A method of controlling sensing level in a liquid ejector is disclosed. The method includes filling a reservoir in communication with a liquid ejector with a printing material to a first level set point, receiving a drop out signal from a laser-based level sensor that reads from a surface of a melt pool in the reservoir, pausing an operation of the liquid ejector, adjusting the printing material level set point to a second level set point of printing material in reservoir that is higher than the first level set point, increasing a quantity of printing material in the reservoir to fill the reservoir to the second level set point, and resuming the operation of the liquid ejector.

A method of controlling sensing level in a liquid ejector is disclosed. The method includes filling a reservoir in communication with a liquid ejector with a printing material to a first level set point, receiving a drop out signal from a laser-based level sensor that reads from a surface of a melt pool in the reservoir, pausing an operation of the liquid ejector, adjusting the printing material level set point to a second level set point of printing material in reservoir that is higher than the first level set point, increasing a quantity of printing material in the reservoir to fill the reservoir to the second level set point, and resuming the operation of the liquid ejector.

Provided is a casting simulation method capable of expressing influence of different inelastic strains produced at different temperatures on strain hardenability at room temperature. The following amount of effective equivalent inelastic strain ε.sub.effective inelastic is substituted into a constitutive equation in which an amount of equivalent inelastic strain is used as a degree of work hardening:

an amount of effective equivalent inelastic strain ε.sub.effective inelastic=∫.sub.o.sup.t{h.sub.(T)/h.sub.(RT)}{(Δε.sub.inelastic/Δt)}dt

, where T denotes a temperature with inelastic strain, h.sub.(T) denotes an increment of yield strength at room temperature with respect to an amount of inelastic strain at the temperature with inelastic strain, h.sub.(RT) denotes an increment of yield strength at room temperature with respect to an amount of inelastic strain applied at room temperature, h.sub.(T)/h.sub.(RT) denotes an effective inelastic strain coefficient α(T), Δε.sub.inelastic/Δt denotes an equivalent inelastic strain rate, and t denotes a time from 0 second in analysis.

Provided is a casting simulation method capable of expressing influence of different inelastic strains produced at different temperatures on strain hardenability at room temperature. The following amount of effective equivalent inelastic strain ε.sub.effective inelastic is substituted into a constitutive equation in which an amount of equivalent inelastic strain is used as a degree of work hardening:

an amount of effective equivalent inelastic strain ε.sub.effective inelastic=∫.sub.o.sup.t{h.sub.(T)/h.sub.(RT)}{(Δε.sub.inelastic/Δt)}dt

, where T denotes a temperature with inelastic strain, h.sub.(T) denotes an increment of yield strength at room temperature with respect to an amount of inelastic strain at the temperature with inelastic strain, h.sub.(RT) denotes an increment of yield strength at room temperature with respect to an amount of inelastic strain applied at room temperature, h.sub.(T)/h.sub.(RT) denotes an effective inelastic strain coefficient α(T), Δε.sub.inelastic/Δt denotes an equivalent inelastic strain rate, and t denotes a time from 0 second in analysis.

A prediction system configured to predict a defect of a target product includes a first pre-trained model trained based on a defect characteristic value indicating a defect associated with a location in an existing product, a feature of a three-dimensional shape of the existing product, and conditional information indicating a manufacturing condition of the existing product. The first pre-trained model is configured to, when a feature of a three-dimensional shape of the target product is input, output a defect characteristic value indicating a defect associated with a location in the target product.

A twin roll casting system includes a pair of counter-rotating casting rolls having an adjustable nip therebetween, a casting roll controller configured to adjust the nip between the casting rolls in response to control signals; a cast strip sensor measuring a parameter of the cast strip and generating strip measurement signals; and an iterative learning control (ILC) controller receiving the strip measurement signals and providing control signals to the casting roll controller. The ILC controller includes a fault detection algorithm receiving the control signals and the strip measurement signals and generating a fault detection signal indicating when a fault condition is detected and an iterative learning control algorithm to generate the control signals. The fault detection algorithm indicates a fault condition when it detects the control signal exceeding an upper control saturation threshold or the ILC controller operating a state that is not guaranteed as stable.

A twin roll casting system includes a pair of counter-rotating casting rolls having an adjustable nip therebetween, a casting roll controller configured to adjust the nip between the casting rolls in response to control signals; a cast strip sensor measuring a parameter of the cast strip and generating strip measurement signals; and an iterative learning control (ILC) controller receiving the strip measurement signals and providing control signals to the casting roll controller. The ILC controller includes a fault detection algorithm receiving the control signals and the strip measurement signals and generating a fault detection signal indicating when a fault condition is detected and an iterative learning control algorithm to generate the control signals. The fault detection algorithm indicates a fault condition when it detects the control signal exceeding an upper control saturation threshold or the ILC controller operating a state that is not guaranteed as stable.

System and method for additive casting of metal objects by constructing production layers having mold regions and object regions includes a mold construction unit to construct a mold region of the current production layer; a Preparation-Deposition-Post (PDP) unit including: a molten metal depositor to deposit molten metal in an object region; a holder for holding the molten metal depositor; at least one induction heating unit; a build table for supporting the vertical stack of production layers; a movable platform to provide relative movement between the PDP unit and the build table; and a controller for controlling the PDP unit and the movable platform to deposit molten metal in a fabrication area, and to control the PDP unit to perform (1) pre-heating the fabrication area before molten metal deposition, to a pre-deposition temperature, and/or (2) post-heating the fabrication area after molten metal deposition, to a post-deposition temperature.

System and method for additive casting of metal objects by constructing production layers having mold regions and object regions includes a mold construction unit to construct a mold region of the current production layer; a Preparation-Deposition-Post (PDP) unit including: a molten metal depositor to deposit molten metal in an object region; a holder for holding the molten metal depositor; at least one induction heating unit; a build table for supporting the vertical stack of production layers; a movable platform to provide relative movement between the PDP unit and the build table; and a controller for controlling the PDP unit and the movable platform to deposit molten metal in a fabrication area, and to control the PDP unit to perform (1) pre-heating the fabrication area before molten metal deposition, to a pre-deposition temperature, and/or (2) post-heating the fabrication area after molten metal deposition, to a post-deposition temperature.

Casting facility includes a pouring machine for pouring molten metal in a ladle into a mold molded by a molding machine and conveyed to a pouring site, and the pouring machine includes a plan acquisition unit configured to acquire a planned temperature range of the molten metal for the mold, a temperature sensor configured to detect a temperature of a pouring flow during pouring of the molten metal into the mold, and a temperature determination unit configured to determine whether or not the temperature of the pouring flow is within the planned temperature range, and the pouring machine stops the pouring of the molten metal into the mold when it is determined that the temperature of the pouring flow is not within the planned temperature range.