This work describes a machine vision system workflow to automatically estimate the broaching tool wear. The proposed system offers the possibility to evaluate the evolution of wear under different machining conditions and to decide when a tool should be replaced, guaranteeing the quality of the machined part and avoiding catastrophic tool breakage. In addition, the paper discusses the advantages of the proposed method over the traditional and widely used ISO 3685:1993 based methods, which are highly influenced by the operator. The proposed method uses a novel wear area segmentation technique based on Machine Learning artificial intelligence, generating highly reproducible values, saving technicians labor-intensive tasks, and obtaining values with high accuracy. The results show a strong relationship between the values obtained by the proposed automatic method and the experimental ones, with errors below 0.17% and 2.88% corresponding to the MSE and MAE respectively.
The scope of this work is to present a reverse engineering (RE) methodology to achieve accurate polygon models for 3D printing or additive manufacturing (AM) applications, as well as NURBS (Non-Uniform Rational B-Splines) surfaces for advanced machining processes. The accuracy of the 3D models generated by this RE process depends on the data acquisition system, the scanning conditions and the data processing techniques. To carry out this study, workpieces of different material and geometry were selected, using X-ray computed tomography (XRCT) and a Laser Scanner (LS) as data acquisition systems for scanning purposes. Once this is done, this work focuses on the data processing step in order to assess the accuracy of applying different processing techniques. Special attention is given to the XRCT data processing step. For that reason, the models generated from the LS point clouds processing step were utilized as a reference to perform the deviation analysis. Nonetheless, the proposed methodology could be applied for both data inputs: 2D cross-sectional images and point clouds. Finally, the target outputs of this data processing chain were evaluated due to their own reverse engineering applications, highlighting the promising future of the proposed methodology.
The scope of this work is to assess the influence of porosity on the mechanical behaviour of L-PBF Inconel 718. To this end, some test specimens were manufactured by L-PBF, according to ASTM E8/E8M. Afterwards, these specimens were scanned by means of XRCT in order to analyse the porosity of each one. The XRCT geometry generated by the previous step were used for FEM analysis. Finally, the results obtained by the analysis were correlated with the experimental results, highlighting the promising future of the proposed methodology.
The main objective of the proposed work was to analyse the influence of magnification and focal spot size scan settings on X-ray computed tomography (CT) measurements results under commercial threshold-based algorithms. The relationship between spatial resolution and contrast sensitivity in CT scans of different materials and the accuracy of the resulting CT measurement results is discussed. For that purpose, Aluminium, Copper, Inconel 718 and Titanium disk phantoms were scanned. Preliminary measurements showed that deviations can increase up to 0.48% when the scanning magnification was increased while, for a given magnification, the decrease of a focus size from 1mm to 0.4mm slightly improves the differences up to 0.15%, being negligible at low magnifications. Unsharpness (U
T
) and contrast-to-noise ratio (CNR) were calculated for each scanning conditions according to standard ASTM E1695 – 20. A new image quality indicator that includes the combined effect of the U
T
and CNR was proposed in order to relate measurement error with the image quality. The indicator proves that the influence of CNR is much higher than influence of U
T
on the CT measurements.
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