into what we have learned from this journey and how we improved our development process for electronic housings. 

The identification of an appropriate rheological model for polymer materials, facilitating accurate predictions of the micro injection molding process, constitutes a subject of current significance and substantial scientific interest. This is particularly the case when high accuracy and miniaturized features are required, for example in scenarios involving micrometric geometries such as those encountered in microfluidic devices for biomedical applications. In this context, the current study investigated the rheological behavior of thermoplastic materials during the micro injection molding process. Specifically, the deviations between simulated predictions compared to real experimentation carried out on the DesmaTec FormicaPlast 1K micro injection molding machine were analyzed. A sensitivity analysis of the main viscosity model parameters was carried out to provide clear insights into potential interventions targeting parameters of the reference rheological model acknowledged as exerting the most significant impact on viscosity. The comparisons between simulated filling analyses and those resulting from experiments were based on the response variables obtained from an instrumented two-cavity micro tool which used two sensors capable of providing melt pressure and temperature at the corresponding sensors positions, the pressure and temperature variation between the two sensors, as well as the time needed by the melt to flow between the two sensors. 

Synera is a process automation platform specifically designed for engineers. Thanks to the user-friendly UI and shareable templates anyone from your team can easily use and modify them to level up their work. The new Moldflow connector allows integrating Moldflow in complex, multidisciplinary development workflows. In this presentation, examples will be presented solving typical injection molding design challenges.