Project Title: Applied Machine Learning and Data Engineering in Business Context Project Focus: Steel Plate Fault Detection Objective: Develop an end-to-end cloud solution for fault detection in steel plates for XYZ Products. Team Members: Alisa Ilina, Anurag Singh, Eirik Egge, Henry Stoll, Magnus Eliassen, Olivia Lundholm
The most common production faults like Bumps, K-Scratches, and Z-Scratches are primarily influenced by factors such as conveyor length, steel plate thickness, and the steel type.
Implementation of the XGBoost model which correctly classifies over 80% of all faults. This allows for the transition from manual fault classification to an automated detection system.
The proposed cloud architecture leverages existing data sources, enabling efficient fault detection and providing a continuous flow of data-driven insights into the production process.
The vision extends beyond just analysis. The aim is to scale automated fault diagnosis across the production line, paving the way for future high-value use cases in the domain.
Delivering substantial value across the production line through cloud-based fault detection mechanisms.
XYZ Products, a renowned steel manufacturer located in Northern Sweden.
Achieve a reduction in costs associated with production faults.
Inefficient manual quality control mechanisms. Significant variable costs due to faulty steel plates. Plates detected as faulty post-sale, resulting in customer dissatisfaction. Manual data collection process for fault labeling. Decision-making often rooted in human assumptions instead of data-driven insights.
Achieved an accuracy of over 80%, marking it as the best performing model for this application.
Other models like MLP, Decision Tree, and LogReg were also evaluated, but XGBoost outperformed them all.
Current System: Manual fault detection and classification lead to increased costs, reduced customer satisfaction, and a lack of actionable insights. Proposed System: An automated fault detection and prevention system powered by machine learning and cloud infrastructure. This includes: Automatic fault detection replacing manual labeling. AI-powered optical recognition of steel plates. Dynamic feedback loops providing actionable insights into the production process, facilitating preventive maintenance and data-driven decision-making.