Research Partners
Published:
🏢 Rolls-Royce @ NTU Corporate Lab, Singapore
📌 Project Title: In-Process Sensing for Indirect Surface Quality Measurement
🗓 Period: August 2014 – September 2018
🎯 Description:
Pursued a PhD at Nanyang Technological University (NTU), Singapore, funded by Rolls-Royce @ NTU Corporate Lab. The research focused on in-situ monitoring and modeling of robotic abrasive belt grinding using machine learning techniques. A complementary-strategy-based sensor integration approach was developed for real-time monitoring, enabling weld seam removal and tool wear detection through machine vision, ML, and deep learning techniques. A mathematical modeling framework based on soft computing and regression methods was proposed to predict surface roughness outcomes.
The research contributed to aerospace manufacturing automation goals and involved active student mentoring, lab demonstrations, and multiple high-impact journal publications.
👨🏫 Focus Areas:
• Sensor integration for robotic belt grinding
• ML and DL for weld seam/tool wear prediction
• Regression modeling for surface roughness
• Tribological signal analysis
• Research dissemination and academic mentoring
📌 Project Title: In-Process Sensing for Indirect Surface Quality Measurement
🗓 Period: August 2014 – September 2018
🎯 Description:
Pursued a PhD at Nanyang Technological University (NTU), Singapore, funded by Rolls-Royce @ NTU Corporate Lab. The research focused on in-situ monitoring and modeling of robotic abrasive belt grinding using machine learning techniques. A complementary-strategy-based sensor integration approach was developed for real-time monitoring, enabling weld seam removal and tool wear detection through machine vision, ML, and deep learning techniques. A mathematical modeling framework based on soft computing and regression methods was proposed to predict surface roughness outcomes.
The research contributed to aerospace manufacturing automation goals and involved active student mentoring, lab demonstrations, and multiple high-impact journal publications.
👨🏫 Focus Areas:
• Sensor integration for robotic belt grinding
• ML and DL for weld seam/tool wear prediction
• Regression modeling for surface roughness
• Tribological signal analysis
• Research dissemination and academic mentoring
🏢 A*STAR ARTC – Singapore
📌 Project Title: IIoT Analytics and Predictive Maintenance for Advanced Manufacturing
🗓 Period: 2018 – 2019
🎯 Description:
Executed IIoT and machine learning-based projects for aerospace and FMCG manufacturing. Developed end-to-end streaming analytics and non-intrusive sensor data collection systems for SAESL. Implemented deep learning image pipelines to assess moisture levels and foreign substance detection in Nestlé powdered products. Proposed ML-based predictive maintenance systems for Rolls-Royce and developed cobot vision systems at ARTC’s Model Factory.
👨🏫 Focus Areas:
• IIoT data acquisition and streaming dashboards
• Hyperspectral imaging in FMCG quality control
• ML models for predictive robot maintenance
• Deep learning for cobot-based vision systems
• Collaboration with Nestlé, SAESL, Rolls-Royce, Arcstone, SKF, SIMTech
📌 Project Title: IIoT Analytics and Predictive Maintenance for Advanced Manufacturing
🗓 Period: 2018 – 2019
🎯 Description:
Executed IIoT and machine learning-based projects for aerospace and FMCG manufacturing. Developed end-to-end streaming analytics and non-intrusive sensor data collection systems for SAESL. Implemented deep learning image pipelines to assess moisture levels and foreign substance detection in Nestlé powdered products. Proposed ML-based predictive maintenance systems for Rolls-Royce and developed cobot vision systems at ARTC’s Model Factory.
👨🏫 Focus Areas:
• IIoT data acquisition and streaming dashboards
• Hyperspectral imaging in FMCG quality control
• ML models for predictive robot maintenance
• Deep learning for cobot-based vision systems
• Collaboration with Nestlé, SAESL, Rolls-Royce, Arcstone, SKF, SIMTech
🏢 Empa – Swiss Federal Laboratories for Materials Science and Technology (Thun, Switzerland)
📌 Project Title: AI-Driven Monitoring and Process Control in Additive Manufacturing
🗓 Period: 2019 – 2023
🎯 Description:
Worked on real-time monitoring and control in metal AM using contrastive learning, transfer learning, and acoustic/optical signals. Developed explainable AI models, streaming frameworks, and collaborated on in-situ synchrotron imaging with PSI. Led projects under SNSF and SFA-AM, and co-developed a Mini LPBF system with Fraunhofer ILT for real-time sensing and control.
👨🏫 Focus Areas:
• Contrastive learning with sensor data
• Physics-based emissions analysis
• Streaming data pipelines
• Synchrotron-enabled AM phase analysis
• Digital twin development with Mini LPBF
• Collaboration with PSI, EPFL, KU Leuven, ETH Zurich
📌 Project Title: AI-Driven Monitoring and Process Control in Additive Manufacturing
🗓 Period: 2019 – 2023
🎯 Description:
Worked on real-time monitoring and control in metal AM using contrastive learning, transfer learning, and acoustic/optical signals. Developed explainable AI models, streaming frameworks, and collaborated on in-situ synchrotron imaging with PSI. Led projects under SNSF and SFA-AM, and co-developed a Mini LPBF system with Fraunhofer ILT for real-time sensing and control.
👨🏫 Focus Areas:
• Contrastive learning with sensor data
• Physics-based emissions analysis
• Streaming data pipelines
• Synchrotron-enabled AM phase analysis
• Digital twin development with Mini LPBF
• Collaboration with PSI, EPFL, KU Leuven, ETH Zurich
🏢 EPFL – École Polytechnique Fédérale de Lausanne
📌 Project Title: MOCONT & Sinergia: Monitoring and Control in Metal AM
🗓 Period: 2021–2023
🎯 Description:
Collaborated on the SNSF Sinergia project “In situ monitoring in additive manufacturing of metals and alloys based on artificial intelligence” (CRSII5_193799), alongside EPFL, PSI, and Empa. Contributed to high-level research in AM monitoring using AI. Also involved in teaching and curriculum design for EPFL’s Master's course “Materials Processing with Intelligent Systems” and mentoring graduate students.
👨🏫 Focus Areas:
• AI-based control in metal AM
• Sinergia CRSII5_193799 consortium research
• Graduate mentorship and teaching
• SFA-AM MOCONT initiative
📌 Project Title: MOCONT & Sinergia: Monitoring and Control in Metal AM
🗓 Period: 2021–2023
🎯 Description:
Collaborated on the SNSF Sinergia project “In situ monitoring in additive manufacturing of metals and alloys based on artificial intelligence” (CRSII5_193799), alongside EPFL, PSI, and Empa. Contributed to high-level research in AM monitoring using AI. Also involved in teaching and curriculum design for EPFL’s Master's course “Materials Processing with Intelligent Systems” and mentoring graduate students.
👨🏫 Focus Areas:
• AI-based control in metal AM
• Sinergia CRSII5_193799 consortium research
• Graduate mentorship and teaching
• SFA-AM MOCONT initiative
🏢 Paul Scherrer Institute (PSI), Switzerland
📌 Project Title: Sinergia: Real-Time Diagnostics for Additive Manufacturing
🗓 Period: 2022–
🎯 Description:
Participated in collaborative research under the SNSF Sinergia project (CRSII5_193799), focusing on real-time monitoring using synchrotron-based diagnostics in LPBF. Contributed to hybrid sensing models and supported experimental campaigns for phase monitoring and emission signal interpretation.
👨🏫 Focus Areas:
• Synchrotron data and beamline diagnostics
• Hybrid sensing models in LPBF
• Cross-institution collaboration (EPFL, Empa)
📌 Project Title: Sinergia: Real-Time Diagnostics for Additive Manufacturing
🗓 Period: 2022–
🎯 Description:
Participated in collaborative research under the SNSF Sinergia project (CRSII5_193799), focusing on real-time monitoring using synchrotron-based diagnostics in LPBF. Contributed to hybrid sensing models and supported experimental campaigns for phase monitoring and emission signal interpretation.
👨🏫 Focus Areas:
• Synchrotron data and beamline diagnostics
• Hybrid sensing models in LPBF
• Cross-institution collaboration (EPFL, Empa)
🏢 ETH Zurich – Department of Materials, Laboratory for Nanometallurgy
📌 Project Title: Collaborative Research on Microstructure and Process Monitoring
🗓 Period: 2023–
🎯 Description:
Maintains an ongoing collaborative partnership with ETH Zurich focused on data sharing, joint publications, and process diagnostics. Strategic academic collaboration facilitated through inspire AG, linking industrial needs with academic innovation in laser-material interaction and additive manufacturing.
👨🏫 Focus Areas:
• Data sharing and joint manuscripts
• Research in nanometallurgy and laser AM
• Partnership via inspire AG
📌 Project Title: Collaborative Research on Microstructure and Process Monitoring
🗓 Period: 2023–
🎯 Description:
Maintains an ongoing collaborative partnership with ETH Zurich focused on data sharing, joint publications, and process diagnostics. Strategic academic collaboration facilitated through inspire AG, linking industrial needs with academic innovation in laser-material interaction and additive manufacturing.
👨🏫 Focus Areas:
• Data sharing and joint manuscripts
• Research in nanometallurgy and laser AM
• Partnership via inspire AG
🏢 Katholieke Universiteit Leuven (KU Leuven)
📌 Project Title: Joint Research Development with MaPS Group
🗓 Period: 2022–
🎯 Description:
Collaborates with the Manufacturing Processes and Systems (MaPS) group at KU Leuven’s De Nayer campus on research proposal development and smart manufacturing initiatives. Focused on collaborative efforts in additive manufacturing with emphasis on scientific exchange and project-based innovation.
👨🏫 Focus Areas:
• Proposal writing and mutual exchange
• Additive manufacturing systems research
📌 Project Title: Joint Research Development with MaPS Group
🗓 Period: 2022–
🎯 Description:
Collaborates with the Manufacturing Processes and Systems (MaPS) group at KU Leuven’s De Nayer campus on research proposal development and smart manufacturing initiatives. Focused on collaborative efforts in additive manufacturing with emphasis on scientific exchange and project-based innovation.
👨🏫 Focus Areas:
• Proposal writing and mutual exchange
• Additive manufacturing systems research