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This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications. 

Abstract This study aims to establish a systematic approach for characterizing recycled polyolefins of unknown composition, with a specific focus on predicting their performance in film extrusion. We explore various characterization techniques, including differential scanning calorimetry (DSC), Fourier‐transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and rheometry to assess their effectiveness in identifying the polyethylene (PE) fractions within polypropylene (PP) recyclates. By integrating experimental data with modeling techniques, we aim to provide insights into the predictive capabilities of these techniques in determining processing behaviors. The research highlights the superior fidelity of DSC in predicting the relative fraction and type of PE in a PP recyclate. FTIR is also identified as a high‐fidelity approach, albeit requiring application‐specific calibration. TGA, capillary, and oscillatory rheometry are recognized for their ability to distinguish between grades of recycled polyolefins but provide aggregate behaviors rather than detailed constituent information. 3D flow simulation of the cast film extrusion investigated the effect of the viscosity characterization method, non‐isothermal assumption, and process settings but could not fully replicate the observed variations in the cast film processing of two industrial polyolefins with similar melt flow rates and viscosity behaviors. This underscores the practical challenge of predicting processing issues prior to actual processing, necessitating reliance on reliable instrumentation suites and human expertise for diagnosing and remedying variations. HighlightsTwo industrial recycled polypropylene materials having similar melt flow rates exhibit drastically different cast film processing behaviors.DSC and FTIR provide reasonable approaches for identifying constituent materials.Modeling of the melt viscosities characterized by capillary and parallel plate rheology suggests that viscosity variations relative to the power‐law behavior assumed in the coat hanger die design is a predominant driver of cast film instabilities. 

Graphical abstract 

OpenMSIStream provides seamless connection of scientific data stores with streaming infrastructure to allow researchers to leverage the power of decoupled, real-time data streaming architectures. Data streaming is the process of transmitting, ingesting, and processing data continuously rather than in batches. Access to streaming data has revolutionized many industries in the past decade and created entirely new standards of practice and types of analytics. While not yet commonly used in scientific research, data streaming has the potential to become a key technology to drive rapid advances in scientific data collection (e.g., Brookhaven National Lab (2022)). This paucity of streaming infrastructures linking complex scientific systems is due to a lack of tools that facilitate streaming in the diverse and distributed systems common in modern research. OpenMSIStream closes this gap between underlying streaming systems and common scientific infrastructure. Closing this gap empowers novel streaming applications for scientific data including automation of data curation, reduction, and analysis; real-time experiment monitoring and control; and flexible deployment of AI/ML to guide autonomous research. Streaming data generally refers to data continuously generated from multiple sources and passed in small packets (termed messages). Streaming data messages are typically organized in groups called topics and persist for periods of time conducive to processing for multiple uses either sequentially or in small groups. The resulting flows of raw data, metadata, and processing results form “ecosystems” that automate varied data-driven tasks. A strength of data streaming ecosystems is the use of publish-subscribe (“pub/sub”) messaging backbones that decouple data senders (publishers) and recipients (subscribers). Popular message-focused middleware solutions such as RabbitMQ (VMware, 2022), Apache Pulsar (Apache Software Foundation, 2022b), and Apache Kafka (Apache Software Foundation, 2022a) all provide differing capabilities as backbones. OpenMSIStream provides robust and efficient, yet easy, access to the rich data streaming systems of Apache Kafka.