Self-learning manufacturing using digital twins

A computer program product comprising:

• one or more computer readable storage media having computer-readable program instructions stored on the one or more computer readable storage media, said program instructions executes a computer-implemented method comprising:
• developing, by a processor, a reinforcement learning model using a historical rules database comprising existing rules translating commercial characteristics of a product into manufacturing characteristics, and historical production settings comprising quality parameters achieved during historical production of on-specification products in accordance with the existing rules;
• inputting, by the processor, a set of commercial characteristics to produce the product, wherein the product is steel, and a detected degradation of one or more product manufacturing equipment into the reinforcement learning model;
• receiving, by the processor, output from the reinforcement learning model translating the set of commercial characteristics into manufacturing characteristics;
• producing the product, by the processor controlling the one or more product manufacturing equipment, with adjustments to the manufacturing characteristics to compensate for the detected degradation of the one or more product manufacturing equipment;
• recording, by the processor, quality characteristics of the product produced by one or more manufacturing systems and production settings of the one or more manufacturing systems used to produce the product;
• comparing, by the processor, the quality characteristics of the product produced by the one or more manufacturing systems with the commercial characteristics of the product;
• rewarding, by the processor, the reinforcement learning model for correctly translating the commercial characteristics to the manufacturing characteristics, whereupon the quality characteristics of the product match the commercial characteristics;
• and
• penalizing, by the processor, the reinforcement learning model for incorrectly translating the commercial characteristics to the manufacturing characteristics, whereupon the quality characteristics of the product do not match the commercial characteristics.

A computer system comprising:

• a
• processor;
• one or more manufacturing systems in communication with the processor;
• and
• a computer-readable storage media coupled to the processor, wherein the computer-readable storage media contains program instructions executing a computer-implemented method comprising the steps of:
• developing, by the processor, a reinforcement learning model using a historical rules database comprising existing rules translating commercial characteristics of a product into manufacturing characteristics, and historical production settings comprising quality parameters achieved during historical production of on-specification products in accordance with the existing rules;
• inputting, by the processor, a set of commercial characteristics to produce the product, wherein the product is steel, and a detected degradation of one or more product manufacturing equipment into the reinforcement learning model;
• receiving, by the processor, output from the reinforcement learning model translating the set of commercial characteristics into manufacturing characteristics;
• producing the product, by the processor controlling the one or more product manufacturing equipment, with adjustments to the manufacturing characteristics to compensate for the detected degradation of the one or more product manufacturing equipment;
• recording, by the processor, quality characteristics of the product produced by the one or more manufacturing systems and production settings of the one or more manufacturing systems used to produce the product;
• comparing, by the processor, the quality characteristics of the product produced by the one or more manufacturing systems with the commercial characteristics of the product;
• rewarding, by the processor, the reinforcement learning model for correctly translating the commercial characteristics to the manufacturing characteristics, whereupon the quality characteristics of the product match the commercial characteristics;
• and
• penalizing, by the processor, the reinforcement learning model for incorrectly translating the commercial characteristics to the manufacturing characteristics, whereupon the quality characteristics of the product do not match the commercial characteristics.