The total error is not significantly higher than the partial errors which may compensate each other. Consequently SegE, DEE and PrE should be analyzed individually to describe the result quality as their sum according to rules of error propagation. Current methods for quality assurance of the segmentation are often either realistic and accurate or resource efficient. Future research should focus on implementing models for cost effective evaluations with high accuracy and realism. Our system of categorization may be enhancing the understanding of the overall process and a valuable contribution to the structural design and reporting of future experiments. It can be used to educate specialists for risk assessment and process validation within the additive manufacturing industry.