Moulds for aeronautical applications must fulfil highly demanding requirements, including the geometrical tolerances before and after curing cycles at high temperatures and pressures. The growing availability of thermoplastic materials printed by material extrusion systems requires research to verify the geometrical accuracy after three-dimensional printing processes to assess whether the part can meet the required geometry through milling processes. In this sense, the application of automated techniques to assess quick and reliable measurements is an open point under this promising technology. This work investigates the integration of a 3D vision system using a structured-light 3D scanner, placed onto an industrial robot in an eye-in-hand configuration and synchronized by a computer. The complete system validates an in-house algorithm, which inspects the whole reconstructed part, acquiring several views from different poses, and makes the alignment with the theoretical model of the geometry of big parts manufactured by 3D printing. Moreover, the automation of the validation process for the manufactured parts using contactless detection of the offset-printed material can be used to define milling strategies to achieve the geometric qualifications. The algorithm was tested using several parts printed by the material extrusion of a thermoplastic material based on black polyamide 6 reinforced with short carbon fibres. The complete inspection process was performed in 38 s in the three studied cases. The results assure that more than 95.50% of the evaluated points of each reconstructed point cloud differed by more than one millimetre from the theoretical model.

Dimensional tolerances and surface finish are two fundamental elements that must be carefully controlled during the manufacturing of industrial parts. The study of signals obtained from the machining of parts is useful for the control and monitoring of the process. For this reason, correlating these signals and information on the surface quality of the machined part is essential for the early detection of breaches in the quality levels of the parts. Therefore, the objective of this work is to develop an intelligent strategy capable of estimating the artificial geometric error induced in the part during machining. In order train these strategies, the experimental signals of the process and the three-dimensional scanned geometry of the machined part are used as starting data. This development aims to reduce both defects and production time, increasing the efficiency, productive capacity and environmental sustainability of the industrial process.