Stabilize and Enhance Reliability in Plastics Production with Anomaly Detection
Explore with us how anomaly detection can drive quality, efficiency, and cost-effectiveness in plastics manufacturing.
Watch our webinar for an introduction to anomaly detection
Artificial intelligence (AI) and machine learning are rapidly gaining traction across a wide range of industries. In the plastics industry, it was already proven to enhance efficiency, reduce waste, and improve product quality. The most common approaches—such as large language models (LLMs) and convolutional neural networks—are based on supervised learning, which requires extensive labeled datasets to achieve optimal performance. However, there is also a class of unsupervised algorithms that uncover patterns within unlabeled data.
In this webinar, we will focus on the unsupervised method known as anomaly detection, and explore how it can drive quality, efficiency, and cost-effectiveness in plastics manufacturing. We will discuss how anomaly detection helps stabilize and enhance reliability in manufacturing processes. Finally, we will show how you can advances anomaly detection even further by integrating it with our cutting-edge process control technology.
Two Sessions
We offer this webinar in English and German.
English Session: Below
German Session: Here
On-Demand Webinar
Agenda
What you can expect from this webinar:
- Introduction to unsupervised anomaly detection
- Application of anomaly detection in production
- Explanation of basic concepts in anomaly detection as well as unsupervised learning
- Q&A session
Speaker
Dr. Phil Gralla
Data Scientist
sensXPERT