Industry Partnerships
Published:
🏢 Rolls-Royce – Seletar Campus, Singapore
📌 Project Title: Experimental Investigation of High-Frequency Vibratory Media Finishing
🗓 Duration: August 2013 – July 2014
🎯 Objective:
Enhance surface finishing efficiency and reduce lead time in mass finishing operations by employing high-frequency vibratory systems to improve material removal and surface smoothness on aerospace-grade aluminum components.
👨🔬 Role: Master's Researcher – Surface Engineering & Experimental Design
🛠 Methods & Technologies:
• Vibratory bowl finishing using aluminum and acrylic chambers
• High-frequency excitation system (TIRA S 55240/LS-340)
• Surface roughness measurement with Taly-scan profilometer
• SEM-based surface morphology analysis
• High-speed video capture of particle motion
• Multi-level DOE with ceramic abrasive media
✅ Outcome:
• Achieved Ra = 0.4 µm with reduced processing time
• Identified optimal frequency–amplitude parameters
• Delivered actionable process recommendations to Rolls-Royce
📌 Project Title: Experimental Investigation of High-Frequency Vibratory Media Finishing
🗓 Duration: August 2013 – July 2014
🎯 Objective:
Enhance surface finishing efficiency and reduce lead time in mass finishing operations by employing high-frequency vibratory systems to improve material removal and surface smoothness on aerospace-grade aluminum components.
👨🔬 Role: Master's Researcher – Surface Engineering & Experimental Design
🛠 Methods & Technologies:
• Vibratory bowl finishing using aluminum and acrylic chambers
• High-frequency excitation system (TIRA S 55240/LS-340)
• Surface roughness measurement with Taly-scan profilometer
• SEM-based surface morphology analysis
• High-speed video capture of particle motion
• Multi-level DOE with ceramic abrasive media
✅ Outcome:
• Achieved Ra = 0.4 µm with reduced processing time
• Identified optimal frequency–amplitude parameters
• Delivered actionable process recommendations to Rolls-Royce
🏢 Singapore Aero Engine Services Pte Ltd (SAESL)
📌 Project Title: Factory Floor Digitization and Asset Monitoring Pilot
🗓 Duration: January 2019 – June 2019
🎯 Objective:
Implement real-time asset monitoring for improved equipment utilization and predictive maintenance using current sensing and customized dashboards.
👨🔬 Role: R&D Scientist – Architecture & Integration
🛠 Methods & Technologies:
• Current sensors for machine activity tracking
• Data acquisition using LabVIEW
• Custom dashboards for real-time utilization insights
• Proof-of-concept deployment for smart factory monitoring
✅ Outcome:
• Developed a modular and scalable monitoring framework
• Contributed to SAESL's digital transformation roadmap
• Enabled real-time visibility into shopfloor operations
📌 Project Title: Factory Floor Digitization and Asset Monitoring Pilot
🗓 Duration: January 2019 – June 2019
🎯 Objective:
Implement real-time asset monitoring for improved equipment utilization and predictive maintenance using current sensing and customized dashboards.
👨🔬 Role: R&D Scientist – Architecture & Integration
🛠 Methods & Technologies:
• Current sensors for machine activity tracking
• Data acquisition using LabVIEW
• Custom dashboards for real-time utilization insights
• Proof-of-concept deployment for smart factory monitoring
✅ Outcome:
• Developed a modular and scalable monitoring framework
• Contributed to SAESL's digital transformation roadmap
• Enabled real-time visibility into shopfloor operations
🏢 Nestlé Singapore Pte Ltd & PT Nestlé Indonesia – Karawang Factory
📍 Locations: Singapore & Karawang, Indonesia
🗓 Duration: 2018 – 2019
📌 Project Title: Moisture Optimization and Production Volume Analysis
🎯 Objective:
Enable moisture detection in powder-based FMCG products through hyperspectral imaging, and analyze SCADA data for production volume optimization.
👨🔬 Role: Consultant – Imaging & Data Pipeline Architect
🛠 Methods & Technologies:
• Hyperspectral imaging calibration and validation
• Ground-truth moisture profiling
• SCADA data root cause and trend analysis
• Predictive analytics for process control
✅ Outcome:
• Validated inline moisture sensing pilot
• Proposed ML-based analytics pipeline
• Enabled data-driven process optimization
📍 Locations: Singapore & Karawang, Indonesia
🗓 Duration: 2018 – 2019
📌 Project Title: Moisture Optimization and Production Volume Analysis
🎯 Objective:
Enable moisture detection in powder-based FMCG products through hyperspectral imaging, and analyze SCADA data for production volume optimization.
👨🔬 Role: Consultant – Imaging & Data Pipeline Architect
🛠 Methods & Technologies:
• Hyperspectral imaging calibration and validation
• Ground-truth moisture profiling
• SCADA data root cause and trend analysis
• Predictive analytics for process control
✅ Outcome:
• Validated inline moisture sensing pilot
• Proposed ML-based analytics pipeline
• Enabled data-driven process optimization
🏢 AC²T Research GmbH – Austrian Competence Centre for Tribology
📍 Location: Viktor-Kaplan-Straße 2/C, 2700 Wiener Neustadt, Austria
📅 Duration: 2020 – 2023
📌 Project Title: Predictive Monitoring of Self-Lubricating Bearings
🎯 Objective:
Develop a semi-supervised ML framework to detect abnormal tribological states in oscillating bushings based on acoustic emission (AE) signals.
👨🔬 Role: Lead Researcher – ML Design & Signal Processing
🛠 Methods & Technologies:
• Custom tribometer with integrated AE and force sensors
• Variational Autoencoders (VAE) and LSTM-based wear modeling
• Sliding window analysis of wear progression
✅ Outcome:
• Predicted wear onset over 3,000 cycles in advance
• Achieved 97% accuracy in normal regime detection
• Findings published in *Friction* (Springer, 2022)
📍 Location: Viktor-Kaplan-Straße 2/C, 2700 Wiener Neustadt, Austria
📅 Duration: 2020 – 2023
📌 Project Title: Predictive Monitoring of Self-Lubricating Bearings
🎯 Objective:
Develop a semi-supervised ML framework to detect abnormal tribological states in oscillating bushings based on acoustic emission (AE) signals.
👨🔬 Role: Lead Researcher – ML Design & Signal Processing
🛠 Methods & Technologies:
• Custom tribometer with integrated AE and force sensors
• Variational Autoencoders (VAE) and LSTM-based wear modeling
• Sliding window analysis of wear progression
✅ Outcome:
• Predicted wear onset over 3,000 cycles in advance
• Achieved 97% accuracy in normal regime detection
• Findings published in *Friction* (Springer, 2022)
🏢 Bystronic Laser AG – Berufsbildung
📍 Location: Industriestrasse 21, 3362 Niederönz, Switzerland
📅 Duration: March 2023 – November 2023
📌 Project Title: Real-Time Quality Monitoring in Laser Cutting Using Acoustic Emissions
🎯 Objective:
Detect burr formation and incomplete cuts in real-time using airborne acoustic sensing during high-speed industrial laser cutting.
👨🔬 Role: Research Lead – Sensor Integration & ML Modeling
🛠 Methods & Technologies:
• Acoustic sensors (Avisoft VS45-H), 1 MHz sampling
• CNNs for cut quality classification
• Frequency domain analysis of cut-through signals
✅ Outcome:
• Enabled real-time cut quality detection
• Demonstrated feasibility for smart QA systems
• Supported pilot validation for industrial deployment
📍 Location: Industriestrasse 21, 3362 Niederönz, Switzerland
📅 Duration: March 2023 – November 2023
📌 Project Title: Real-Time Quality Monitoring in Laser Cutting Using Acoustic Emissions
🎯 Objective:
Detect burr formation and incomplete cuts in real-time using airborne acoustic sensing during high-speed industrial laser cutting.
👨🔬 Role: Research Lead – Sensor Integration & ML Modeling
🛠 Methods & Technologies:
• Acoustic sensors (Avisoft VS45-H), 1 MHz sampling
• CNNs for cut quality classification
• Frequency domain analysis of cut-through signals
✅ Outcome:
• Enabled real-time cut quality detection
• Demonstrated feasibility for smart QA systems
• Supported pilot validation for industrial deployment
🏢 Fraunhofer Institute for Laser Technology ILT
📍 Location: Steinbachstraße 15, 52074 Aachen, Germany
📅 Duration: 2022 – 2023
📌 Project Title: Compact LPBF Module for Alloy Research & Monitoring
🎯 Objective:
Design a laboratory-scale LPBF platform with modular laser access (IR + green) and integrated sensors for alloy processing research.
👨🔬 Role: Technical Collaborator – Sensor Architecture
🛠 Methods & Technologies:
• Compact modular build chamber
• Interchangeable IR and green laser modules
• Preheating and shielding atmosphere
• Embedded real-time sensor interfaces
✅ Outcome:
• Delivered operational LPBF testbed to EMPA
• Enabled real-time process sensing
• Supported under Canton of Bern initiative
📍 Location: Steinbachstraße 15, 52074 Aachen, Germany
📅 Duration: 2022 – 2023
📌 Project Title: Compact LPBF Module for Alloy Research & Monitoring
🎯 Objective:
Design a laboratory-scale LPBF platform with modular laser access (IR + green) and integrated sensors for alloy processing research.
👨🔬 Role: Technical Collaborator – Sensor Architecture
🛠 Methods & Technologies:
• Compact modular build chamber
• Interchangeable IR and green laser modules
• Preheating and shielding atmosphere
• Embedded real-time sensor interfaces
✅ Outcome:
• Delivered operational LPBF testbed to EMPA
• Enabled real-time process sensing
• Supported under Canton of Bern initiative
🏢 Synova S.A.
📍 Location: Route de Genolier 13, 1266 Duillier (Nyon), Switzerland
📅 Duration: 2023 – 2024
📌 Project Title: Real-Time Material Differentiation in Water Jet-Guided Laser Cutting
🎯 Objective:
Enable real-time detection of material transitions in WJGL processes using multimodal sensing for selective cut-stop control in micromachining.
👨🔬 Role: Research Collaborator – Signal Processing & Neural Networks
🛠 Methods & Technologies:
• Sensor fusion: acoustic and optical channels
• Neural networks for multilayer material recognition
• Real-time RMS/variance signal feature extraction
✅ Outcome:
• Achieved in-process material differentiation
• Reduced substrate damage in cutting
• Part of Innosuisse project #58389.1 IP-ICT
📍 Location: Route de Genolier 13, 1266 Duillier (Nyon), Switzerland
📅 Duration: 2023 – 2024
📌 Project Title: Real-Time Material Differentiation in Water Jet-Guided Laser Cutting
🎯 Objective:
Enable real-time detection of material transitions in WJGL processes using multimodal sensing for selective cut-stop control in micromachining.
👨🔬 Role: Research Collaborator – Signal Processing & Neural Networks
🛠 Methods & Technologies:
• Sensor fusion: acoustic and optical channels
• Neural networks for multilayer material recognition
• Real-time RMS/variance signal feature extraction
✅ Outcome:
• Achieved in-process material differentiation
• Reduced substrate damage in cutting
• Part of Innosuisse project #58389.1 IP-ICT