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      University of Skövde, link to startpage

      Swedish Metal

      Research Group Skövde Artificial Intelligence Lab
      Resarch Environment Informatics

      Swedish Metal

      Research Group Skövde Artificial Intelligence Lab
      Resarch Environment Informatics

      Quick Facts

      Full project name

      Swedish metal

      Duration

      October 2018 – October 2021

      Partners

      Jernkontoret, Sandvik Materials Technology and SSAB.

      Financing

      KK-foundation.

      The project will create new values for the Swedish metal industry. This will be done through the use and development of recent data analysis techniques. By doing so, one will find currently hidden relationships between known and unknown process parameters and the resulting product quality.

      About the project

      In other sectors, where such advanced computer-based data analysis is already in use, for example internet-based retail and services, and financial analysis, business have gone through disruptive changes, allowing for a next level of competitiveness. In areas with very complex tasks, like text and speech understanding, machine-translation, image understanding, and autonomous vehicles, there have been dramatic advancements. However, such advancements have not yet been seen in manufacturing. In metal manufacturing, specifically, a range of complex physical and chemical processes take place, processes that have yet to realize the potential of advanced data analysis. With this project, we expect to better understand the complexity of production processes, for better predictability and controllability of output quality.

      Partners and cases

      In this project, we apply and advance machine learning techniques, for complex industrial manufacturing cases provided by Sandvik Materials Technology and SSAB. For these industrial cases, the process output quality is not fully predictable, but depends on a number of factors that are unknown today.

      Project result

      The expectation is to control complexity and to find many opportunities for both quality, efficiency and sustainability improvements. Further, we expect to develop novel knowledge in improved algorithms for process complexity analysis and complexity management, thereby advancing the field of data analysis in general and machine learning in particular.

      Research team.

      Project leader

      Senior Lecturer in Computer Science

      Partners and financing

      SSAB
      Sandvik
      Jernkontoret
      Knowledge Foundation