Project: In-line visual inspection using unsupervised learning
The purpose of this project is to introduce and improve machine learning- assessment of the quality of massproduced industrial (steel) products, and in an easier and more efficient way detect quality problems. The goal is to introduce deviation detection based on normalizing flows in real industrial processes, and strengthen the existing End-to-End solution within visual inspection.
Project manager Diana Unander Other project members Welf Löwe, Morgan Ericsson, Jonas Nordqvist, Felix Viberg, Martin Kroon and Björn Lindenberg Participating organizations Linnaeus University, Gimic and Gunnebo Industries, which is part of The Crosby Group and SKF Financier Vinnova (within the call for Advanced and innovative digitization autumn 2022) Timetable 1 January 2023 – 31 December 2025 Subject Computer and information science (Department of Computer Science and Media Technology), Mathematics (Department of Mathematics), Mechanical Engineering (Department of Mechanical Engineering). All subjects from the Faculty of Technology.
More about the project
Product Lifecycle Management (PLM) manages the entire lifecycle of a product from its birth through the engineering, design and manufacture, service, and disposal of the manufactured products in the industry. In the context of advanced manufacturing industries, the applications of AI technologies are emerging in the PLM.
This project aims to use Big Data and machine learning in the industrial product life cycle management and construct a sustainable experience for society. The current goal of the research is to use natural language processing (NLP) to automate the extraction of crucial information from free-text of service calls and on-field technician comments. For this purpose, we are analysing service calls and machine logs data. The long-term goal of the research is the predictive maintenance of professional washers and dryers.