Injection molding machine system
Provided is an injection molding machine system ( 1 ) that performs control of molding conditions in an injection molding machine ( 2 ) by an agent ( 6 ) including a machine learning device which performs reinforcement learning. In the present learning, physical data obtained from the injection molding machine ( 2 ) and a defect type indicating the type of a molding defect in a molded article are used as states, molding conditions are used as actions, and a defect state indicating the defect level of the molding defect is used as a reward.
1. An injection molding machine system comprising:
an agent having a machine learner, the machine learner performing reinforcement learning of determining an action according to a value function while receiving rewards for actions done in various states and learning the value function, and
an injection molding machine configured to manufacture a mold product under prescribed molding conditions; and
the injection molding machine system being configured to adjust the molding conditions using the agent,
wherein the machine learner is configured to:
use, as the state, only physical data obtained from the injection molding machine and a defect type representing a kind of a molding defect of the mold product;
use the molding conditions as the action; and
use, as the reward, a defect state indicating a defect degree of a molding defect.
2. The injection molding machine system according to claim 1 , further comprising:
a defect judging device configured to measure the mold product; and
a classifier configured to perform learning through supervised learning,
wherein the machine learner is configured to use, as the defect type and the defect state, output data obtained from the classifier when input data including measurement data of the mold product measured by using the defect judging device is input to the classifier that has performed the learning.
3. The injection molding machine system according to claim 2 ,
wherein the classifier is configured to perform the learning by using plural actual product data sets each including the measurement data, the defect type, and the defect state of the actual mold product and plural quasi-data sets, and
wherein the quasi-data sets include the measurement data, the defect type, and the defect state obtained by modifying the actual product data sets.
4. The injection molding machine system according to claim 1 , wherein the defect type includes at least one of a sink mark, a burr, a void, a short shot, burn marks, a warping, sink marks, weld lines, a jetting, or flow lines formed in the mold product by injection molding.
5. The injection molding machine system according to claim 1 , wherein the defect type includes at least one of a sink mark, a burr, or a void formed in the mold product by injection molding.
6. The injection molding machine system according to claim 1 ,
wherein the machine learner is configured to use the physical data obtained from the injection molding machine and the defect type output from a classifier as the state, so that the machine learner judges, according to the defect type, what molding condition should be made an adjustment target and determines optimum molding conditions as an action (a t ) under a given state (s t ), and
wherein the machine learner performs the reinforcement learning by using the state (s t ), the action (a t ), and the rewards (r t ), the machine learner learning by leaving how action branching should occur depending on the defect type to an algorithm.