Abstract: A machine learning device includes a sparse modeling processing unit and a selection unit. The sparse modeling processing unit acquires individual importance degrees for each of explanatory variable candidates, the individual importance degrees being acquired by using respective sparse modeling methods different from each other, each of the sparse modeling methods taking input data including a specified objective variable in a learning model used for industrial activity and the explanatory variable candidates that are candidates for an explanatory variable for explaining the specified objective variable. The selection unit calculates a comprehensive importance degree for each of the explanatory variable candidates based on the individual importance degrees of each of the explanatory variable candidates, and selects an explanatory variable of the learning model from among the explanatory variable candidates based on the comprehensive importance degree.

Abstract: A machine learning device includes a sparse modeling processing unit and a selection unit. The sparse modeling processing unit acquires individual importance degrees for each of explanatory variable candidates, the individual importance degrees being acquired by using respective sparse modeling methods different from each other, each of the sparse modeling methods taking input data including a specified objective variable in a learning model used for industrial activity and the explanatory variable candidates that are candidates for an explanatory variable for explaining the specified objective variable. The selection unit calculates a comprehensive importance degree for each of the explanatory variable candidates based on the individual importance degrees of each of the explanatory variable candidates, and selects an explanatory variable of the learning model from among the explanatory variable candidates based on the comprehensive importance degree.

Abstract: To provide a quality prediction system predicting a quality element of a molded item using machine learning. The quality prediction system includes a sensor disposed in the mold and configured to detect state data regarding the molten material supplied in the cavity, a learned-model storage unit configured to store a model which is a learned model generated by machine learning in which the state data detected by at least the sensor is used as a training data set and is a learned model related to the state data and a quality element of the molded item, and a quality prediction unit configured to predict the quality element of the molded item which is newly molded based on the state data newly detected by the sensor and the learned model.

Abstract: A deterioration determining apparatus includes: an operating condition acquirer to acquire an operating condition of a processing device; a processing state data acquirer to acquire processing state data detected by a sensor attached to the processing device; a learning model generator to conduct machine learning using, as learning data, the operating condition and the processing state data so as to preliminarily generate a learning model concerning the operating condition and the processing state data; an actual data acquirer to acquire actual data that is the processing state data at a determining time; a predicted data acquirer to acquire, using the learning model, predicted data that is the processing state data for the operating condition at the determining time; and a determiner to determine the degree of deterioration in the processing device in accordance with the degree of divergence between the actual data and the predicted data.

Abstract: A device for assisting molding condition determination is used with a molding method that molds an article by feeding molten material into a mold. The device includes a learning model generating unit, an input unit, and an output unit. The learning model generating unit creates a learning model through machine learning in which a plurality of molding condition element items used to mold the article and a plurality of quality element items of the molded article are used as learning data. The learning model relates to a degree of influence of each molding condition element item on each quality element item. The input unit receives input of a subject quality element item to be checked, selected from the quality element items. The output unit outputs, using the learning model, the multiple molding condition element item that has the degree of influence on the subject quality element item.

Abstract: A device for assisting molding condition determination includes a molding state data adjustment amount obtaining unit and a molding condition element adjustment amount obtaining unit. The molding state data adjustment amount obtaining unit obtains, using a first learning model, a molding state data adjustment amount having a value equivalent to a difference between molding state data detected by a sensor and a molding state data target value. The molding condition element adjustment amount obtaining unit obtains, using a second learning model, an adjustment amount for a molding condition element corresponding to the molding state data adjustment amount.