Hamid Ghorbani

Turn-milling is a relatively new process which combines turning and milling operations, offering a number of advantages such as chip breaking and interrupted cutting, which improves tool life. In addition to providing the capability of producing eccentric forms or shapes, it increases productivity for difficult-to-machine material at lower cost. This study investigates the influence of cutting speed and feed on surface roughness and tool wear in conventional turning and turn-milling of H13 tool steel. The tests were conducted for longitudinal and face machining strategies. It was found that the range of surface roughness in turning is lower than in turn-milling. In longitudinal turning, face-turning, and face turn-milling operations, surface roughness is elevated in the higher feeds. However, the surface roughness in longitudinal turn-milling operations can be reduced by increasing the feed. Although the simultaneous rotation of the tool and workpiece in turn-milling could negatively affect the surface quality, this operation provides the advantage of an interrupted cutting mechanism that produces discontinuous chips. Also, the wear of the endmill in longitudinal and face turn-milling operations is lower than the wear of the inserts used in conventional longitudinal and face turning. Using Response Surface Methodology (RSM), mathematical models were developed for surface roughness and tool wear in each operation. The RSM models developed in this study achieved coefficients of determination (R²) above 90%, with prediction errors below 7% for surface roughness and below 3% for tool wear. The analysis of variance (ANOVA) revealed that the feed and cutting speed are the most influential parameters on the surface roughness and tool wear, respectively, with p-value < 0.05. The experimental results demonstrated that tool wear in turn-milling was reduced by up to 50% compared to conventional turning.

Point cloud processing of raw scan data is a critical step to enhance the accuracy and efficiency in computer-aided inspection and remanufacturing of damaged aero-engine blades. This paper presents a new methodology to obtain a noise-reduced and simplified dataset from the raw scan data while preserving the underlying geometry of the damaged blade in high-curvature and damaged regions. At first, outliers are removed from the scan data, and measurement noise is reduced through local least-squares quadric surface/plane fitting on the adaptive support domain of measured points under the measurement uncertainty constraint of inspection data. Then, a directed Hausdorff distance-based region growing scheme is developed to progressively search within the support domain of denoised data points to obtain a down-sampled dataset while preserving the local geometric shape of the surface. Numerical and experimental case studies have been conducted to evaluate the accuracy and computation time of scan-to-CAD rigid registration and CAD-to-scan non-rigid registration processes using the down-sampled dataset of damaged blades. The results have demonstrated that the proposed methodology effectively removes the measurement noise and outliers and provides a down-sampled dataset from the scan data that can significantly reduce the time complexity of the computer-aided inspection and remanufacturing process of the point cloud of damaged blades with a negligible loss of accuracy.

Airfoil blades are typically inspected in sections to verify their conformance to the geometric tolerances specified on their nominal design. To maintain the accuracy of geometric error evaluation, in particular, for the position and orientation errors of the airfoil sections, sectional airfoil profiles should be reconstructed from the inspection data points. This paper presents a new method to automatically reconstruct the airfoil profile from unorganized noisy sectional data points of 3D scanned blades. A three-step airfoil profile reconstruction approach is presented. First, the algorithm thins the scattered set of sectional data points by projecting them onto the local curves fitted to them. For this purpose, a recursive weighted local least-squares scheme is proposed to fit local curves within the measurement uncertainty constraint of inspection data. Then, to order the thinned set of data points, the profile polygon is generated and imperfect nodes are modified by evaluation of the angular deviation of edges. Finally, a closed nonperiodic B-spline curve is fitted to the thinned and ordered set of data points to construct the smooth airfoil profile. A series of case studies have been carried out to demonstrate the effectiveness of the proposed airfoil profile reconstruction method. Implementation results have demonstrated that the proposed method is accurate and robust to noise. In addition to blade inspection, other applications such as repair and adaptive machining of aero-engine blades can equally benefit from the proposed method for automatic airfoil profile reconstruction.

This paper presents a novel method for aligning the scanned point clouds of damaged blades with their nominal computer-aided design (CAD) model. To inspect a damaged blade, the blade surface is scanned and the scan data in the form of a point cloud is compared with the nominal CAD model of the blade. To be able to compare the scanned point cloud and the CAD model, they must be brought to a common coordinate system via a registration algorithm. The geometric nonconformity between the scanned damaged blade and its nominal model stemmed from the damaged regions can affect the registration (alignment) outcome. The alignment errors then cause wrong inspection results. To prevent this from happening, the data points from the damaged regions have to be removed from the alignment calculations. The proposed registration method in this work can accurately and automatically eliminate the unreliable scanned data points of the damaged regions from the registration process. The main feature is a correspondence search technique based on the geometric properties of the local neighborhood of points. By combining the average curvature Hausdorff distance and average Euclidean Hausdorff distance, a metric is defined to locally measure the dissimilarities between the scan data and the nominal model and progressively remove the identified unreliable data points of the damaged regions with each iteration of the fine-tuned alignment algorithm. Implementation results have demonstrated that the proposed method is accurate and robust to noise with superior performance in comparison with the existing methods.

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.