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Toyoda Koki Kabushiki Kaisha

Yukinori Kakazu

Takao Yoneda

Kunihiko Unno

Masashi Yamanaka

Tomonari Kato

Moriaki Sakakura

Kazuya Hattori

Shiho Hattori

Masaaki Minagawa

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 checking the input-output characteristic 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. One of the nodes of the input layer is selected as a variable input element while the rest of the nodes are regarded as fixed input elements. A variable data input to the variable input element is varied within a predetermined variation range, while data input to the fixed input elements are fixed, so as to detect how the components of output data from the output layer change. The detected changes of the components of the output data are displayed as the input-output characteristic of the neural network.

Method and apparatus for checking input-output characteristic of neural

Disclosed is a processing apparatus capable of optimizing its rule base comprising a plurality of rules having an antecedent and a consequent each. The antecedent contains input conditions of machining, and the consequent describes the result of machining. The antecedents are applied to input condition data. The consequents of the applied rules yield machining result data that are evaluated for rule fitness. When the rule base is updated, some rules are deleted and some preserved depending on their degrees of fitness. Between given two rules having the same consequent, any number of corresponding conditional elements in their antecedents are exchanged so as to generate new rules. The process is repeated until the rule base is optimized. With the antecedents of rules applied to input condition data and with the consequents of the applied rules yielding machining result data, those data may be modified according to modification request data. The consequents of rules are applied to the modified machining data, and the antecedents of these applied rules furnish corrective input condition data. This backward application of rules is an easy way to obtain corrective input condition data.

Rule fitness determination, rule removal, and new rule generation

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Hokkaido, Japan

Yukinori Kakazu