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Research Papers

Traceable Porosity Measurements in Industrial Components Using X-Ray Computed Tomography

[+] Author and Article Information
Petr Hermanek

Department of Management and Engineering,
University of Padova,
Stradella San Nicola 3, 36100 Vicenza, Italy
e-mail: petr.hermanek@unipd.it

Filippo Zanini

Department of Management and Engineering,
University of Padova,
Stradella San Nicola 3, 36100 Vicenza, Italy
e-mail: filippo.zanini@unipd.it

Simone Carmignato

Department of Management and Engineering,
University of Padova,
Stradella San Nicola 3, 36100 Vicenza, Italy
e-mail: simone.carmignato@unipd.it

Manuscript received September 5, 2017; final manuscript received March 1, 2019; published online March 28, 2019. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 141(5), 051004 (Mar 28, 2019) (8 pages) Paper No: MANU-17-1554; doi: 10.1115/1.4043192 History: Received September 05, 2017; Accepted March 01, 2019

Manufacturing technologies deliver products that can suffer from various defects, one of which is internal porosity. Pores are present in most of the parts produced by, e.g., casting, additive manufacturing, and injection molding and can significantly affect the performance of the final products. Due to technological and economic limits, typically porosity cannot be completely removed by optimizing process parameters. It is therefore essential to have a measurement technique that can detect and evaluate these defects accurately. Apart from conventional nondestructive techniques, such as ultrasonic testing or Archimedes’ method that suffer from various limitations, X-ray computed tomography has emerged as a promising solution capable of measuring size, spatial distribution, and shape of pores. In this paper, a method to achieve traceable computed tomography measurements of internal porosity using a reference object with calibrated internal artificial defects is described and demonstrated on an industrial case study. Furthermore, the possibility to improve measurement results by optimizing parameters used for the evaluation of acquired data is discussed. The optimization method is based on an iterative procedure that reduces to ±5 × 10−5 mm3 the error of the measured values of total void content in the reference object.

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Figures

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Fig. 1

Computer-aided design (CAD) models with general dimensions: (a) test object and (b) reference standard. Dimensions are in millimeters. The ROI used for the data analysis (see Sec. 3.2) is displayed with a square on the right figure.

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Fig. 2

CT scanning configuration: (a) reference standard (bright) mounted together with the test object (black) and (b) reference standard alone

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Fig. 3

Example of grayscale image (a) and gray values histogram showing the background and material peaks and the ISO-50% value (b)

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Fig. 4

Example of average GVs related to background (10092), material (23605), and four pores of different sizes (17519, 14686, 14097, and 12920 ordered from the smallest to the largest pore) (a) and representation of a simulated pore imaged with four different pixel sizes (b)

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Fig. 5

Flowchart of the iterative procedure for the optimization of threshold value

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Fig. 6

Relative errors of volume measurements evaluated on the reference standard as the difference between average value from repeated scans (calculated for individual defects) and reference values. (a) Results obtained from the reference standard scanned together with the test object, (b) a close-up of (a) with reduced range on y-axis, (c) results obtained from the reference standard scanned alone, and (d) a close-up of (c) with reduced range on y-axis.

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Fig. 7

Total void content obtained by using different threshold values: (a) total void content in the test object (which is about 1% of the total test object volume) and (b) total void content in the reference standard. Error bars represent expanded measurement uncertainty.

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Fig. 8

Example of defect analysis performed on the test part scanned together with the reference standard. The colors of the two parts were intentionally set in the visualization software in order to reflect the real surface colors, i.e., the test part is black, whereas the reference standard is bright.

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