Optimisation of a robotic painting process by implementing Design of Experiments
Abstract: The modern painting process in automotive industry is complex and a lot of factors affect the result. The trial and error method is used today to control the quality and introduce new colours. This method takes a lot of time and does not show any clear numbers of how the process is affected by changing the parameters. During this thesis, we have investigated a delimited number of parameters. The work is based on experiments performed on samples that represents a flat surface of the cab, to reduce experimental costs. Our master thesis is done at Scania in Oskarshamn, where all the cabs for the European production is produced. The objectives with this thesis has been to explain how the process parameters of the robotic applicator affect the paint distribution, paint thickness and the colour of the top coat. We also optimised the process by finding which settings gives an even paint distribution, a correct thickness and an accepted colour of the top coat. We have been using Design of Experiments to achieve the goals of this study. Design of Experiments is a statistic method that is used to perform experiments effectively. It also shows the effect of changing the factors from a low to a high level. We have chosen to divide the workflow into three parts: screening, optimisation and confirmation. The experiments are performed during the daily production to replicate the real circumstances. The shape air, paint flow and high rotation is the most important parameters to control. Paint flow also seems to have a linear impact on the thickness of the top coat layer. The Shape air and the high rotation on the other hand mainly affect the distribution of the top coat layer. Different levels are needed for the shape air and high rotation depending on what paint flow is used. The optimal settings of the factors for our colour were found to be paint flow at 82 %, the shape air at 90 %, the high rotation at 90 % and high voltage at 100 %. The optimal settings give a result of 1,535 μm in spread and 40,08 μm in mean thickness. Our settings compared to today’s results contributes to a reduced paint consumption, better quality and therefore less rework.
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