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:
Doctoral research conducted at Nanyang Technological University (NTU), Singapore, funded by the Rolls-Royce @ NTU Corporate Lab. The study focused on in-situ monitoring and modeling of robotic abrasive belt grinding processes using machine learning methodologies. A sensor integration strategy was developed to enable real-time monitoring, facilitating weld seam removal and tool wear detection through machine vision, ML, and deep learning techniques. A predictive framework using soft computing and regression models was proposed to estimate surface roughness.
The research advanced automation in aerospace manufacturing and included responsibilities in student mentoring, laboratory demonstrations, and contributions to peer-reviewed journal publications.
👨🏫 Focus Areas:
• Sensor integration in robotic grinding
• Machine learning for weld seam/tool wear detection
• Soft computing-based surface roughness prediction
• Tribological signal interpretation
• Academic mentoring and dissemination
📌 Project Title: In-Process Sensing for Indirect Surface Quality Measurement
🗓 Period: August 2014 – September 2018
🎯 Description:
Doctoral research conducted at Nanyang Technological University (NTU), Singapore, funded by the Rolls-Royce @ NTU Corporate Lab. The study focused on in-situ monitoring and modeling of robotic abrasive belt grinding processes using machine learning methodologies. A sensor integration strategy was developed to enable real-time monitoring, facilitating weld seam removal and tool wear detection through machine vision, ML, and deep learning techniques. A predictive framework using soft computing and regression models was proposed to estimate surface roughness.
The research advanced automation in aerospace manufacturing and included responsibilities in student mentoring, laboratory demonstrations, and contributions to peer-reviewed journal publications.
👨🏫 Focus Areas:
• Sensor integration in robotic grinding
• Machine learning for weld seam/tool wear detection
• Soft computing-based surface roughness prediction
• Tribological signal interpretation
• Academic mentoring and dissemination
🏢 A*STAR ARTC – Singapore
📌 Project Title: IIoT Analytics and Predictive Maintenance for Advanced Manufacturing
🗓 Period: 2018 – 2019
🎯 Description:
Led IIoT and ML-based initiatives for the aerospace and FMCG manufacturing sectors. Developed real-time streaming analytics systems and non-intrusive sensor platforms for SAESL. Implemented deep learning pipelines for moisture content analysis and foreign particle detection in Nestlé powdered products. Proposed predictive maintenance frameworks using ML for Rolls-Royce, and spearheaded the development of collaborative robot (cobot) vision systems within ARTC’s Model Factory — a state-of-the-art Industry 4.0 testbed for smart manufacturing solutions. The Model Factory enabled rapid prototyping and validation of advanced automation concepts in realistic factory conditions.
👨🏫 Focus Areas:
• IIoT-based sensor networks and dashboards
• Hyperspectral imaging for FMCG inspection
• ML-driven predictive maintenance
• Deep learning-enabled cobot vision
• Industrial collaboration with Nestlé, SAESL, Rolls-Royce, Arcstone, SKF, SIMTech
📌 Project Title: IIoT Analytics and Predictive Maintenance for Advanced Manufacturing
🗓 Period: 2018 – 2019
🎯 Description:
Led IIoT and ML-based initiatives for the aerospace and FMCG manufacturing sectors. Developed real-time streaming analytics systems and non-intrusive sensor platforms for SAESL. Implemented deep learning pipelines for moisture content analysis and foreign particle detection in Nestlé powdered products. Proposed predictive maintenance frameworks using ML for Rolls-Royce, and spearheaded the development of collaborative robot (cobot) vision systems within ARTC’s Model Factory — a state-of-the-art Industry 4.0 testbed for smart manufacturing solutions. The Model Factory enabled rapid prototyping and validation of advanced automation concepts in realistic factory conditions.
👨🏫 Focus Areas:
• IIoT-based sensor networks and dashboards
• Hyperspectral imaging for FMCG inspection
• ML-driven predictive maintenance
• Deep learning-enabled cobot vision
• Industrial 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:
Led the development of real-time monitoring and control systems in metal additive manufacturing using contrastive and transfer learning approaches applied to acoustic and optical emissions. Developed explainable AI frameworks and streaming pipelines, and contributed to synchrotron-based imaging experiments in collaboration with PSI. Co-developed a Mini LPBF platform with Fraunhofer ILT for integrated sensing and control. Work supported by SNSF and SFA-AM programs.
👨🏫 Focus Areas:
• Contrastive learning for sensor fusion
• Emission signal analysis for process feedback
• Real-time data streaming systems
• Synchrotron-enabled phase transformation analysis
• Miniature LPBF system development
• Collaborations with PSI, EPFL, KU Leuven, ETH Zurich
📌 Project Title: AI-Driven Monitoring and Process Control in Additive Manufacturing
🗓 Period: 2019 – 2023
🎯 Description:
Led the development of real-time monitoring and control systems in metal additive manufacturing using contrastive and transfer learning approaches applied to acoustic and optical emissions. Developed explainable AI frameworks and streaming pipelines, and contributed to synchrotron-based imaging experiments in collaboration with PSI. Co-developed a Mini LPBF platform with Fraunhofer ILT for integrated sensing and control. Work supported by SNSF and SFA-AM programs.
👨🏫 Focus Areas:
• Contrastive learning for sensor fusion
• Emission signal analysis for process feedback
• Real-time data streaming systems
• Synchrotron-enabled phase transformation analysis
• Miniature LPBF system development
• Collaborations 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:
Participated in the SNSF Sinergia project “In situ monitoring in additive manufacturing of metals and alloys based on artificial intelligence” (CRSII5_193799), with partners including EPFL, PSI, and Empa. Contributed to AI-based monitoring systems and taught in EPFL’s Master's course “Materials Processing with Intelligent Systems.” Also supervised graduate students and supported curriculum development.
👨🏫 Focus Areas:
• AI-integrated process monitoring and control
• Cross-institutional research under CRSII5_193799
• Graduate-level teaching and supervision
• Involvement in the SFA-AM MOCONT initiative
📌 Project Title: MOCONT & Sinergia: Monitoring and Control in Metal AM
🗓 Period: 2021–2023
🎯 Description:
Participated in the SNSF Sinergia project “In situ monitoring in additive manufacturing of metals and alloys based on artificial intelligence” (CRSII5_193799), with partners including EPFL, PSI, and Empa. Contributed to AI-based monitoring systems and taught in EPFL’s Master's course “Materials Processing with Intelligent Systems.” Also supervised graduate students and supported curriculum development.
👨🏫 Focus Areas:
• AI-integrated process monitoring and control
• Cross-institutional research under CRSII5_193799
• Graduate-level teaching and supervision
• Involvement in the SFA-AM MOCONT initiative
🏢 Paul Scherrer Institute (PSI), Switzerland
📌 Project Title: Sinergia: Real-Time Diagnostics for Additive Manufacturing
🗓 Period: 2022–2025
🎯 Description:
Engaged in real-time diagnostics for LPBF under the SNSF Sinergia project (CRSII5_193799), leveraging synchrotron-based imaging for phase transition tracking. Contributed to the development of hybrid sensing frameworks and facilitated experimental campaigns for in-situ emission interpretation.
👨🏫 Focus Areas:
• Synchrotron diagnostics and beamline experiments
• Hybrid sensor fusion models for LPBF
• Scientific collaboration with EPFL and Empa
📌 Project Title: Sinergia: Real-Time Diagnostics for Additive Manufacturing
🗓 Period: 2022–2025
🎯 Description:
Engaged in real-time diagnostics for LPBF under the SNSF Sinergia project (CRSII5_193799), leveraging synchrotron-based imaging for phase transition tracking. Contributed to the development of hybrid sensing frameworks and facilitated experimental campaigns for in-situ emission interpretation.
👨🏫 Focus Areas:
• Synchrotron diagnostics and beamline experiments
• Hybrid sensor fusion models for LPBF
• Scientific collaboration with EPFL and Empa
🏢 ETH Zurich – Department of Materials, Laboratory for Nanometallurgy
📌 Project Title: Collaborative Research on Microstructure and Process Monitoring
🗓 Period: 2023–
🎯 Description:
Maintaining an active partnership with ETH Zurich to promote joint publications, data exchange, and collaborative studies on laser-material interactions and microstructure diagnostics. This initiative is supported by inspire AG, fostering academia-industry linkages.
👨🏫 Focus Areas:
• Collaborative research in nanometallurgy and AM
• Joint manuscript preparation and data sharing
• Partnership through inspire AG
📌 Project Title: Collaborative Research on Microstructure and Process Monitoring
🗓 Period: 2023–
🎯 Description:
Maintaining an active partnership with ETH Zurich to promote joint publications, data exchange, and collaborative studies on laser-material interactions and microstructure diagnostics. This initiative is supported by inspire AG, fostering academia-industry linkages.
👨🏫 Focus Areas:
• Collaborative research in nanometallurgy and AM
• Joint manuscript preparation and data sharing
• Partnership through inspire AG
🏢 Katholieke Universiteit Leuven (KU Leuven)
📌 Project Title: Joint Research Development with MaPS Group
🗓 Period: 2022–
🎯 Description:
Engaged in research proposal development and collaborative activities with the MaPS group at KU Leuven’s De Nayer campus. Focus areas include additive manufacturing, digital production systems, and academic exchange to promote innovation in smart manufacturing.
👨🏫 Focus Areas:
• Proposal co-development and faculty exchange
• Additive manufacturing research and innovation
📌 Project Title: Joint Research Development with MaPS Group
🗓 Period: 2022–
🎯 Description:
Engaged in research proposal development and collaborative activities with the MaPS group at KU Leuven’s De Nayer campus. Focus areas include additive manufacturing, digital production systems, and academic exchange to promote innovation in smart manufacturing.
👨🏫 Focus Areas:
• Proposal co-development and faculty exchange
• Additive manufacturing research and innovation