Abstract: A machining condition setting apparatus for electrical discharge machining that sets the machining conditions, including the machining pulse energy, the feed of the electrode, etc. based on set data regarding specifications including the material of the workpiece and the desired surface roughness of the product. Plural sets of basic data showing the relationship between the machining conditions and the specifications resulting from the machining conditions are selected. The relationship between the specifications and machining conditions are stored in an inference unit which includes a neural network type computation unit, by using selected basic data. The inference unit determines the most appropriate machining conditions based on the set data and the specifications. Thus, the storage unit only need store a very small amount of basic data.

Abstract: A method of determining electric discharge machining conditions comprises the steps of establishing multiple sets of predefined machining parameters, establishing for each set of predefined machining parameters a set of basic machining data indicating relationships present during electric discharge machining among electric discharge machining current, machining depth and minimum electrode side surface undersize, selecting at least two sets of basic machining data from among the multiple sets thereof in accordance with a given set of predefined machining parameters, and inferring and thereupon generating machining condition data representing a series of pairs of values comprising machining depth values and corresponding electric discharge machining current values for machining the workpiece to a prescribed configuration and prescribed dimensions, the electric discharge machining current values in the series progressively changes with increasing machining depth value from a value larger than the electric discha

Abstract: A machine tool having a tool member which is moveable along at least one axis relative to a workpiece to be machined, comprising thermosensors for sensing temperatures of a plurality of components of the machine tool. Responsive to the sensed temperatures, neural networks infer thermal deformations of the machine components. Based on the inferred values of thermal deformations, a correction signal is obtained which is combined with a drive signal for moving the tool member and/or workpiece to correct for any positioning error in the direction of the axis. Thermal deformations of machine components should be suitably inferred under the circumstances prevailing where the machine tool is installed. A corresponding method of correcting thermal deformations in a machine tool is also described.