Abstract: A lead-free steel for machine structural use with excellent machinability and low strength an isotropy, which does not contain Pb and is equal to or higher than a conventional D Pb-containing free cutting steel in properties, is provided. This steel includes, on the weight basis, C: 0.10 to 0.65%; Si: 0.03 to 1.00%; Mn: 0.30 to 2.50%; S: 0.03 to 0.35%; Cr: 0.1 to 2.0%; Al: less than 0.010%; Ca: 0.0005 to 0.020%; Mg: 0.0003 to 0.020%; 0: less than 20 ppm; and the balance being Fe and inevitable impurities.

Abstract: A lead-free steel for machine structural use with excellent machinability and low strength anisotropy, which does not contain Pb and is equal to or higher than a conventional Pb-containing free cutting steel in properties, is provided. This steel includes, on the weight basis, C: 0.10 to 0.65%; Si: 0.03 to 1.00%; Mn: 0.30 to 2.50%; S: 0.03 to 0.35%; Cr: 0.1 to 2.0%; Al: less than 0.010%; Ca: 0.0005 to 0.020%; Mg: 0.0003 to 0.020%; O: less than 20 ppm; and the balance being Fe and inevitable impurities.

Abstract: A lead-free steel for machine structural use with excellent machinability and low strength an isotropy, which does not contain Pb and is equal to or higher than a conventional D Pb-containing free cutting steel in properties, is provided. This steel includes, on the weight basis, C: 0.10 to 0.65%; Si: 0.03 to 1.00%; Mn: 0.30 to 2.50%; S: 0.03 to 0.35%; Cr: 0.1 to 2.0%; Al: less than 0.010%; Ca: 0.0005 to 0.020%; Mg: 0.0003 to 0.020%; 0: less than 20 ppm; and the balance being Fe and inevitable impurities.

Abstract: A process time estimating apparatus is disclosed for estimating the process time for manufacturing an object such as a metal die. The apparatus includes a process time estimating section, a process occupancy time measurement section and a process program scheduling section. The process time estimating section includes a neural network device as an estimating device. An estimation input factor extracting section extracts input factors such as drawing information for an object to be manufactured. A storing section stores input factors for later neural network learning to improve the estimation capability of the system. The process occupancy time measurement section reads the process code and automatically measures the actual time involved in performing the process for a particular object being manufactured. A selecting section selects a measured process time for neural network learning.

Abstract: A data processing device for machining a three dimensional surface includes a tool locus data generator providing tool shape and dimension indicative information of a machining tool and machining route information, calculating information selected from a curvature, a normal line and a tangential line at a machining point of the three dimensional surface, and calculating the optimum shape of a machining tool and/or controlling condition of the tool based on the calculated information; a device for checking the tool locus data of a machining tool based on a predetermined pattern; and a processor for converting the checked tool locus data into data which is to be inputted into a machine which conducts three dimensional surface machining.

Abstract: A high speed machining controller comprises an acoustic emission sensor which is installed in or near a machine for machining a work piece in a high speed and which detects acoustic emission generated during machining a work piece; a signal processing means which is composed of a band-pass filter for passing an element of a frequency band corresponding to the acoustic emission by inputting a signal outputted from the acoustic emission sensor, a rectification circuit for rectifying a signal outputted from said band-pass filter, a peak detection circuit for detecting peak values of the signal outputted from the rectification circuit, and a smoothing circuit for smoothing output signals of said peak detection circuit and for outputting a signal corresponding to the amplitude of the acoustic emission for the purpose of processing the output signal of the acoustic emission sensor to detect its amplitude; a cutting condition discrimination means for judging the cutting condition of a work piece by comparing a detec

Abstract: An apparatus for discriminating a machining state from a non-machining state in a machine tool wherein a workpiece is machined with a cutting tool or grinder, comprising plural vibration sensors including at least one acoustic-emission sensor, which detect vibrations of different frequency bands, respectively, which are produced during machining of the workpiece by the tool. A discriminating device judges that the workpiece is being machined by the tool when levels of output signals generated from at least two of the vibration sensors are higher than respective predetermined threshold levels, and judges that the workpiece is not being machined by the tool when the level of an output signal generated from less than two of the vibration sensors is higher than a predetermined threshold level. Also disclosed is a method for discrimination between the machining and non-machining states.