Shape Measurement Data Research Articles

Shape Deviation Generator--A Convolution Framework for Learning and Predicting 3-D Printing Shape Accuracy

The 3-D shape accuracy is a critical performance measure for products built via additive manufacturing (AM). With advances in computing and increased accessibility of AM product data, machine learning for AM (ML4AM) has become a viable strategy for enhancing 3-D printing performance. A proper description of the 3-D shape formation through the layer-by-layer fabrication process is critical to ML4AM, such as feature selection and AM process modeling. The physics-based modeling and simulation approaches present voxel-level description of an object formation from points to lines, lines to surfaces, and surfaces to 3-D shapes. However, this computationally intensive modeling framework does not provide a clear structure for machine learning of AM data. Significant progress has been made to model and predict the shape accuracy of planar objects under data analytical frameworks. In order to predict, learn, and compensate for 3-D shape deviations using shape measurement data, we propose a shape deviation generator (SDG) under a novel convolution formulation to facilitate the learning and prediction of 3-D printing accuracy. The shape deviation representation, individual layer input function, and layer-to-layer transfer function for the convolution modeling framework are proposed and derived. A deconvolution problem for identifying the transfer function is formulated to capture the interlayer interaction and error accumulation effects in the layer-by-layer fabrication processes. Physics-informed sequential model estimation is developed to fully establish the SDG models. The Gaussian process regression is adopted to capture spatial correlations. The printed 2-D and 3-D shapes via a stereolithography (SLA) process are used to demonstrate the proposed modeling framework and derive new process insights for AM processes. Note to Practitioners —With advances in computing and increased availability of product data, machine learning for additive manufacturing (ML4AM) has become a viable strategy for enhancing 3-D printing accuracy. This work establishes the shape deviation generator (SDG) as a novel data analytical framework through a convolution formulation to model the 3-D shape formation in the AM process. This new engineering-informed machine-learning framework will facilitate the learning of AM data to establish models for geometric shape accuracy prediction and control.

Matching Accuracy Verification of Three Dimensional Shape Measurement Data using Concave Target Markers

Matching Accuracy Verification of Three Dimensional Shape Measurement Data using Concave Target Markers

Shoe-last design innovation for better shoe fitting

Shoe-last, a 3D mould used for making footwear, influence the shape, size and fitting of footwear. Current shoe-last design software has focused mainly on reverse engineering of existing shoe-last and modification. Shoe-last designers have generally preferred to design the shoe-last manually due to limitations of design software. In order to solve these problems, a new software based on CATIA platform was developed. The shoe-last model is based on foot shape measurement data and foot biomechanics. Using the existing shoe-last design standards and the sections from existing shoe-lasts, design tables and relationship equations enables the design of shoe-last with different toe type, heel height and custom shoe-last. The design includes comfort and fit aspects as well as design aspect, therefore enables design of aesthetical comfortable shoes. Since the design can be modified instantaneously, the designers could visualize design changes leading to a reduction in shoe-last design cycle.

Three-dimensional visualization of shape measurement data based on a computer generated hologram

In this paper, we present a technique to reconstruct a 3D object by its measurement data using a computer generated hologram (CGH). First, both the range image and the intensity image of a 3D object are acquired simultaneously using a 3D shape measurement system. Then, the Fresnel CGH is designed and fabricated using these data. Finally, by combining CGH with optical holography, an image hologram is recorded. This technique resolves how to obtain a 3D image in a CGH as well as how to reproduce the measurements of object images in three-dimensional object shapes. In this paper the principle behind the proposed method, the technology for designing and fabricating the CGH and optical hologram, and the experimental results are given.

Neutron-Induced Fission of 233U, 238U, 232Th, 239Pu, 237Np, natPb and 209Bi Relative to 235U in the Energy Range 1-200 MeV

Fission cross section ratios of 233U, 238U, 232Th, 239Pu, 237Np, natural Pb and 209Bi to 235U have been measured in a wide energy range of incident neutrons from 1 MeV to 200 MeV using a time-of-flight technique at the neutron spectrometer GNEIS based on the 1-GeV proton synchrocyclotron of PNPI. For actinide targets, the threshold cross section method and evaluated data below 14 MeV were used for normalization of the shape measurement data, while the evaluated and recommended fission cross sections of 235U were used to convert the ratio data to absolute fission cross sections. For Pb and Bi targets, an absolute normalization of the measured cross section ratios has been done using the thickness of the targets and detection efficiencies.

Reverberation rejection with a dual-line towed array

Low-frequency bottom reverberation from explosive sources was measured with a dual-line horizontal towed array during a 1991 system engineering trial. The receiver's parallel line arrays were processed coherently in order to unambiguously resolve the reverberation field in azimuth. Initial beamforming that assumed a straight-and-parallel array configuration led to poor or erroneous ambiguity resolution, suggesting the system was deformed significantly from the assumed shape. In the absence of reliable shape measurement data, the array shape was deduced acoustically using tow ship signals and the direct blast of an explosive source. The estimated shape was then used in a shape-compensated beamformer to produce azimuthally unambiguous estimates of the reverberation field. Backlobe rejection of more than 20 dB was attained for both direct blast and distant reverberation energy.

Reference algorithms for the approximation of shape measurement data using non-orthogonal periodic functions

The evaluation of measurement instruments and processes by comparing them them to references has become a basic principle of metrology. In our paper this concept, which originally had been developed for the physical measurement process, is applied to measurement data processing. In this way the evaluation of several algorithms is possible where a mathematical proof fails or does not seem to be as adequate. We present the principle of a special reference algorithm supporting the quality estimation of an approximation method for shape measurement data. This approximation method, which uses non-orthogonal sine fuctions, runs successfully but has some theoretical deficiencies.