Abstract: A lubricating oil volume adjustment system and a lubricating oil volume adjustment method are provided. The system includes a storage device and a processor and is connected to a machine including a motor through a data acquisition device acquiring current information of the motor. The storage device stores a machine learning model trained by a training data set including a plurality of pieces of the current information of the motor during operation and a plurality of temperature values measured during operation of the machine. The processor is configured to acquire the current information of present operation of the motor by using the data acquisition device, predict a temperature value of the machine when the motor operates under the current information by using the machine learning model, and calculate and adjust a lubricating oil volume suitable to be used by the machine during operation according to the predicted temperature value.

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.

Abstract: A lubricating oil volume adjustment system and a lubricating oil volume adjustment method are provided. The system includes a storage device and a processor and is connected to a machine including a motor through a data acquisition device acquiring current information of the motor. The storage device stores a machine learning model trained by a training data set including a plurality of pieces of the current information of the motor during operation and a plurality of temperature values measured during operation of the machine. The processor is configured to acquire the current information of present operation of the motor by using the data acquisition device, predict a temperature value of the machine when the motor operates under the current information by using the machine learning model, and calculate and adjust a lubricating oil volume suitable to be used by the machine during operation according to the predicted temperature value.

Abstract: A tool-life prediction method, applicable to a machine tool having a machining end, includes steps of capturing a plurality of measurement data from a tool of the machining end, transforming each of the plurality of measurement data into a corresponding complex process capability index (Cpk), utilizing an artificial neural network being trained to generate a tool-life prediction scale with respect to the Cpk, and then based on the tool-life prediction scale to determine a remaining tool life of the tool. In addition, a tool life prediction system is also provided.

Abstract: A tool-life prediction method, applicable to a machine tool having a machining end, includes steps of capturing a plurality of measurement data from a tool of the machining end, transforming each of the plurality of measurement data into a corresponding complex process capability index (Cpk), utilizing an artificial neural network being trained to generate a tool-life prediction scale with respect to the Cpk, and then based on the tool-life prediction scale to determine a remaining tool life of the tool. In addition, a tool life prediction system is also provided.

Abstract: A remote machining optimization system for a machine tool is provided, which includes an input unit configured to input a machining parameter including a spindle speed and a cut depth; a receiving unit configured to receive sound signals and vibration signals from the machine tool; a processing unit configured to generate a machining program with a program generating module, to modify the spindle speed and the cut depth according to the sound signals with a speed optimization module and a depth optimization module, respectively; a communication unit configured to send the machining program to the machine tool; and a storage unit configured to store the modified spindle speed and the cut depth.

Abstract: A remote machining optimization system for a machine tool is provided, which includes an input unit configured to input a machining parameter including a spindle speed and a cut depth; a receiving unit configured to receive sound signals and vibration signals from the machine tool; a processing unit configured to generate a machining program with a program generating module, to modify the spindle speed and the cut depth according to the sound signals with a speed optimization module and a depth optimization module, respectively; a communication unit configured to send the machining program to the machine tool; and a storage unit configured to store the modified spindle speed and the cut depth.

Abstract: A machining path modification method includes the following steps: analyzing the machining path of a machining program to decide whether any point of the machining path is an avoidant point; obtaining an abnormal point of the machining path; and if the abnormal point exists, modifying the machining program to add an abnormality avoidant path on the avoidant point of the machining path.