Efficiency in Hot Runner Design: AI-assisted Optimization for Peak Performance to Enhance Product Quality | EWIKON
By utilizing AI-driven CFD simulations, EWIKON revolutionizes temperature control in hot runner systems, achieving more precise and cost-efficient production.
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EWIKON
EWIKON is considered synonymous in the injection molding industry with innovative and proven hot runner systems for waste-free production of plastic parts, combining technical precision with practical applicability.
Solution
Using AI-powered CFD simulations, EWIKON optimized the temperature distribution in their hot runner systems, leading to higher precision, reduced costs, and more efficient production processes.
Company size
>300 employees
Revenue
<5M EUR
Challenge
In EWIKON's extrusion processes, an even temperature distribution within the hot runner systems was critical for product quality. Traditionally, manual adjustments for thermal balance required significant time, greatly extended process durations, and heavily relied on the expertise of the staff.
It became evident that an optimized and automated approach was necessary to improve the unbalanced melt flow in the hot runner channels, ensure product consistency, and enhance operational efficiency. EWIKON sought a solution to tackle these challenges, simplify the thermal design, improve quality, and reduce labor-intensive manual steps – for a more efficient and agile production cycle.
Result
Through the use of AI-driven CFD simulations, EWIKON significantly enhanced the efficiency of their hot runner systems. The improved temperature control reduced energy losses and facilitated an even heat distribution, which in turn extended the lifespan of the tools and reduced waste.
The production processes were optimized through precise control, leading to cost savings and an accelerated time-to-market.
Process
EWIKON began with a detailed analysis of the requirements for their hot runner systems to ensure efficient and consistent temperature control. Precise control of material flows and heat distribution played a central role in this. In the first step, all relevant parameters in the hot runner system were recorded, and a comprehensive model was created to represent the unique physical and thermal properties of the system. This model formed the basis for the application of AI-supported Computational Fluid Dynamics (CFD).
With the help of AI-supported CFD technology, EWIKON created simulation-based scenarios that depicted a variety of potential material and temperature profiles in the system. The AI algorithms conducted automated analyses, continuously adjusting the distribution of material flows and temperature gradients in the hot runner. Each simulation cycle enabled the AI to determine the best possible parameters while simultaneously minimizing deviations in heat distribution.
A crucial part of the process was the AI's ability to independently learn from each run and to improve the results for subsequent simulations. This allowed parameters such as temperature, pressure distribution, and material flow to be continuously adjusted without the need for manual intervention. The automated workflows ensured that the CFD simulations delivered faster and more precise results, making potential efficiency improvements visible at an early stage.
Moreover, the AI-supported simulation enabled real-time adjustment of process parameters, significantly reducing development time while simultaneously increasing production quality. Through the intelligent use of simulation and machine learning, the processes were not only accelerated, but the rejection rate was also reduced, and energy efficiency was increased.
Conclusion
Through the implementation of AI-driven CFD simulations, EWIKON was able to significantly enhance the efficiency and precision of their hot runner systems. The use of advanced simulation technology enabled a detailed analysis and optimization of material and heat distribution, reducing production errors and improving the quality of the final product.
Automation and continuous improvement through machine learning led to faster adaptation of process parameters and shorter development times. EWIKON demonstrates how modern AI solutions can play a crucial role in the optimization and future-proofing of manufacturing processes, with positive effects on product quality, sustainability, and operating costs.
