A dental machining system for manufacturing a dental restoration/appliance, including: a dental tool machine which includes: a dental blank holder for movably holding at least one dental blank relative to one or more dental tools; one or more driving units each for movably holding one or more dental tools, a control unit for controlling the dental blank holder and the driving units based on construction data of the dental restoration/appliance and a plurality of machining processes specific for the manufacturing of the dental restoration/appliance from the dental blank. The control unite executes a trained artificial intelligence algorithm that predicts the machining time for manufacturing the dental restoration/appliance based on input data including: process parameters defining the machining processes respectively; and mappings which include information on the target geometry of the dental restoration/appliance.

An arithmetic processing unit divides a modified NC program into a plurality of segments and determines a machining time estimation target segment among the plurality of divided segments. When estimating the machining time required for the machining of a machine tool controlled based on the modified NC program, the arithmetic processing unit shortens the time required for the machining time estimation process by reutilizing an estimated machining time for an NC program before modification with respect to segments other than the machining time estimation target segment.

The object of the present invention is to provide a machining time estimation device for reducing the time required for estimating the machining time. The device comprises storage for storing machining program and machining setting, and machining information acquiring unit for acquiring machining information on an operating condition of a machine tool adapted to be controlled on the basis of the machining program and machining setting. The device further comprises varied element extraction unit for extracting an element varied between a plurality of said machining settings, effected block extraction unit for extracting an effected block the machining time is varied on the effect of the varied element, estimated reduction in time calculating unit for calculating on each effected block difference caused by the varied element and calculating estimated difference in time throughout the machining program, and machining time display unit for outputting the estimated difference in time.

Methods, systems and apparatus, including computer programs encoded on computer storage medium, for controlling operations of machine tool workstations. Machine tool workstations are grouped into functional groups. Neural networks corresponding to the functional groups are trained to process respective inputs representing parts to be processed to generate respective outputs representing sequences of ordered subsets of the parts that produce a reduced setup time for workstations in the functional groups. Data representing respective collections of parts to be processed by workstations included in the functional groups is processed using the trained neural networks to generate corresponding sequences of ordered subsets of the collection of parts. Average delay times associated with the generated sequences of ordered subsets of the collection of parts are computed. If the average delay times are less than a predetermined threshold, parts are released to the functional groups for processing according to the generated sequences.

A camera photographs a part to be estimated, which is produced by cutting a sheet metal in advance, and a dimension reference marker. An image processing unit generates edge data by extracting an edge of the part photographed by the camera, and enlarges or reduces the edge data based on a size of the dimension reference marker photographed by the camera such that a size of the edge corresponds to an actual size of the part. A machining time calculation unit calculates a length of a cutting line for cutting out the part from the sheet metal based on the edge of the edge data corresponding to the actual size of the part, and calculates a machining time for producing the part by cutting the cutting line in accordance with a material and a thickness of the sheet metal.

A method predicts an expected waiting time for a route having a plurality of production operations in manufacturing. The method includes receiving a sorted list of production operations characterizing a route for manufacturing a lot, and defining a starting time point of a lot production start time. The method further includes, for each production operation in the sorted list, (i) sampling feature values for a plurality of features by sampling from a database of collected feature values for operation measured feature values based on the starting time point, wherein the features characterize a property and/or a state of the lot and/or a property and/or a state of a factory for manufacturing the lot, and (ii) predicting an expected waiting time of each production operation based on the sampled feature values. The expected waiting time of each production operation is accumulated to determine the expected waiting time for the route.

Provided is an information processing device which extracts a means which reduces a manufacturing time pertaining to a machining pre-step, a machining step, and a machining post-step in manufacturing artifacts by means of a control device, the information processing device comprising: a storage unit which stores machining-related information related to the machining of a workpiece and includes a machining program; a manufacturing time estimation unit which estimates at least one work step from the machining-related information stored in the storage unit and calculates the manufacturing time necessary for the machining of a workpiece by calculating the time taken for the estimated work step; and a reducing means determination unit which selects, from among a plurality of means pre-stored in the storage unit, a means for reducing the time for the at least one work step estimated by the manufacturing time estimation unit.

A machining condition search device according to the disclosure includes a machining condition generation unit, a practical machining command unit, a machining evaluation unit, a prediction unit, an optimum machining condition calculation unit. The machining condition generation unit generates a machining condition defined by one or more control parameters settable on a machining apparatus. The practical machining command unit causes the machining apparatus to perform machining based on a generated machining condition. The machining evaluation unit generates an evaluation value of performed machining, on the basis of information indicating a machining result of the performed machining. The prediction unit predicts an evaluation value corresponding to a machining condition under which machining is not performed, on the basis of the evaluation value and the machining condition corresponding to the evaluation value. The optimum machining condition calculation unit obtains an optimum machining condition on the basis of a prediction value predicted by the prediction unit and an evaluation value generated by the machining evaluation unit. The optimum machining condition is a machining condition under which an evaluation value is equal to or greater than a threshold and a tolerance is maximum.

A machining program editing device calculates an editable time for a block of a machining program, makes editable only those of a plurality of blocks in the machining program which have the editable time left remaining, and locally writes only edited portions into a machining program used for control.

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.