# US 10,442,023 B2 · Simulation apparatus of wire electric discharge machine having function of determining welding positions of core using machine learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,442,023 B2 - Document ID: US10442023B2 - Status: Granted - Kind: B2 - Application number: 15/361,493 - Filed: 2016-11-28 - Published: 2017-06-01 - Granted: 2019-10-15 - Earliest claimed: 2015-11-30 - Assignees: Fanuc Corporation - Applicants: FANUC Corporation - Inventors: Kenichiro Shirai - Web page: https://patentgrep.com/patent/US10442023B2 ## CPC - B23H 5/00: Combined machining - B23H 9/00: Machining specially adapted for treating particular metal objects or for obtaining special effects or results on metal objects (heat treatment by cathodic discharge C21D1/38) - G05B 13/0265: {the criterion being a learning criterion} - G05B 19/25: for continuous-path control - G05B 19/402: characterised by control arrangements for positioning, e.g. centring a tool relative to a hole in the workpiece, additional detection means to correct position (G05B19/19 takes precedence) - G06N 20/00: Machine learning - B23H 7/02: Wire-cutting - B23H 7/20: for program control, e.g. adaptive - G05B 2219/45221: Edm, electrical discharge machining, electroerosion, ecm, chemical - B23H: WORKING OF METAL BY THE ACTION OF A HIGH CONCENTRATION OF ELECTRIC CURRENT ON A WORKPIECE USING AN ELECTRODE WHICH TAKES THE PLACE OF A TOOL; SUCH WORKING COMBINED WITH OTHER FORMS OF WORKING OF METAL (processes for the electrolytic or electrophoretic production of coatings, electroforming, or apparatus therefor C25D; processes for the electrolytic removal of material from objects C25F; manufacturing printed circuits using precipitation techniques to apply the conductive material to form the desired conductive pattern H05K3/18) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A simulation apparatus of a wire electric discharge machine that performs machining to cut out a core from a workpiece calculates a position and a length of a welding part used to weld the core to the workpiece, calculates an evaluation value of the position and the length, and performs machine learning of the adjustment of the position and the length. In the machine learning, the position and the length of the welding part and the evaluation value are acquired as state data, a reward is calculated based on the state data and calculated reward conditions, and the machine learning of the adjustment of the position and the length of the welding part is performed using the calculated reward. ## Claims ### Claim 1 A simulation apparatus of a wire electric discharge machine that performs machining to cut out a core from a workpiece based on machining preconditions including a program, the simulation apparatus comprising: a processor configured to: calculate and output, when the core is cut out from the workpiece, a position and a length of a welding part on a machining path for the machining to weld the core to the workpiece, calculate an evaluation value to evaluate the position and the length of the welding part, acquire the position and the length of the welding part and the evaluation value as state data on the welding part, set reward conditions, calculate a reward based on the state data and the reward conditions, perform machine learning of an adjustment of the position and the length of the welding part, determine and output (i) an adjustment target including at least one of the position and the length of the welding part and (ii) adjustment amounts of the adjustment target as an adjustment action, based on the state data and a result of the machine learning, recalculate and output the position and the length of the welding part based on the adjustment action, perform the machine learning of the adjustment of the position and the length of the welding part based on (a) the adjustment action, (b) the state data acquired based on the recalculated position and the recalculated length of the welding part, and (c) the reward calculated based on the state data acquired based on the recalculated position and the recalculated length of the welding part, complete the machine learning in response to the calculated evaluation value being converged, and upon completion of the machine learning, output an optimum position of the welding part, wherein the reward conditions are set such that the processor is configured to calculate a positive reward (a) when a number of the welding parts is small, or (b) when a position for supporting the core is well balanced, and such that the processor is configured to calculate a negative reward (c) when the number of the welding parts is large, or (d) when the length of the welding part is shorter than a previously-set welding-parts minimum distance, or (e) when a magnitude of a force by which the core is supported is smaller than a previously-set prescribed threshold, or (f) when a magnitude of a force for dropping the core is large, or (g) when the position for supporting the core is poorly balanced. ### Claim 2 (depends on 1) The simulation apparatus according to claim 1, wherein the evaluation value includes at least any of the force by which the core is supported calculated from the position and the length of the welding part, the force for dropping the core calculated from the position and the length of the welding part, and balance of positions for supporting the core calculated from the position and the length of the welding part. ### Claim 3 (depends on 1) The simulation apparatus according to claim 1, further comprising: a memory configured to store the result of the machine learning, and the stored result of the machine learning to the processor when the processor uses the result of the machine learning again. ### Claim 4 (depends on 1) The simulation apparatus according to claim 1, wherein the simulation apparatus is connected to at least one further simulation apparatus and the processor is further configured to mutually exchange or share the result of the machine learning with the at least one further simulation apparatus.