Grouping of Paint Strokes to Reduce Search Space of Scheduler and Improve Painting Quality

E. Kolakowska and M. Kristiansen (Denmark)


Scheduling, grouping, paint quality, process automation.


When the robots are used in the industry to perform some of the jobs and their programs are generated automatically, it is necessary to make scheduling of their tasks. This problem is new for the automated painting process, where the number of tasks often contains 100 200 paint strokes. This paper deals with two big issues when scheduling paint strokes: reducing the size of a NP-hard problem so it is computational feasible and incorporating the process knowledge to improve paint quality. The method introduced in this paper is a grouping approach. It is a method for decomposing the problem into a number of sub problems, called groups, which both reduces the search space of the scheduler and improves the quality. A defect explored in this work is a paint dust. It occurs when the time interval between two overlapping paint strokes is too big, causing the first paint layer to dry and not flow together with the fresh paint. Simulations on two examples are made. They demonstrate clearly that grouping minimises the CPU time of the scheduler and prevents the quality defects from paint dust.

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