Toyoda Koki Kabushiki Kaisha

Other

Shiho Hattori

Takao Yoneda

Masashi Yamanaka

Kunihiko Unno

Tomonari Kato

Moriaki Sakakura

Kazuya Hattori

Yukinori Kakazu

An intelligent machining system employs a neural network for calculating machining conditions on the basis of attribute data or a workpiece and a grinding machine. The system comprises a reference machining condition calculating unit, a neural network which receives the attribute data and provides corrections, and a correcting unit for correcting the reference machining conditions by using the corrections. Corrections which cannot be determined by means of empirical expressions or theoretical expressions are determined appropriately by the neural network previously learned. The system determines corrections for the machining conditions on the basis of machining errors decided by a neural network. The system also detects time-series machining phenomena by sensors, processes the output detection signals of the sensors by a neural network to obtain machining circumstance data. The feed rate of the tool is controlled by a fuzzy inference on the basis of the machining circumstance data.

Intelligent machining system

An apparatus for carrying out learning operation of a neural network which has an input layer, an intermediate layer and an output layer. Plural nodes of the input layer are related to plural nodes of the intermediate layer with plural connection weights, while the plural nodes of the intermediate layer are also related to plural nodes of the output layer with plural connection weights. Although input data composed of plural components and teaching data composed of plural components are used in learning operation, some of the components are ineffective for the purpose of learning operation. During error calculation between output data from the neural network and the teaching data, the apparatus judges whether each of the components of the teaching data is effective or ineffective, and output errors corresponding to the ineffective components are regarded as zero. The connection weights are thereafter corrected based upon the calculated errors. The apparatus further comprises means for adding new input data and teaching data into a data base, and means for calculating a degree of heterogeneousness of the new teaching data. The new input data and teaching data are added to the data base only when the degree of heterogeneousness is smaller than a predetermined value.

Method and apparatus for performing learning in a neural network

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Nagoya, Japan

Shiho Hattori