A method for automated part probing using a physical machine defining a physical working volume, the method including: generating a virtual model based on a virtual part design received from a user account, the virtual model comprising a virtual part model, based on the virtual part design, virtually fixed to a virtual fixture plate arranged within a virtual working volume representative of the physical working volume; generating a probing routine based on the virtual model; sending the probing routine to the machine; receiving probe outputs from the machine; and validating the virtual model based on the probe outputs.

A simulation apparatus includes a processor that executes a simulation of a control program executed on a controller. The controller controls motion of a machine that handles an object. The processor includes: a motion control device that controls motion of a virtual machine based on a motion command to move the virtual machine in a virtual space, with the virtual machine corresponding to the machine; a determination device that determines whether a volume of a region, where a work space in which the virtual machine works overlaps with the virtual object, is equal to or greater than a predetermined reference value, the virtual object being handled by the virtual machine and corresponding to the object; and a follow-up device that makes the virtual object follow the motion of the virtual machine based on the motion command when the volume is equal to or greater than the reference value.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining portions of an item that would be unsupported during three-dimensional printing. One of the methods includes obtaining data representing a three-dimensional model of an item to be created by a three-dimensional printer, processing data representing discrete portions of an upper layer of the three-dimensional model that are labelled as unsupported to place supports for at least some of the discrete portions that are labelled as unsupported, processing data representing any remaining discrete portions of the upper layer that are labelled as unsupported and for which a support for the remaining discrete portion would intersect with a lower portion in the three-dimensional model, generating, for each of the discrete portions labelled as a portion at which to generate a support, a support for the discrete portion in the three-dimensional model.

A device that estimates a life of a clamping mechanism clamping rotation of a rotary table includes a machine learning device. The machine learning device observes operating state data of the rotary table and operation history data of the rotary table as a state variable indicative of a current state of an environment, and acquires life data indicative of the life of the clamping mechanism as label data. In addition, the device uses the state variable that has been observed and the label data that has been acquired and learns the operating state data and the operation history data and the life data in association with each other.

Automated fixture layout is approached in two distinct stages. First, the spatial locations of clamping points on the work piece are determined to ensure immobility of the fixtured part under any infinitesimal perturbation. Second, spatial locations are matched against a user-specified library of reconfigurable clamps to synthesize a valid fixture layout or configuration that includes clamps that are accessible and collision free. The spatial locations matching during the second stage can be the same spatial locations chosen in the first stage to ensure immobility, or a different set of spatial locations.

A CNC machining device (1) comprises a control console (3) and a CNC machine (5). A training assembly (33) for training operation of the CNC machining device includes a training control console (35) substantially identical to the control console (3) of the CNC machining device (1); a digital twin (36) of the CNC machine (5), comprising a simulator (37) configured to simulate the effect of commands from the training control console (35) on the CNC machine (5); and a display device (39) configured for a trainee to view the current state of the simulator (37).

A method for automated part probing using a physical machine defining a physical working volume, the method including: generating a virtual model based on a virtual part design received from a user account, the virtual model comprising a virtual part model, based on the virtual part design, virtually fixed to a virtual fixture plate arranged within a virtual working volume representative of the physical working volume; generating a probing routine based on the virtual model; sending the probing routine to the machine; receiving probe outputs from the machine; and validating the virtual model based on the probe outputs.

Automated fixture layout is approached in two distinct stages. First, the spatial locations of clamping points on the work piece are determined to ensure immobility of the fixtured part under any infinitesimal perturbation. Second, spatial locations are matched against a user-specified library of reconfigurable clamps to synthesize a valid fixture layout or configuration that includes clamps that are accessible and collision free. The spatial locations matching during the second stage can be the same spatial locations chosen in the first stage to ensure immobility, or a different set of spatial locations.

A method for automated part probing using a physical machine defining a physical working volume, the method including: generating a virtual model based on a virtual part design received from a user account, the virtual model comprising a virtual part model, based on the virtual part design, virtually fixed to a virtual fixture plate arranged within a virtual working volume representative of the physical working volume; generating a probing routine based on the virtual model; sending the probing routine to the machine; receiving probe outputs from the machine; and validating the virtual model based on the probe outputs.

A method of designing (300) an optimised component (100) for a load-bearing application comprises the steps of: analysing (302) a solid component (200) having an external shape to determine a stress distribution in the solid component (200) in response to an applied load; and defining (304), based upon the determined stress distribution in the solid component (200), a structure for an optimised component (100) comprising an outer skin layer (108) and an internal filament structure (110). The outer skin layer (108) forms an external shape substantially identical to the external shape of the solid component (200) and the internal filament structure (110) is enclosed by the outer skin layer (108), such that the optimised component (100) is configured to bear the applied load.