Digitalised Prediction Based ProductionOptimisation

Digitalised Prediction Based ProductionOptimisation

Project time: 2017 – 2018

Budget: 499 500 kronor

Funding: SIP Produktion2030

Industrial production systems typically include many process steps performed by automatic or semi-automatic machines. Depending on the different variables, these machines age and thereby affecting both the quality of the manufacturing step and the resource requirements

Industrial production systems typically include many process steps performed by automatic or semi-automatic machines. Depending on the different variables, these machines age and thereby affecting both the quality of the manufacturing step and the resource requirements. Existing state-of-the-art monitoring systems typically do not conform to either the historical outcome or predicted future outcomes, which means that many industrial stages of production today are conducted in an unsustainable manner w ith quality problems, waste, disposals, overconsumption of resources, etc. as a result. To address these problems, the idea to be tested in this project is that the unique response signature of the manufacturing machine, together with information about the environment and production output can be used to generate an adaptive digital twin. The idea is to use the adaptive digital twin to predict optimal production output and then control the machine as well as to plan service and maintenance fulfilling this prediction. This digitized prediction-based production optimization system will be tested by two research groups at Luleå Technical University and personnel at SSAB Borlänge through a case study. There is a great value of this idea which can contribute to significantly increased sustainability in industrial production

 

 

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2017 – 2018

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