Computed tomography (CT) is a flexible measurement method for a variety of metrological tasks. However, to achieve reliable and accurate measurements, interrelated setting parameters must be chosen. The workpiece orientation is one of such parameters, especially for multimaterials. Current approaches to support optimal orientation do not adequately consider the effect on accuracy in complex measurement tasks. Additionally, only few experimental studies provide the therefore necessary insights. Consequently, this study aimed to investigate the influence of orientation of a multimaterial workpiece on the subsequent evaluation and measurement process. For this purpose, a workpiece containing multiple features was scanned in a total of 14 orientation configurations and the resulting effects on both image (based on contrast-to-noise-ratio) and measurement quality (based on measurement accuracy) were investigated. It was shown that components measurable in the medium power range of a CT system have a wide range of optimal angles for optimal orientation. The image quality is suited for its identification.

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Industrial x-ray computed tomography (CT) has become an important tool for detecting and characterizing pores in workpieces produced through additive manufacturing (AM). However, a procedure to quantify the ability of a CT system to reliably detect pores, ideally in a non-destructive manner and before the actual analysis process, is still being researched. Previous approaches can either only be carried out at a great expense and destructively, or have not yet been validated in the actual case of pore detection. This work presents a potential reference object and corresponding performance parameters, the metrological structural resolution and the grey-scale resolution. To investigate their suitability for predicting the ability to detect pores, both the reference object, and special pore-containing AM samples were examined using the same CT settings. Linking the performance parameters with the pore detection rate of the AM samples showed that structural resolution, but also image sharpness, are suitable parameters.

Industrial x-ray computed tomography (CT) is increasingly used in the field of dimensional metrology. However, the measurement accuracy is influenced by many factors for which comprehensive expert’s knowledge is still not available. This work presents an approach to establish a user support system that allows a user to achieve highly accurate measurements. The approach generates knowledge from experimental investigations deploying specifically designed test parts and uses the knowledge in a case-based reasoning (CBR) user support system. Validation experiments showed that the user support system was successful at providing a user with instructions that led to highly accurate measurements of three previously unknown industrial workpieces.

Glass fiber (GF) Sheet Molding Compound (SMC) composites are popular lightweight materials due to their good processability. Hybrid SMCs expand the field of operation, combining the high stiffness of unidirectional carbon fibers (CF) with the economic efficiency of GF. Combinations of manufacturing deviations (delamination, varying GF content, CF misorientation) occur during the production of hybrid SMCs and impede the mechanical performance of the part. A function-oriented quality assurance instead of strict tolerances is proposed. Finite element (FE) simulations are computationally too expensive for an assessment within the cycle time. Hence, surrogate models are trained on multiple parameterized FE simulations. The surrogate models shall allow for an individual functional assessment in real-time based on integrated measurement inputs. This work focuses on the generation of parametrized FE simulations for measurement inputs and surrogate modeling. Simulations and surrogate models show acceptable deviations from tensile tests for multiple combinations of manufacturing deviations. The measurement uncertainty of the stiffness prediction is assessed for both the FE simulation and the surrogate models in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM).