Improving geometric accuracy of 3D printed parts using 3D metrology feedback and mesh morphing

ABSTRACT: Additive manufacturing (AM), also known as 3D printing, has gained significant interest due to the freedom it offers in creating complex-shaped and highly customized parts with little lead time. However, a current challenge of AM is the lack of geometric accuracy of fabricated parts. To improve the geometric accuracy of 3D printed parts, this paper presents a three-dimensional geometric compensation method that allows for eliminating systematic deviations by morphing the original surface mesh model of the part by the inverse of the systematic deviations. These systematic deviations are measured by 3D scanning multiple sacrificial printed parts and computing an average deviation vector field throughout the model. We demonstrate the necessity to filter out the random deviations from the measurement data used for compensation. Case studies demonstrate that printing the compensated mesh model based on the average deviation of five sacrificial parts produces a part with deviations about three times smaller than measured on the uncompensated parts. The deviation values of this compensated part based on the average deviation vector field are less than half of the deviation values of the compensated part based on only one sacrificial part

Efficient planning of peen-forming patterns via artificial neural networks

Robust automation of the shot peen forming process demands a closed-loop feedback in which a suitable treatment pattern needs to be found in real-time for each treatment iteration. In this work, we present a method for finding the peen-forming patterns, based on a neural network (NN), which learns the nonlinear function that relates a given target shape (input) to its optimal peening pattern (output), from data generated by finite element simulations. The trained NN yields patterns with an average binary accuracy of 98.8\% with respect to the ground truth in microseconds

Evaluation of the Metrological Performance of a Handheld 3D Laser Scanner Using a Pseudo-3D Ball-Lattice Artifact

RÉSUMÉ: This paper proposes the use of a pseudo-3D ball-lattice artifact to characterize a handheld laser scanner from a metrological standpoint. The artifact allows the computation of local and global errors in measurement by using the reference-frame-independent parameters of size, form, and distance within the measuring volume of the scanner, and in a single point cloud, without the need for registration. A set of tests was performed using the whole measuring volume, and three acquisition parameters, namely the orientation of the sweeps during the scans, the exposure time, and the distance to the scanner were analyzed for their effects on the accuracy of the scan data. A composite error including the errors in measuring size, form, and distance was used as a single figure of merit to characterize the performance of the scanner in relation to the data-acquisition parameters. The orientation of sweeps did not have a considerable effect on the errors. The accuracy of the scan data was strongly affected by exposure time and its interaction with the distance at which the artifact was scanned. The errors followed a quadratic trend with respect to the distance of the artifact to the scanner. The tested scanner performed best at its manufacturer’s recommended stand-off distance

Vibration-based piezoelectric energy harvesting system for rotary motion applications

The modeling and design of a piezoelectric flexible beam that can be used as an energy scavenger for rotary motion applications is investigated in this thesis. The energy harvester consists of a piezoelectric cantilever beam with a tip mass mounted on a rotating hub. Dynamic model of the harvester is derived using Euler-Bernoulli beam equation and considering the effect of the piezoelectric transducer. Equations of motion are derived using Lagrangian approach followed by relationships describing the harvested power. In particular, expressions describing the optimum load resistance and the maximum power that can be harvested using the proposed system are derived. Effect of parameters on the harvester dynamics is analyzed. Fabrication of the prototype is presented further. The model is verified for resonance and off-resonance operation comparing to the experimental results. It is shown that by proper parameter selection the output power of the harvester is sufficient to power wireless sensors

Section-specific geometric error evaluation of airfoil blades based on digitized surface data

Manufactured aero-engine blades are normally inspected in sections. Given discrete section-specific data points, the related geometric error evaluation task for three-dimensional tolerances of the blades is challenging and not yet well studied by researchers. Particularly, the existing method shows limited effectiveness in detecting position error and difficulty in accurate estimation of orientation error of airfoil sections. Moreover, touch-probes on a coordinate measuring machine are traditionally used to collect sectional coordinate data, which is a lengthy process as the data is collected through probe contact with the blade surface. Blade manufacturers would rather use 3D laser scanning that can complete data acquisition much faster. However, this poses a new challenge to data analysis. The collected set of points, referred to as point cloud, is all over the surface rather than at the desired, pre-specified sections. Thus, generating reliable section-specific data from the massive, unorganized scanned data points remains a problem to be solved. This thesis first presents a new methodology for evaluating three-dimensional tolerances of airfoil sections based on reconstructing the airfoil profiles from section-specific data points. According to a given measurement uncertainty, a progressive curve fitting scheme is proposed to generate the airfoil profile that meets the uncertainty constraint. Subsequently, the profile is utilized in related feature extraction of the proposed error evaluation approach. The second part of the thesis focuses on generating the reliable section-specific data points from the complete surface scan. An adaptive surface projection of data points onto the pre-specified section plane is proposed. A localized surface-fitting scheme is devised for this purpose. The main challenge lies in the selection of local data points, referred to as local neighborhood, for surface fitting. In particular, with the non-uniform distribution of data points in a noisy point cloud, existing neighborhood selection methods lead to biased fitting results. To avoid bias, a method of establishing balanced local neighborhood for surface fitting is proposed. An automated technique is also presented for systematic identification of eligible points for projection. The proposed computational framework in this thesis enables fully automatic and accurate evaluation of geometric errors using the latest high-speed geometric inspection platform.Applied Science, Faculty ofMechanical Engineering, Department ofGraduat

Scan-to-CAD alignment of damaged airfoil blade point clouds through geometric dissimilarity assessment

ABSTRACT: This paper presents a method for accurate alignment of the scanned point clouds of damaged blades with their nominal CAD model, which is an essential task in automated inspection for remanufacturing. The geometric dissimilarity of the underlying surface of the local neighborhoods of each measured data point and its nearest corresponding point on the CAD model is evaluated using a metric combining the average curvature Hausdorff distance and average Euclidean Hausdorff distance. The algorithm eliminates unreliable pairs with high geometric dissimilarity values in damaged regions from the matching process. The effectiveness of the proposed method is verified by experimental tests