A5050301130- Digital Transformation in Industry
- Advanced Machining and Optimization Techniques
- Industrial Vision Systems and Defect Detection
- Advanced machining processes and optimization
- Edcuational Technology Systems
- Multimedia Learning Systems
- Manufacturing Process and Optimization
- Advanced Surface Polishing Techniques
- Decision Support System Applications
National Cheng Kung University
2017-2025
Universitas 45 Surabaya
2015
In the era of digital transformation, containerized systems and twins are vital technologies in intelligent manufacturing. Our previous research, Implementation Framework Digital Twins for Intelligent Manufacturing (IF-DTiM), lacks comprehensive security features[1]. To address this critical gap, we propose Advanced Security Approach Building Secure Containerized (ASABS-CMDT), integrating automated tools detecting vulnerabilities, container image creation, secure signing, all within...
Electrical discharge machining (EDM) can machine hard conductive workpieces that are difficult to using traditional techniques. For monitoring the EDM process virtual metrology (VM), probes with a very high sampling rate needed acquire voltage and current signals of electrodes, thereby generating huge amount sensor data raising big processing issue in extracting features from raw data. This paper proposes novel efficient scheme for feature extraction EDM, called BEDPS, based on Spark HDFS. A...
In this paper, we propose a novel data transformation scheme over big platform, aiming at injecting production from the local database in factory side and transforming them into workpiece-centric form that many manufacturing analytics systems need. The key idea is to blend processing techniques, including table composition with external distributed files, columnar storage, partition, massively parallel for minimizing time. Our proposed brings two main impacts smart manufacturing. First, our...
EDM (Electrical Discharge Machining) is a process to remove metal from conductive materials using electrical sparks. To monitor the virtual metrology (VM), we need obtain electrode's voltage and current signals of machine tool. Due nature EDM, sensors installed on tool acquire at high sampling rate generate vast amount data in short time, thereby raising big-data processing issue. Our previous work proposed an efficient approach called BEDPS big Hadoop distributed cluster. This paper...