Kabushiki Kaisha Toshiba

Toshiba Digital Solutions Corporation

Shintarou Takahashi

Toshiyuki Katou

Minoru Nishizawa

Toru Ito

Takashi Morimoto

Rumi Hayakawa

Hidemasa Itou

A manufacturing condition output apparatus of an embodiment is a manufacturing condition output apparatus which outputs a manufacturing condition of a product. The manufacturing condition output apparatus outputs change degree information which is information regarding degrees of change of values regarding defect probabilities for a plurality of variables relating to manufacturing of the product from model information of a model generated through machine learning on a basis of manufacturing data of the product and inspection result data of the product, as a manufacturing condition.

Manufacturing condition output apparatus, quality management system, and storage medium

A product state estimation device includes: an examination result acquisition device that acquires an examination result related to a state of a product obtained through each shot by a die-casting machine; a time series data acquisition device that acquires time series data based on an output from a sensor that detects an operation state of the die-casting machine at each shot; a time series data manipulation device that performs manipulation that clips data of a predetermined time interval out of the time series data; an estimation model generation device that generates an estimation model by using a neural network with the examination result of the product and the manipulated time series data as learning data; and an estimation device that estimates information related to quality of the product based on the manipulated time series data obtained from a plurality of detection signals at each shot by using the estimation model.

Product state estimation device

An abnormality detection device of an embodiment includes an encoder, a first identifier, a decoder, and a second identifier. The encoder is configured to compress input data using a compression parameter to generate a compressed data. The first identifier is configured to determine whether a distribution of the compressed data input by the encoder is a distribution of the compressed data or a prior distribution prepared in advance, and inputs a first identification result to the encoder. The decoder is configured to decode the compressed data using a compressing parameter to generate reconstructed data. The second identifier is configured to determine whether the reconstructed data input by the decoder is the reconstruction data or the input data and outputs a second identification result to the encoder and the decoder.

Abnormality detection device, learning device, abnormality detection method, and learning method

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Kawasaki, Japan

Shintarou Takahashi