Planning offline inspection strategies in low-volume manufacturing processes

The design of appropriate and successful quality-inspection strategies plays an important role within manufacturing organizations. It is one of the leverage factors to ensure customers the expected quality level of products. In the case of low-volume or single-unit productions, such as those produced with emerging additive manufacturing (AM) technologies, the design of quality controls may be problematic due to the lack of historical data and the inadequacy of traditional statistical approaches. In literature some studies focused on the design and selection of in-process inspection strategies for low-volume productions. However, in some cases, such as AM productions, in-process inspections may not be adequate, easy to perform or cost-effective. To this end, the present work aims at identifying a general methodology for planning offline inspections for low-volume productions. The specific research question addressed concerns how to select the best compromise between effectiveness and affordability of alternative offline inspection strategies for such productions. The proposed method consists of formulating a probabilistic model for predicting defects and defining two performance indicators that outline the overall effectiveness and affordability of an offline inspection strategy. This approach is finally applied to a real low-volume AM production of parts manufactured by selective laser melting (SLM) technique