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

A machine learning device which decides 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 for obtaining the kneaded product 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 device comprising:
• a state acquisition unit which acquires a state variable including at least one first evaluation parameter related to performance evaluation of the kneaded product and at least one kneading condition;
• a reward calculation unit which calculates a reward for a decision result of the at least one kneading condition based on the state variable;
• an update unit which updates a function for deciding the at least one kneading condition from the state variable based on the reward;
• and
• a decision unit which, by repeating the update of the function, decides 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.

A non-transitory computer-readable recording medium which records a machine learning program of a machine learning device which decides 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 for obtaining the kneaded product 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 program causing a computer to function as,
• the machine learning device comprising:
• a state acquisition unit which acquires a state variable including at least one first evaluation parameter related to performance evaluation of the kneaded product and at least one kneading condition;
• a reward calculation unit which calculates a reward for a decision result of the at least one kneading condition based on the state variable;
• an update unit which updates a function for deciding the at least one kneading condition from the state variable based on the reward;
• and
• a decision unit which, by repeating the update of the function, decides 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.

A communication method conducted at the time of machine learning of a kneading condition for a kneading device which kneads a polymer material to obtain a kneaded product, the kneading device including:

• a chamber to which a material for obtaining the kneaded product 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 communication method comprising:
• observing a state variable including at least one first evaluation parameter related to performance evaluation of the kneaded product and at least one kneading condition;
• transmitting the state variable onto a network;
• and
• receiving a machine-learned kneading condition based on a reward for a decision result of the at least one kneading condition calculated based on the state variable, the at least one kneading condition decided based on updating a function from the state variable based on the reward, and the kneading condition machine-learned kneading condition decided by repeating the updating of the function 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.

A kneading device which kneads a polymer material to obtain a kneaded product, the kneading device comprising:

• a chamber to which a material for obtaining the kneaded product is input;
• two or more rotors which knead the material input to the chamber;
• 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;
• a state observation unit which acquires a state variable including at least one first evaluation parameter related to performance evaluation of the kneaded product and at least one kneading condition;
• and
• a communication unit which transmits the state variable onto a network and receives a machine-learned kneading condition based on a reward for a decision result of the at least one kneading condition calculated based on the state variable, the at least one kneading condition decided based on updating a function from the state variable based on the reward, and the kneading condition machine-learned kneading condition decided by repeating the updating of the function 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.