My detailed portfolio

Research Focus

My research sits at the boundary between surface engineering, precision metrology, and manufacturing intelligence. The unifying question is: how does a surface's geometry, integrity, and texture determine its functional performance, and how do we measure, predict, and control that relationship in real production environments?

This spans three interconnected areas:

Surface Engineering & Tribology

Laser surface texturing (picosecond ablation, micro-dimple arrays), surface hardening, friction and wear control in lubricated and dry contacts, and the design of functional surfaces for powertrain and transmission components. I study how engineered surface features, at the micro- and nanoscale, govern tribological performance and component longevity.

Manufacturing Metrology & Surface Integrity

Multi-scale surface characterization aligned with ISO 25178 (areal parameters, Abbott-Firestone, PSD), using a broad instrumentation base: white-light interferometry (WLI), confocal laser scanning microscopy, focus variation microscopy, stylus profilometry, SEM/EDS, and 3D scanning. Residual stress measurement via XRD, Magnetic Barkhausen Noise (MBN), and ESPI. On-machine and in-line metrology for closed-loop manufacturing, embedding measurement directly into production to shorten quality feedback loops.

Data-Driven Manufacturing & Decision Support

AI-assisted surface classification and anomaly detection, image-based process monitoring using FFT and power spectral density analysis, multi-criteria decision analysis (TOPSIS, AHP, Pugh Matrix) applied to manufacturing systems, and FMEA/FMSA supported by large language models and retrieval-augmented generation (RAG).

Industry Collaboration

I work directly with industrial partners on applied research challenges. Active projects and collaborations involve:

• Scania & TRATON — functional surfaces for automotive applications (Fun4MEL, Vinnova)
• Volvo — multi-axis machining system assessment and surface quality in powertrain manufacturing
• Siemens Energy — surface integrity and functional performance of energy system components (Fun4MEL, Vinnova)
• LEAX — AI-assisted surface metrology for powertrain quality control (AI-MetPower)
• Chalmers University / Scania / Volvo — optical measurement methods for components' surface roughness (CentreX project)

Total externally funded research coordinated or co-led: approximately 11 MSEK across 10+ projects since 2006. Open to new industry collaborations, contract research, and advisory roles.

Selected Projects

• Fun4MEL (Vinnova) — Functional surfaces for mechatronic applications. Consortium with TRATON, Scania, Volvo and others. Focuses on surface functionalization for extended component life and improved wear resistance under electrification and circularity constraints.
• AI-MetPower (Vinnova) — AI-assisted surface metrology for powertrain quality control. Integrating machine learning and image analysis into production metrology workflows.
• CentreX Optical Gear Metrology (KTH/Chalmers, Scania, Volvo) — Optical measurement methods for gear surface roughness specification and verification.

Teaching & Supervision

I teach MG2045 Decision Making for Advanced Manufacturing at KTH, covering multi-criteria decision analysis (TOPSIS, AHP, Pugh Matrix, SWOT, Decision Trees), FMEA, and AI applications in manufacturing. The course combines quantitative methods with real industrial decision contexts.

I supervise PhD and MSc students in surface metrology, laser surface processing, manufacturing data analysis, and AI-assisted quality control.

I also lead the AI-FIKA initiative within my department, building AI literacy and applied AI skills across the research group.

Selected Publications

40+ peer-reviewed publications.

Recent highlights:

• Towards On-Machine Surface Metrology Using Image-Based Frequency Analysis for Surface Variation Analysis — JMMP 2026
• In Situ–On Machine–Post Process Metrology System Design for Machining System Characterization — Journal of Machine Engineering, 2026
• Multi-Axis Machining System Assessment — International Journal of Advanced Manufacturing Technology, 2025 (with Söderberg, Archenti; Seco Tools, Volvo PTP Köping)
• Numerical Study of Dimple Texture Influence on Hydrodynamic Pressure Generation — Metals, 2020
• Experimental Study of Micro-Dimpled Texture in Friction Control under Dry and Lubricated Conditions — 2022

Peer reviewer: Precision Engineering (Elsevier), MDPI journals (Metals, Sensors, Journal of Manufacturing and Materials Processing), euspen

Instrumentation & Methods

• Instrumentation: WLI (Zygo, Bruker, Taylor-Hobson), confocal (Olympus), stylus (Taylor Hobson, Mahr), XRD, MBN, ESPI, SEM/EDS, CMM, CCD microscopy
• Surface analysis: ISO 25178 areal parameters, Gaussian/robust/morphological filtering, PSD, Abbott-Firestone curves, FFT-based texture analysis, ISO 21920 profile parameters, and more
• Software: Python (NumPy, SciPy, pandas, scikit-learn), MATLAB, MountainsMap, SPIP, Comsol
• AI/ML: surface classification, anomaly detection, RAG-based FMEA, image-based process monitoring

Background

PhD in Mechanical Engineering (with honours, 9.67/10), Koszalin University of Technology, Poland, 2013. Dissertation: Analysis of stereometric features of surfaces after abrasive processes using new evaluation parameters.

Based in Sweden since 2015. At KTH since 2015, current position since 2018.

Contact

rtom@kth.se  |  +46 8 790 90 74

Google Scholar: scholar.google.com/citations?user=rtomkowski

LinkedIn: linkedin.com/in/roberttomkowski

KTH profile: kth.se/profile/rtom