How to use Artificial Intelligence (AI) to automatically predict process stability and quality on the shop floor?
Wednesday, October 21, 2020
Sponsored by Engel
This webinar will explore ways that plastics manufacturers can easily bridge the gap between their current production status and the new possibilities using Machine Learning and Artificial Intelligence.
Learn how to use internal resources such as valuable process engineering expertise in combination with practical technologies to automatically predict process stability and quality issues.
Mr. Michael Aichinger, Managing Director, DAIM GmbH
Michael Aichinger has a background in physics and has been working as a data scientist and simulation engineer in different fields for 15 years. In the past years, he has focused on the injection molding industry and specific fields related to smart machines and smart production. In several projects, he has been working on the analysis of process data and examined factors influencing the quality of plastic products. He has a profound knowledge of machine learning and statistical methods. As managing director of DAIM, he is responsible for quantitative methods and overlooks the data science group within the company.
Ms. Hayane Braganca, Sales Manager, TIG USA LLC
Hayane Braganca holds a Master in Business Administration and a Bachelor in International Business. Since 2014, she works in consultative sales helping clients identify business opportunities using data and business intelligence. Her role includes supporting manufacturers to become paperless, improve their productivity and machine utilization with a Manufacturing Execution System from TIG.