US 12,420,452 B2
Machine learning method, machine learning device, machine learning program, communication method, and kneading device
Hikaru Hamada, Takasago (JP); Yasuaki Yamane, Takasago (JP); Norifumi Yamada, Takasago (JP); and Kazuo Miyasaka, Takasago (JP)
Assigned to KOBE STEEL, LTD., Hyogo (JP)
Appl. No. 17/996,397
Filed by KOBE STEEL, LTD., Hyogo (JP)
PCT Filed May 14, 2021, PCT No. PCT/JP2021/018408
§ 371(c)(1), (2) Date Oct. 17, 2022,
PCT Pub. No. WO2021/241278, PCT Pub. Date Dec. 2, 2021.
Claims priority of application No. 2020-094033 (JP), filed on May 29, 2020; and application No. 2020-205926 (JP), filed on Dec. 11, 2020.
Prior Publication US 2023/0202070 A1, Jun. 29, 2023
Int. Cl. B29B 7/72 (2006.01); B29B 7/60 (2006.01)
CPC B29B 7/728 (2013.01) [B29B 7/603 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A machine learning method for a machine learning device to decide a kneading condition of a kneading device for kneading a polymer material to obtain a kneaded product, the kneading device including:
a chamber to which a material to be kneaded is input;
two or more rotors which knead the material input to the chamber; and
a controller in charge of control of the two or more rotors, control of a kneading time of the material, and control of an operation step of the kneading device, the machine learning method comprising:
acquiring a state variable including at least one first evaluation parameter related to performance evaluation of the 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,
wherein the at least one first evaluation parameter includes at least one of physical properties and shape characteristics related to the kneaded product.