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 welding part calculation section that calculates and outputs, when the core is cut out from the workpiece, a position and a length of a welding part formed on a machining path for the machining to weld the core to the workpiece; and an operation evaluation section that calculates an evaluation value used to evaluate the position and the length of the welding part output from the welding part calculation section; characterised by a machine learning device that performs machine learning of an adjustment of the position and the length of the welding part, wherein the machine learning device has a state observation section that acquires the position and the length of the welding part and the evaluation value as state data on the welding part, a reward conditions setting section that sets reward conditions, the reward conditions being set such that a positive reward is given when the number of the welding parts is small or when a position for supporting the core is well balanced and such that a negative reward is given when the number of the welding parts is large, when a length of the welding part is shorter than a previously-set welding-parts minimum distance, when a magnitude of a force by which the core is supported is smaller than a previously-set prescribed threshold, when a magnitude of a force for dropping the core is large, or when the position for supporting the core is poorly balanced, a reward calculation section that calculates a reward based on the state data and the reward conditions, a welding-part adjustment learning section that performs the machine learning of the adjustment of the position and the length of the welding part, and a welding-part adjustment output section that determines and outputs an adjustment target and adjustment amounts of the position and the length of the welding part as an adjustment action based on the state data and a machine learning result of the adjustment of the position and the length of the welding part by the welding-part adjustment learning section, wherein the welding part calculation section is configured to recalculate and output the position and the length of the welding part based on the adjustment action output from the welding-part adjustment output section, and the welding-part adjustment learning section is configured to perform the machine learning of the adjustment of the position and the length of the welding part based on the adjustment action, the state data acquired by the state observation section based on the position and the length of the welding part recalculated by the welding part calculation section, and the reward calculated by the reward calculation section based on the state data. a welding part calculation section that calculates and outputs, when the core is cut out from the workpiece, a position and a length of a welding part formed on a machining path for the machining to weld the core to the workpiece; and an operation evaluation section that calculates an evaluation value used to evaluate the position and the length of the welding part output from the welding part calculation section; characterised by a machine learning device that performs machine learning of an adjustment of the position and the length of the welding part, wherein the machine learning device has a state observation section that acquires the position and the length of the welding part and the evaluation value as state data on the welding part, a reward conditions setting section that sets reward conditions, the reward conditions being set such that a positive reward is given when the number of the welding parts is small or when a position for supporting the core is well balanced and such that a negative reward is given when the number of the welding parts is large, when a length of the welding part is shorter than a previously-set welding-parts minimum distance, when a magnitude of a force by which the core is supported is smaller than a previously-set prescribed threshold, when a magnitude of a force for dropping the core is large, or when the position for supporting the core is poorly balanced, a reward calculation section that calculates a reward based on the state data and the reward conditions, a welding-part adjustment learning section that performs the machine learning of the adjustment of the position and the length of the welding part, and a welding-part adjustment output section that determines and outputs an adjustment target and adjustment amounts of the position and the length of the welding part as an adjustment action based on the state data and a machine learning result of the adjustment of the position and the length of the welding part by the welding-part adjustment learning section, wherein the welding part calculation section is configured to recalculate and output the position and the length of the welding part based on the adjustment action output from the welding-part adjustment output section, and the welding-part adjustment learning section is configured to perform the machine learning of the adjustment of the position and the length of the welding part based on the adjustment action, the state data acquired by the state observation section based on the position and the length of the welding part recalculated by the welding part calculation section, and the reward calculated by the reward calculation section based on the state data.