Abstract
The 3D printing technology is innovative, allowing the creation of customized objects. Due to the possible reduction of manufacturing costs, 3D objects printing becomes increasingly attractive to several industrial areas. High levels of customization are achieved at low cost on a wide variety of materials. Despite of high levels of reliability and performance of this technology, it is still necessary to ensure that printed objects meet industrial standard quality requirements. Non–destructive tests are a class of possible tests that may be performed to verify if the industrial standard quality requirements are met. Some non–destructive tests require sampling the object at predefined positions. This paper aims to present an automatic procedure to perform non–destructive sampling of the object, by computing the optimal trajectory planning of the sampling equipment. The procedure is suitable to any inspection machine with three standard XYZ axes and two additional axes allowing the adjustment of the inspection head (e.g. a thermographic camera) to better inspect the object. Inspection trajectories to be computed are to be optimal w.r.t. the total time of inspection, while avoiding collisions between the object and the inspection head/camera. While any (3D printed or not) object can be considered, we take advantage of the 3D object representation (in the STL format used for printing) to determine the optimal inspection trajectories. Strong and weak points of the proposed methods are analyzed through a case study.
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Acknowledgements
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020 (ALGORITMI).
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Ramos, B., Pinho, D., Vaz, A.I.F. (2022). Optimal Inspection Trajectories for 3D Printed Objects. In: Dignum, F., Mathieu, P., Corchado, J.M., De La Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Lecture Notes in Computer Science(), vol 13616. Springer, Cham. https://doi.org/10.1007/978-3-031-18192-4_29
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