Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines. Reinforcement models dynamically adjust equipment settings for producing products to account for equipment performance degradation over time and changes in operation performance.

A machine learning device included in a tool selecting apparatus includes a state observing unit that observes, as state variables indicative of a current environmental state, data related to machining condition, data related to cutting condition, data related to machining result, and data related to a tool, and a learning unit that, by using the state variables, learns distribution of the data related to the machining condition, the data related to the cutting condition, and the data related to the machining result, with respect to data related to the tool.

Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines. Reinforcement models dynamically adjust equipment settings for producing products to account for equipment performance degradation over time and changes in operation performance.

A machine learning device included in a tool selecting apparatus includes a state observing unit that observes, as state variables indicative of a current environmental state, data related to machining condition, data related to cutting condition, data related to machining result, and data related to a tool, and a learning unit that, by using the state variables, learns distribution of the data related to the machining condition, the data related to the cutting condition, and the data related to the machining result, with respect to data related to the tool.

A client terminal checks whether there is information indicating that forming is impracticable with reference to content of data for a forming apparatus to form a 3-dimensional object and acquires a feature amount and forming setting related to 3-dimensional forming of the data if it is determined that there is no information indicating that the forming is impracticable. The client terminal transmits the acquired feature amount and forming setting to a server and supplies information regarding forming evaluation which is based on the feature amount and the forming setting and is acquired from the server.

A client terminal checks whether there is information indicating that forming is impracticable with reference to content of data for a forming apparatus to form a 3-dimensional object and acquires a feature amount and forming setting related to 3-dimensional forming of the data if it is determined that there is no information indicating that the forming is impracticable. The client terminal transmits the acquired feature amount and forming setting to a server and supplies information regarding forming evaluation which is based on the feature amount and the forming setting and is acquired from the server.