Overview
This webinar is Part 3 of the Artificial Intelligence in Industrial Automation and Machinery series.
Many industries are looking to AI to deliver increased efficiency of production processes and improving product quality. The addition of AI in manufacturing leads to increased complexity in the workflow, from design to production. The digital transformation in manufacturing resulted in many production lines instrumented with sensors, but process engineering teams often lack the specialized skills required by AI and struggle to apply analytical models to the process data. This presentation provides an overview of the MATLAB and Simulink platforms and tools that empower process engineers to employ statistical, signal processing and machine learning approaches to improve the quality of noisy process data, apply anomaly detection to monitoring manufacturing processes and quality, and increase efficiency from improved monitoring.
Highlights
- Learn about the three main objectives in applying AI to manufacturing
- Apply advanced preprocessing and feature engineering to build robust models of manufacturing processes
- Empower operators and processes engineers with Apps and dashboards
About the Presenters
Imola Fodor works as an Innovation Engineer at Electrolux. Her responsibilities comprise data-driven project ideation with development, leading PoC projects and AI technology/tools evaluation.
Bernhard Suhm is the product manager for Machine Learning at MathWorks. He works closely with customer facing and development teams to address customer needs and market trends in our machine learning related products, primarily the Statistics and Machine Learning toolbox. Prior to joining MathWorks Bernhard led a team of analysts consulting call centers on optimizing the delivery of customer service. He also held positions at a usability consulting company and Carnegie Mellon University. He received a PhD in Computer Science specializing in speech user interfaces from Karlsruhe University in Germany.
