Modelling and Control of Heat Distribution in a Powder Bed Fusion 3D Printer

• Main Authors: Hanses, Jonathan, Eriksson, Morten
• Format: Others
• Language: English
• Published: Linköpings universitet, Reglerteknik 2019
• Subjects: Control; Modelling; 3D printer; PBF; Heat Distribution; Control Engineering; Reglerteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159792

This thesis report describes how to improve the control of the temperature in a Powder Bed Fusion 3D printer. This is accomplished by first creating a model ofthe thermal system. To create a good model, both black-box and grey-box models of the system are estimated and compared. Based on the best model, different control designs are examined and the results are compared to find the control design yielding the best results. The system being modelled is a multiple input multiple output system with acomplex internal structure. The modelling can be divided into several steps. Firstly, data has to be acquired from the system. Secondly, the data is analysed and processed. Thirdly, models are estimated based on the collected data. Different model structures such as state-space, ARX, ARMAX, Output Error, Box Jenkins and grey-box models are examined and compared to each other. Finally, the different derived models are validated and it turns out the ARMAX model yields the best prediction capabilities. However, when the controllers were tested on the actual system the controllers that are based on the grey-box model yield the best results. The different control designs examined in this work are diagonal PI controllers, decoupled PI controllers, feed forward controllers, IMC controllers and statefeedback controllers. The controllers are all based on the derived models. The controllers are implemented into a code structure capable of communicating with the printers. Here, tests of the performance for the different controllers on the actual system are executed. The results show that a non-linear system can be controlled using linear controllers. However, introducing some fuzzy control elements such as limiting the controllers to only be used within small temperature intervals and using a fixed input outside this interval yield better results. From these results, the best linear controller is a diagonal PI controller tuned from a grey-box model with as many states as there are controllable areas of the powder bed. The improvement is only marginal compared to the original PI controller, reinforcing the conclusion that some non-linear strategies are needed in the controller in order to achieve significant improvements.