公开(公告)号：US20140112603A1

公开(公告)日：2014-04-24

申请号：US13742017

申请日：2013-01-15

Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE

Inventor： Yi-Ming CHEN ,  Tzuo-Liang LUO ,  Shao-Ying HUNG ,  Chien-Chih LIAO ,  Wei-Juh LIN ,  Shao-Ku HUANG

IPC: F16C29/00

CPC classification number: F16C29/005 ,  F16C29/025

Abstract: A hydrostatic rail guide includes a guide rail, a guide carriage, a hydrostatic supply device, a measurement device and a controller. The guide carriage is hydrostatically mounted on the guide rail and has a hydrostatic channel filled with oil. The oil keeps the guide carriage and the guide rail at a gap. The hydrostatic supply device is adapted for supplying the oil to the hydrostatic channel and adjusting the pressure of the oil. The measurement device is installed on the guide carriage and adapted for detecting the gap between the guide carriage and the guide rail and for producing a distance signal of the measured gap. The controller is connected to the hydrostatic supply device and the measurement device. The controller is adapted for controlling the hydrostatic supply device, based on the distance signal, in order to adjust the hydrostatic pressure of the oil after being pressurized.

Abstract translation: 静液压导轨包括导轨，导向架，静液压供给装置，测量装置和控制器。 导向架被静液体地安装在导轨上，并且具有充满油的流体静力通道。 油保持导向架和导轨间隙。 静液压供应装置适于将油供应到静水通道并调节油的压力。 测量装置安装在导向架上，用于检测导向架和导轨之间的间隙，并产生测量间隙的距离信号。 控制器连接到静液压供应装置和测量装置。 控制器适于基于距离信号控制静液压供应装置，以便在加压之后调节油的静水压力。

公开(公告)号：US20240019838A1

公开(公告)日：2024-01-18

申请号：US17975418

申请日：2022-10-27

Applicant: INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE

Inventor： Yi-Ming CHEN ,  Shu-Chung LIAO

IPC: G05B19/4065

CPC classification number: G05B19/4065 ,  G05B2219/37336 ,  G05B2219/50185

Abstract: A composite tool cutting monitoring system used for a machine is provided. The cutting monitoring system includes a data capturing module, a database and a cutting control module. The data capturing module is configured to capture a motor current data of the machine and a tool wear data. The motor current data is used as a training data for a tool wear state prediction model to perform deep learning and forecasting. The database is configured to establish a tool wear database for the comparison of a tool wear state. The tool wear state prediction model outputs a tool wear state prediction data to the cutting control module. The cutting control module judges whether the tool wear state is normal according to the tool wear state prediction data.