Is your factory running at peak efficiency?
- 23 maj2025
In today's competitive industrial landscape, the difference between market leaders and followers often comes down to operational efficiency. Whether you're managing a global manufacturing operation, or a specialized production facility, unplanned downtime and quality issues directly impact your bottom line.
This exclusive webinar dives into how advanced optimization and AI is revolutionizing production. We bring together three industrial experts who have successfully bridged the gap between optimization techniques, cutting-edge AI research and practical shop floor implementation. They'll share approaches that deliver measurable results across industries.
Download the invitation here.
Dato
23 maj 2025 kl. 10:00 - 11:10
Sted
Microsoft Teams
Sprog
English
Tilmeldingsfrist
20 maj 2025
Pris
Participation is free of charge. If you are registered and do not attend without notifying in advance, a no-show fee of 250 DKK (excluding VAT) will be charged
Contact for more information
MADE: International Senior Consultant, PhD Merete Nørby, mnorby@made.dk, +45 2112 3991
Program
10:00 Welcome and introduction from MADE
International Senior Consultant, PhD Merete Nørby, MADE, Denmark
10:05 Intelligent Anomaly Detection in Manufacturing: A Multi-Sensor ML Approach to Predictive Maintenance
Associate Professor, Dept. of Materials and Production Chen Li, Aalborg University, Denmark
In this talk, we will showcase an innovation to IoT solution for the Robot Screwing Cell that transforms manufacturing maintenance through data-driven prediction. By integrating multiple data streams— robot, screwdriver, and acoustic data—our system employs machine learning algorithms to detect anomalies in real-time.
Our approach enables early detection of equipment failures while providing deeper insights into production processes, allowing manufacturers to optimize operations and reduce costly downtime. Join us to explore the practical implementation and business benefits of intelligent ML-driven systems in industrial settings.
10:20 Model-Based Optimization of Predictive Maintenance Planning for Cost-Effective and Reliable Industrial Equipment
Research Associate PhD, MIET, FHEA Nasser Amaitik, Aston University, Birmingham, UK
Our innovative predictive maintenance planning framework transforms manufacturing operations through integrated data-driven decision-making. Our approach employs advanced GA-based algorithms to optimize maintenance plans across predefined time horizons and enables precise degradation forecasting while evaluating different maintenance strategies, allowing manufacturers to make informed decisions that reduce downtime and operational costs.
Our framework's adaptability across various industrial applications and equipment types makes it a versatile solution for modern manufacturing challenges.
10:35 Finding manufacturing anomalies by analyzing production data with AI solutions
PhD, Production and Innovation Manager Ferenc Tolner, am-LAB; Szombathely, Hungary
A practice inspired process optimization approach will be discussed on the field of pultrusion that is a lesser-known plastic production procedure for parts with the need of extreme durability. Besides standard data analytical considerations Machine Learning techniques (Random Forest, Grandient Boosting, Logistic Regression and SVM) are applied in order to attempt the classification of faulty and non-faulty parts based on historical information.
Furthermore, in-depth understanding of production information is carried out to be able to recommend restrictions on disturbing factors and negative influences. Besides analysing historical data, sensorics solutions are being applied for real-time dimensional analysis and continuous parameter adjustment to maintain quality and reduce associated costs.
10:50 Q&A and discussion
11:10 Conclusion
