Data-efficient determination of machine-specific process windows in thermoforming using the example of PCR materials, TAPPI Journal July 2025

 


Application: The presented method offers a quantitative, practical comparison of material conditions for different materials during forming. This means that empirical values and data collected using a single material with one or more formats can be utilized for process optimization, such as material substitution across competing materials for thermoforming applications.

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Author: Felix Lange, Florian Schaller, Fabian Kayatz, and Marek Hauptmann
Data-efficient determination of machine-specific process win
ABSTRACT: In an industrial context, process windows for thermoplastics in thermoforming processes are still often determined through time-consuming trial-and-error approaches. This results in increased effort when commissioning new machines, implementing new technologies, or substituting sheet materials. One key reason is the lack of methods that allow for efficient, process-related assessment of material behavior and a quantitative definition of a “target state” of the heated sheet in relation to geometry and process conditions. In this study, we present the In-Situ Thermoforming Characterization (ITC) method as an application-oriented approach that enables format-independent evaluation of material behavior directly within the forming station of a thermoforming system. The method was successfully applied to a material substitution case, replacing conventional virgin polypropylene (PP) with post-consumer recyclate (PCR) — in this case, recycled PP (rPP) — in the production of a defined cup geometry. The results enabled the transfer of existing process knowledge from the virgin material to the recyclate, thereby accelerating material qualification. Based on the collected data, material behavior under process conditions could be mapped within the design space, making it possible to identify machine settings that deliver equivalent forming results in our test setup. Overall, the method shows strong potential for efficient and precise determination of machine-specific process windows.
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