Method for preventing defect caused by shift in cavity parts

    公开(公告)号:US11045866B2

    公开(公告)日:2021-06-29

    申请号:US16965871

    申请日:2018-11-15

    Abstract: A method is provided for measuring a shift between a carrier for a pattern (a carrier plate) and a flask and preventing a defect caused by a shift in the cavity parts. The method for preventing a defect caused by the shift in the cavity parts in molding a cope and a drag with flasks by using a cope flask (110) that is assembled with a carrier plate (130) for the cope flask and a drag flask (120) that is assembled with a carrier plate (140) for the drag flask, comprises the steps of measuring a shift between the carrier plate (130) for the cope flask and the cope flask (110), measuring a shift between the carrier plate (140) for the drag flask and the drag flask (120), measuring a shift between the cope flask (110) and the drag flask (120) that have been assembled, determining if a shift in cavity parts is within an allowable range, wherein the data on the shift is obtained based on the shift between the carrier plate (130) for the cope flask and the cope flask (110), the shift between the carrier plate (140) for the drag flask and the drag flask (120), and the shift between the cope flask (110) and the drag flask (120, that have been assembled.

    Loss-on-ignition estimation apparatus, loss-on-ignition estimation method, machine-learning apparatus, and machine-learning method

    公开(公告)号:US11976790B2

    公开(公告)日:2024-05-07

    申请号:US17190974

    申请日:2021-03-03

    CPC classification number: F17D1/088 B22C19/04 G06N20/00

    Abstract: An object is to accurately estimate loss-on-ignition in a short time. A loss-on-ignition estimation apparatus includes at least one processor configured to carry out an estimation step, the estimation step including estimating the loss-on-ignition of foundry sand with use of a learned model constructed by means of machine learning. The learned model is configured to receive, as input, (1) sand weight data relating to a weight of the foundry sand detected in a calcination period and (2) at least one of (i) sand property data relating to one or more properties of the foundry sand, (ii) additive data relating to one or more additives added to the foundry sand, and (iii) calcination environment data relating to a calcination environment detected in the calcination period. The learned model is configured to generate, as output, an estimated loss-on-ignition of the foundry sand.

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