Artificial Intelligence and Big Data Analytics Laboratory

Advanced Solutions at METU-DTX’s Artificial Intelligence and Big Data Analytics Laboratory!

Smart-Flexible Manufacturing Systems Lab

Artificial Intelligence and Big Data Analytics Lab

Virtual and Augmented Reality Lab

The Artificial Intelligence and Big Data Analytics Laboratory at METU-DTX is designed to cover all stages of production processes through Big Data Analytics and AI applications.

This lab plays a crucial role in elevating the products of SMEs in the machinery sector to Industry 4.0 levels. By coordinating with the laboratories at the centre, it gathers real-time, high-volume data from various sensors, providing AI support for prototype systems based on the analyses of this data.

Why Choose the Artificial Intelligence and Big Data Analytics Laboratory at METU-DTX?

At the Artificial Intelligence and Big Data Analytics Laboratory, the unique challenges of digital transformation are well understood. Equipped with the latest technologies and expert staff, the lab develops practical solutions to enhance productivity, improve quality, and reduce costs. Partnering with the lab can help realize new potentials and advance businesses in various sectors.

Big Data Topics

Research on scalable and efficient methods for processing, storing, and analyzing large volumes of data.
Techniques for data cleaning, transformation, fusion, and integration within distributed computing frameworks and databases.
Descriptive statistics and visualization.

AI Topics

Logical AI
Representation
Reasoning
Planning

Pattern Recognition
Inference
Learning from Experience

Key Applications

Production Line Optimization:

Using smart sensors, advanced cameras, and intelligent robots integrated with data analytics to monitor and manage production processes, enhancing efficiency and enabling robotic automation.
Supply Chain Management:

Improving supply chain planning, inventory management, demand forecasting, logistics optimization, warehouse automation, and supplier relationship management. This optimization can reduce costs, improve the use of storage spaces, and enhance delivery processes.
Autonomous Robots/Devices:

Designing robots/devices that adapt to changing conditions, collaborate with humans, detect obstacles (AGVs and drones), and more, fostering not just automation but also autonomous systems.
Predictive Maintenance:

Utilizing predictive maintenance, service management, and maintenance/repair prioritization to foresee or catch equipment failures or product defects, optimizing both manufacturing processes and service management.
Quality Control:

Automatically inspecting product quality using image processing and machine learning techniques, reducing the incidence of faulty products and recall costs.
Energy and Sustainability:

Applying AI for energy consumption forecasting, efficiency analysis, renewable energy integration, and carbon footprint calculations, which can lower energy costs and reduce environmental impact.
Product Development and Design:

Accelerating tasks like innovative prototyping and geometry preparation through generative design, aiding engineers in creating solutions beyond traditional thinking and reducing the number of required simulations and prototypes.
Human Resources Management:

Enhancing recruitment processes, talent management, training and development programs, and performance management, improving overall workforce productivity.
Marketing and Sales:

Implementing customer segmentation, personalized marketing, pricing strategies, and customer relationship management. Integrating data analytics and predictive analysis can lead to more effective marketing campaigns and increased sales potential.