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Digitalised Prediction Based Production Optimisation

Reference number
Coordinator Luleå tekniska universitet - Institutionen för teknikvetenskap och matematik
Funding from Vinnova SEK 499 100
Project duration November 2017 - April 2018
Status Completed
Venture The strategic innovation programme for Production2030
Call 2017-02880-en

Purpose and goal

The purpose of the project was to evaluate whether a digital twin can be used to predict tool degradation and damage in order to avoid unplanned stop. As a case study a cutting machine on SSAB was chosen since it is crucial for the system availability. The aim was to demonstrate that a digital twin can be used to understand damage and degradation so that unplanned stops can be avoided which has been showed in the project

Expected results and effects

In the project it has been shown that the cutting process is possible to measure and predict by use of a digital twin. We have also shown influences by process parameters and that we can detect damage and degradation of the cutting tool. This idea project is expected to lead to the initiation of full scale research to utilize the demonstrated potential. The long-term effect of utilization of these methods is expected to be increased production quality, increased efficiency, minimized waste and discards in the production process as well as improved service and maintenance.

Planned approach and implementation

In order not disturb the manufacturing process, we chose to develop the technology by use of a cutting machine at LTU. Simulation models of the cutting process were created and a large number of experiments were made to validate models and detect degradation and damage to the cutting tool. At the end of the project, measurements were carried out on a real machine at SSAB in Borlänge to verify that developed technology was possible to implement in an industrial environment.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 25 November 2019

Reference number 2017-04812

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