Semiconductor & Manufacturing

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    • Advanced Machine Tools Research Center

      Advanced Machine Tools Research Center

      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.
    • Darsen Lu《New Era for AI Chips》

      Darsen Lu《New Era for AI Chips》

      With the rapid advancement of artificial intelligence and IoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming. At the moment, GPU’s are considered the baby of the AI world, Google has also developed a TPU to expedite AI procedures. Both GPU and TPU make the process swift. Prof. Darsen Lu’s Research Team has built an open simulation platform called (simNemo) to enable design exploration by academic and industry users in Taiwan on both the AI platform and AI application fronts.
    • Zero Contact Cloud Smart Machine Maintenance Expert System

      Zero Contact Cloud Smart Machine Maintenance Expert System

      Our system provides system identification, servo tuning, and feed axis diagnosis for machine tools. Also, executing remote function verification, and data collection are included. All these functions were actually tested verified by the co-operation manufacturer. It help operators or engineers to maintain and adjust the machine so that the feed drive system match. During this critical time of the COVID-19 epidemic, the proposed system provides a zero-touch remote diagnostic function and method for engineers to maintain the machines in the processing plant can effectively reduce costs and time.
    • Chung Yuan Christian University, R&D Center of Smart Manufacturing

      Chung Yuan Christian University, R&D Center of Smart Manufacturing

      Web interface of CPS platform: Although many factories have introduced MES and scheduling systems, they have not been integrated with the CPS architecture. The transmission of information is not transparent and automated, and it is difficult to ensure the relevance and continuity of the information, which affects the control of the entire production process and the on-site real-time information. Our integrated CPS structure enables the project to obtain the best dispatch of the machine and the shortest delivery time. We also use DDS to receives real-time on-site information and presents it as a platform, which enables control the real-time on-site situation. We constructed an advanced plastic mold & molding service platform, combining CPS and cloud architecture, based on the production life cycle, integrating heterogeneous equipment and systems, and it achieves the goals of real-time monitoring of the manufacturing process, rapid iteration and dynamic optimization.
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