Kobe Steel, Ltd.

Kazuo Miyasaka

Yasuaki Yamane

Kazuhisa Fukutani

Norifumi Yamada

Kosuke Higashi

Hikaru Hamada

Yasuhiro Kameda

A machine learning method includes: acquiring a state variable including at least one first evaluation parameter related to performance evaluation of a kneaded product and at least one kneading condition; calculating a reward for a decision result of the at least one kneading condition based on the state variable; updating a function for deciding the at least one kneading condition from the state variable based on the reward; and by repeating the update of the function, deciding a kneading condition under which the reward obtained becomes maximum, in which the at least one first evaluation parameter includes at least one of physical properties and shape characteristics related to the kneaded product.

Machine learning method, machine learning device, machine learning program, communication method, and kneading device

A sealed kneading machine includes a kneading chamber to which a material to be kneaded is supplied, and a rotor disposed in the kneading chamber to be able to rotate about a rotor shaft. The rotor includes a plurality of kneading blades for kneading the material to be kneaded, and the length of all the kneading blades in the rotor shaft direction of each thereof is 45% or less of the total length of the rotor in the rotor shaft direction.

Internal mixer

Growing community of inventors

Takasago, Japan

Kazuo Miyasaka