Stabilize and Enhance Reliability in Plastics Production with Anomaly Detection

Join us on March 20th 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 Session

We offer this webinar in English and German.

English Session: March 20th at 4 PM CET

German Session: March 20th at 10 AM CET

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Live 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

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Speaker

Dr. Phil Gralla

Data Scientist

sensXPERT