Advanced Machine Tools Research Center
| Summary | Tool wear and health condition monitoring during the processing: The tool wear monitoring technology developed by our researching team is specifically designed to analyze whether the tool is broken, collapsed, etc., and to estimate the remaining useful life(RUL) of the tool according to the working conditions of mass processing. By acquiring the vibration signal data with three-axis accelerometers installed on the machine tool, this technology could determine whether the current tool cutting vibration has exceeded the safety range by plotting a control chart. Once it exceeds the safe range, the current tool processing state will be assumed as abnormal. It gives users a reference to replace the broken tools immediately to prevent continuing processing, which causes vast loss such as poor quality of workpieces. In addition, this technology allows users to build models for distinct working conditions to predict the RUL of tools. It could allow users to evaluate the current health condition of tools and schedule the time to change the tool. Thermal compensation technique for machine tool spindle: This technique established a thermal compensation mathematical model for temperature field distribution and tool center point (TCP) displacement using a machine tool spindle thermal compensation algorithm. Temperature rises in different machine components have different effects on TCP displacement as well as temperature sensor's positions and quantities have sizeable effects on the accuracy of the spindle’s thermal deformation displacement error compensation. This technique proposed a temperature measurement point sensitivity analysis and ranking technique to quantify the contribution of information from each temperature sensor to thermal deformation. Furthermore, a method for determining the optimal number of temperature measurement points and their positions was established. This method can achieve an excellent compensation effect while reducing the number of temperature sensors, which could reduce the sensor assembly and maintenance costs also enhance the accuracy of the model. Bearing Condition Evaluation: Bearings are important components in machine tools. When they are damaged, it will cause poor product quality, decreased production efficiency, and even the machines’ malfunction. In recent years, with the development and progress of artificial intelligence, an increasing number of machine learning algorithms have been applied to machine tools. The Bearing Condition Evaluation technology developed by our researching team combines self-made sensors and unsupervised artificial intelligence algorithms in order to diagnose the health condition of the bearings and estimate their remaining useful life (RUL). Through the collection of vibration signal data of the bearings and conducting the specific working condition of health evaluation during the warming of machine tools, the diagnosis model built by the unsupervised artificial intelligence algorithm is capable of achieving the evaluation of bearings’ health conditions and RUL simultaneously and more accurately. Company Description: The researching team is led by Professor Chih Chun Cheng, a professor from National Chung Cheng University. The team has developed several technologies including the Bearing Health Condition Evaluation, the Tool Wear's Monitoring, and the Thermal Compensation system as well as collaborated with many domestic manufacturers all have project collaborations and technology transfers. In addition, many technology modules have been adopted by companies. Not only the abundant research results are applied to machine tool-related industries, but they are also given recognition from others who come from different domains, such as AIDC and GlobalWafers Co., Ltd. are in project collaboration presently. |
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| Keyword | Tool wear and health condition monitoring during the processing Thermal compensation technique for machine tool spindle Bearing Condition Evaluation | ||
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