Machine learning for splice improvement

A method of manufacturing a tire, the method comprising:

• one or more processors and a memory, the memory configured to store instructions that are executable by the one or more processors and cause the one or more processors to:
• receiving, by one or more processors from one or more sensors of one or more pieces of tire manufacturing equipment in a tire manufacturing plant, one or more values corresponding to manufacture of a tire by the one or more pieces of tire manufacturing equipment;
• determining, by the one or more processors, one or more metrics based on the one or more values;
• generating, by the one or more processors, a matrix based on the one or more values and the one or more metrics;
• predicting, by the one or more processors, via input of the matrix into a machine learning model constructed to output tire manufacturing performance data, a value for a splice tolerance metric for the tire, wherein the value for the splice tolerance metric predicts that a first splice of a plurality of splices of the tire exceeds a tolerance window;
• determining, by the one or more processors based on the value for the splice tolerance metric, a parameter of at least one piece of equipment in the tire manufacturing plant to adjust;
• and
• providing, by the one or more processors, a command to adjust a first parameter used by the at least one piece of equipment in the tire manufacturing plant responsive to the value of the splice tolerance metric, wherein the first parameter includes an amount of pressure applied by the at least one piece of equipment;
• receiving, by the one or more processors, subsequent to the adjustment of the first parameter, one or more values corresponding to the manufacture of the tire by the one or more pieces of tire manufacturing equipment;
• generating, by the one or more processors, an updated matrix based on the one or more values received subsequent to the adjustment of the first parameter;
• predicting, by the one or more processors, via input of the updated matrix into the machine learning model, that the first splice for the tire does not satisfy the tolerance window;
• and
• adjusting, by the one or more processors, responsive to the prediction that the first splice for the tire does not satisfy the tolerance window subsequent to the adjustment, at least one of a second parameter of the at least one piece of equipment or a parameter of a second piece of equipment of the tire manufacturing plant;
• receive, subsequent to the adjustment of the second parameter or the parameter of the second piece of equipment, one or more values corresponding to the manufacture of the tire by the one or more pieces of tire manufacturing equipment;
• generate an updated matrix based on the one or more values received subsequent to the adjustment of the second parameter or the parameter of the second piece of equipment;
• and
• predict, via input of the updated matrix into the machine learning model, that the first splice for the tire satisfies the tolerance window.