PROCESS FOR PREPARING A FLUID CONDUIT
    2.
    发明申请

    公开(公告)号:US20200181320A1

    公开(公告)日:2020-06-11

    申请号:US16318792

    申请日:2017-07-11

    Abstract: The invention relates to a process for preparing a fluid conduit comprising a mono-layer comprising a thermoplastic elastomer in an amount of at least 80 wt % with respect to the total weight of the mono-layer, comprising at least the 5 following steps: a. Melting a composition comprising at least a thermoplastic elastomer having a melt volume flow rate measured at 230° C. under a load of 10 kg (MVR 230° C./10 kg), according to IS01133 (2011) of at most 40 g/10 min and having a heat resistance of at least 250 hours at 175° C. at which the 10 elongation at break remains at least 100% as measured according to ISO 527 with a test speed of 50 mm/min; b. Forming a parison from the melt; c. Placing the parsion in a mold; d. Blow-up the parison against the mold; 15 e. Cooling down the mold, thereby obtaining the fluid conduit comprising the mono-layer. The invention also relates to a fluid conduit.

    SYSTEM AND METHOD FOR GENERATING A RECIPE FOR A THERMOPLASTIC COMPOUND

    公开(公告)号:US20250013920A1

    公开(公告)日:2025-01-09

    申请号:US18709689

    申请日:2022-10-03

    Abstract: A processor system and method (100) are provided for generating a recipe for a thermoplastic compound, wherein the recipe defines a set of ingredients and a relative contribution of the ingredients for manufacturing the thermoplastic compound. The ingredients may comprise additives to be added to a base polymer. The recipe may be generated by training (110) a machine learnable model on compound data (20) of existing (historical) compounds to predict values of compound material properties from an input recipe, providing (120) candidate recipes, selecting 1 (40) a best recipe based on a scoring function, outputting (150) the selected recipe, e.g., via a display, to enable a sample of the compound to be manufactured (200) and measured (210), receiving (160) measurement data of the sample and determining a deviation to a target specification, and determining (170) if the recipe is acceptable. If the recipe is not acceptable, the machine learned model may be retrained or updated based on the measurement data and the recipe of the sample and the above-identified steps may be repeated until a recipe meets the target specification.

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