Accuracy analysis of a piece-to-piece reverse engineering workflow for a turbine foil based on multi-modal computed tomography and additive manufacturing

Bauer, Fabian; Schrapp, Michael; Szijarto, Janos

doi:10.1016/j.precisioneng.2019.07.008

Cited by 22 publications

(12 citation statements)

References 29 publications

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“…Computed tomography has been recognized as an indispensable technology not only in the healthcare domain but also with respect to industrial applications such as reverse engineering [ 1 , 2 ], flaw detection [ 3 ] and meteorology to name a few [ 4 , 5 ]. The non-destructive nature of CT scanning has also proven to be tremendously valuable in the case of geomaterials, allowing to elucidate transport phenomena in porous media; visualize deformation and strain localization in soils, rocks or sediments; or perform fracture and damage assessment in asphalt, cement and concrete [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Alsamadony

Yildirim

Glatz

et al. 2021

Sensors

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Deep neural networks have received considerable attention in clinical imaging, particularly with respect to the reduction of radiation risk. Lowering the radiation dose by reducing the photon flux inevitably results in the degradation of the scanned image quality. Thus, researchers have sought to exploit deep convolutional neural networks (DCNNs) to map low-quality, low-dose images to higher-dose, higher-quality images, thereby minimizing the associated radiation hazard. Conversely, computed tomography (CT) measurements of geomaterials are not limited by the radiation dose. In contrast to the human body, however, geomaterials may be comprised of high-density constituents causing increased attenuation of the X-rays. Consequently, higher-dose images are required to obtain an acceptable scan quality. The problem of prolonged acquisition times is particularly severe for micro-CT based scanning technologies. Depending on the sample size and exposure time settings, a single scan may require several hours to complete. This is of particular concern if phenomena with an exponential temperature dependency are to be elucidated. A process may happen too fast to be adequately captured by CT scanning. To address the aforementioned issues, we apply DCNNs to improve the quality of rock CT images and reduce exposure times by more than 60%, simultaneously. We highlight current results based on micro-CT derived datasets and apply transfer learning to improve DCNN results without increasing training time. The approach is applicable to any computed tomography technology. Furthermore, we contrast the performance of the DCNN trained by minimizing different loss functions such as mean squared error and structural similarity index.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Alsamadony

Yildirim

Glatz

et al. 2021

Sensors

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“…A GOM ATOS III Triple Scan + TRITOP digitising system was used to construct exact 3D models. The optical scanner is frequently employed in research studies as it guarantees high data quality with minimum noise [29,30]. It is based on advanced projection technology of structured blue light and has two 8 megapixel cameras, and this high-performance device can provide a typical point spacing of 0.01 mm.…”

Section: Steam Turbine Diaphragm Geometries and Analysis Toolsmentioning

confidence: 99%

Effect of Manufacturing Processes on Blade Throat Size and Position in a Steam Turbine Diaphragm

Eret¹,

Hoznedl²

2022

Journal of Machine Engineering

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Despite a sustainable energy future, steam turbines are requisite for the reliability and security of the electric power supply in many countries. Accurate and precise manufacturing of the steam path is crucial to turbine efficiency. Before entering the rotor blades, the steam must be correctly guided using stationary blading in a diaphragm. Steam turbine diaphragms are complicated components to manufacture, and welding is the most common fabrication method. A case study presented in this paper employs data from a 3D optical scanner for a geometric deviation analysis of the upper half of the diaphragm at two production steps, after complete welding and after final machining. Unrolled cylinder cross-sections at different diameters are used to evaluate the blade throat sizes and positions compared to the nominal geometry. The results indicate significant geometric changes between the two fabrication steps, and several suggestions are put forward for targeted future work.

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“…Modern optical scanners can construct exact 3D digital models [33,34]. A GOM ATOS III Triple Scan + TRITOP digitising system, which is an industry-standard system used by many leading manufacturing companies, is employed in the current study.…”

Section: Investigated Geometries and Analysis Toolsmentioning

confidence: 99%

Analysis of Geometric Errors of Throat Sizes of Last Stage Blades in a Mid-Size Steam Turbine

Eret¹,

Hoznedl²

2022

Journal of Machine Engineering

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Steam turbine technology with enhanced flexibility will continue to participate in electric power supply mixes. Last stage blades secure the reliability of a steam turbine and require high precision manufacturing and assembly. This case study presents a statistical analysis of geometric errors of the throat sizes of the last stage blades in a mid-size steam turbine. A 3D optical scanner is employed to capture detailed geometries of rotor blades and a half of assembled nozzle diaphragm. Unrolled cylinder cross-sections are used to evaluate 2D geometrical features such as blade throats and areas at three different diameters, and the results are compared to intended designs. In addition, linear correlations between the throat size and blade pitch, area and trailing edge thickness are established, and blade throat position shifts are quantified. Such a comprehensive study is presented for the first time, and some useful conclusions can be retrieved from this case study.

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Accuracy analysis of a piece-to-piece reverse engineering workflow for a turbine foil based on multi-modal computed tomography and additive manufacturing

Cited by 22 publications

References 29 publications

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography

Effect of Manufacturing Processes on Blade Throat Size and Position in a Steam Turbine Diaphragm

Analysis of Geometric Errors of Throat Sizes of Last Stage Blades in a Mid-Size Steam Turbine

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