Our research is built on three interconnected pillars:
advanced perception, intelligent modeling, and real-world technological integration.
Advanced machine vision and multimodal sensing for precise data acquisition and process understanding
Predictive, generative, and hybrid AI models combining data-driven approaches.
Deployment of AI models into real-world systems and edge environments
Our research follows a structured and reproducible framework that
transforms scientific innovation into validated technological solutions.
Scientific Methods
Development of machine vision, AI, and hybrid modeling methods for complex systems.
Experimental Validation
Controlled experiments, multimodal data acquisition, and rigorous benchmarking
Technological Integration
Translation of models into deployable hardware-software and edge-enabled systems
Real-World
Impact
Validated solutions for industrial, food, and pharmaceutical environments
Our methods are translated into validated solutions across manufacturing,
food systems, and pharmacutical technologies.
Smart Manufacturing
We develop machine vision and AI-enabled monitoring systems for intelligent manufacturing environments.
- Machining intelligence
- Optical quality inspection
- Welding diagnostics
- Digital twin-enabled production
Food System &
Agri-Tech
We apply machine vision and AI to enhance sustainability and quality cross the food value chain.
- Precission agriculture
- Intelligent optical sorting
- Plant health monitoring
- Food processing optimization
Medical Technologies
We develop machine vision and AI solutions for regulated environments, with focus on pharmaceutical technologies.
- Optical inspection systems
- AI-supported quality control
- Multimodal process validation
- Compliance-oriented digital twins
We operate across TRL 1-6, bridging scientific innovation and validated deployment
Fundamental Research
Basic principles observed and technology concepts formulated
Experimental
Validation
Proof-of-concept demonstrated and experimental development
Pilot
Systems
Pilot-scale testing and validation in relevant environments
Industrial
Deployment
Validated production technology and deployment in the industry
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Scientific publications
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Industrial partners
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Finished student theses
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Active/completed projects
Temporal and statistical insights into multivariate time series forecasting of
Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a…
Using a Region-Based Convolutional Neural Network (R-CNN) for Potato Segmentation
This study focuses on the segmentation part in the development of a potato-sorting system that utilizes camera input for the segmentation and classification of potatoes. The key challenge addressed is the need for efficient segmentation to allow the sorter to handle a higher volume of potatoes simultaneously. To achieve this,…
Advancing Intelligent Toolpath Generation: A Systematic Review of CAD–CAM Integration
This systematic literature review investigates advancements in intelligent computer-aided design and computer-aided manufacturing (CAD–CAM) integration and toolpath generation, analyzing their evolution across Industry 4.0 and emerging Industry 5.0 (I5.0) paradigms. Using the theory–context–characteristics–methodology framework, the study synthesizes 51 peer-reviewed studies (from 2000 to 2025) to map theoretical foundations, industrial applications,…
Predicting Corn Moisture Content in Continuous Drying Systems Using LSTM
As we move toward Agriculture 4.0, there is increasing attention and pressure on the productivity of food production and processing. Optimizing efficiency in critical food processes such as corn drying is essential for long-term storage and economic viability. By using innovative technologies such as machine learning, neural networks, and LSTM…
Hardened workpiece shape prediction using acoustic responses and deep neural
This study proposes a novel approach to predict the shape of hardened metal workpieces using acoustic responses processed by a deep convolutional neural network (CNN), aiming to advance automated straightening in manufacturing. Tool steel 1.2379 workpieces of varying widths (24 mm, 90 mm, 200 mm) were struck using a custom-built device, with acoustic…
Large language models for G-code generation in CNC machining: A
This research explores the viability of producing ISO G-code for 3-axis machining with OpenAI’s Chat Generative Pre-Trained Transformer models, particularly ChatGPT-3.5 and the newer GPT-4o. G-code (RS-274-D, ISO 6983) converts human directives into commands that machines can understand, controlling toolpaths, spindle velocities, and feed rates to produce particular aspects of…