Abstract
The integration of a digital twin into inspection planning enables a novel procedure that reduces avoidable inspection times and costs. This paper shows a method for component-specific adaption of inspection plans by feeding back data-based quality results into inspection planning. An initial evaluation of the method on a real aerospace aluminum component is carried out using a 3-axis milling process. Machine learning based quality models were implemented for the inspection features shape deviation and surface roughness. With the knowledge gained, the inspection time for the process can be reduced by up to 75 % per component.
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