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Research Papers

In Situ Additive Manufacturing Process Monitoring With an Acoustic Technique: Clustering Performance Evaluation Using K-Means Algorithm

[+] Author and Article Information
Hossein Taheri

Mem. ASME
Department of Manufacturing Engineering,
Georgia Southern University,
1100 IT Dr., #3130,
Statesboro, GA 30458;
Center for Nondestructive Evaluation (CNDE),
Iowa State University,
1915 Scholl Rd., 115 ASC II,
Ames, IA 50011
e-mail: htaheri@georgiasouthern.edu

Lucas W. Koester

Center for Nondestructive Evaluation (CNDE),
Iowa State University,
1915 Scholl Rd., 115 ASC II,
Ames, IA 50011
e-mail: lkoester@iastate.edu

Timothy A. Bigelow

Center for Nondestructive Evaluation (CNDE),
Iowa State University,
1915 Scholl Rd., 115 ASC II,
Ames, IA 50011
e-mail: bigelow@iastate.edu

Eric J. Faierson

Quad City Manufacturing Lab (QCML)-Western Illinois University (WIU),
1322 Gillespie St., Suite 102,
Rock Island, IL 61201
e-mail: efaierson@qcml.org

Leonard J. Bond

Mem. ASME
Center for Nondestructive Evaluation (CNDE),
Iowa State University,
1915 Scholl Rd., 115 ASC II,
Ames, IA 50011
e-mail: bondlj@iastate.edu

1Corresponding author.

Manuscript received March 3, 2018; final manuscript received January 27, 2019; published online February 28, 2019. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 141(4), 041011 (Feb 28, 2019) (10 pages) Paper No: MANU-18-1132; doi: 10.1115/1.4042786 History: Received March 03, 2018; Accepted January 28, 2019

Additive manufacturing (AM) is based on layer-by-layer addition of materials. It gives design flexibility and potential to decrease costs and manufacturing lead time. Because the AM process involves incremental deposition of materials, it provides unique opportunities to investigate the material quality as it is deposited. Development of in situ monitoring methodologies is a vital part of the assessment of process performance and understanding of defects formation. In situ process monitoring provides the capability for early detection of process faults and defects. Due to the sensitivity of AM processes to different factors such as laser and material properties, any changes in aspects of the process can potentially have an impact on the part quality. As a result, in-process monitoring of AM is crucial to assure the quality, integrity, and safety of AM parts. There are various sensors and techniques that have been used for in situ process monitoring. In this work, acoustic signatures were used for in situ monitoring of the metal direct energy deposition (DED) AM process operating under different process conditions. Correlations were demonstrated between metrics and various process conditions. Demonstrated correlation between the acoustic signatures and the manufacturing process conditions shows the capability of acoustic technique for in situ monitoring of the additive manufacturing process. To identify the different process conditions, a new approach of K-means statistical clustering algorithm is used for the classification of different process conditions, and quantitative evaluation of the classification performance in terms of cohesion and isolation of the clusters. The identified acoustic signatures, quantitative clustering approach, and the achieved classification efficiency demonstrate potential for use in in situ acoustic monitoring and quality control for the additive manufacturing process.

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References

Leino, M., Pekkarinen, J., and Soukka, R., 2016, “The Role of Laser Additive Manufacturing Methods of Metals in Repair, Refurbishment and Remanufacturing—Enabling Circular Economy,” Phys. Procedia, 83, pp. 752–760. [CrossRef]
Taheri, H., Shoaib, M. R. M., Koester, L. W., Bigelow, T. A., Collins, P. C., and Bond, L. J., 2017, “Powder Based Additive Manufacturing—A Review of Types of Defects, Generation Mechanisms, Detection, Property Evaluation and Metrology,” Int. J. Addit. Subtractive Mater. Manuf., 1(2), pp. 172–209.
Mehrabi, A. B., and Farhangdoust, S., 2018, “A Laser-Based Non-Contact Vibration Technique for Health Monitoring of Structural Cables; Background, Success, and New Developments,” Adv. Acoust. Vib., 2018, pp. 1–13.
Tashakori, S., Baghalian, A., Senyurek, V. Y., Farhangdoust, S., Mcdaniel, D., and Tansel, I. N., 2018, “Composites Bond Inspection Using Heterodyne Effect and SuRE Methods,” Shock Vib., 2018, pp. 1–7. [CrossRef]
Taheri, H., 2017, “Classification of Nondestructive Inspection Techniques With Principal Component Analysis (PCA) for Aerospace Application,” ASNT 26th Research Symposium, Jacksonville, FL, Mar, 13–16, pp. 219–227.
Koester, L., Taheri, H., Bond, L. J., Barnard, D., and Gray, J., 2016, “Additive Manufacturing Metrology: State of the Art and Needs Assessment,” AIP Conf. Proc. 1706, 130001.
Hirsch, M., Patel, R., Li, W., Guan, G., Leach, R. K., Sharples, S. D., and Clare, A. T., 2017, “Assessing the Capability of In-Situ Nondestructive Analysis During Layer Based Additive Manufacture,” Addit. Manuf., 13, pp. 135–142. [CrossRef]
Grasso, M., Laguzza, V., Semeraro, Q., and Colosimo, B. M., 2016, “In-Process Monitoring of Selective Laser Melting: Spatial Detection of Defects Via Image Data Analysis,” ASME J. Manuf. Sci. Eng., 139(5), pp. 51001–51016. [CrossRef]
Dunsky, C., 2014, “Process Monitoring in Laser Additive Manufacturing,” Industrial Laser Solutions, December.
Delgado, J., Ciurana, J., and Rodríguez, C. A., 2012, “Influence of Process Parameters on Part Quality and Mechanical Properties for DMLS and SLM With Iron-Based Materials,” Int. J. Adv. Manuf. Technol., 60(5), pp. 601–610. [CrossRef]
Gong, H., Rafi, K., Gu, H., Janaki Ram, G. D., Starr, T., and Stucker, B., 2015, “Influence of Defects on Mechanical Properties of Ti-6Al-4V Components Produced by Selective Laser Melting and Electron Beam Melting,” Mater. Des., 86, pp. 545–554. [CrossRef]
Everton, S. K., Hirsch, M., Stravroulakis, P., Leach, R. K., and Clare, A. T., 2016, “Review of In-Situ Process Monitoring and In-Situ Metrology for Metal Additive Manufacturing,” Mater. Des., 95, pp. 431–445. [CrossRef]
Chua, Z. Y., Ahn, I. H., and Moon, S. K., 2017, “Process Monitoring and Inspection Systems in Metal Additive Manufacturing: Status and Applications,” Int. J. Precis. Eng. Manuf.-Green Technol., 4(2), pp. 235–245. [CrossRef]
Grasso, M., and Colosimo, B. M., 2017, “Process Defects and In Situ Monitoring Methods in Metal Powder Bed Fusion: A Review,” Meas. Sci. Technol., 28(4), 44005. [CrossRef]
Clijsters, S., Craeghs, T., Buls, S., Kempen, K., and Kruth, J. P., 2014, “In Situ Quality Control of the Selective Laser Melting Process Using a High-Speed, Real-Time Melt Pool Monitoring System,” Int. J. Adv. Manuf. Technol., 75(5–8), pp. 1089–1101. [CrossRef]
Craeghs, T., Clijsters, S., Yasa, E., Bechmann, F., Berumen, S., and Kruth, J. P., 2011, “Determination of Geometrical Factors in Layerwise Laser Melting using Optical Process Monitoring,” Opt. Lasers Eng., 49(12), pp. 1440–1446. [CrossRef]
Imani, F., Gaikwad, A., Montazeri, M., Rao, P., Yang, H., and Reutzel, E., 2018, “Process Mapping and In-Process Monitoring of Porosity in Laser Powder Bed Fusion Using Layerwise Optical Imaging,” ASME J. Manuf. Sci. Eng., 140(10), pp. 101009–101014. [CrossRef]
Krauss, H., Eschey, C., and Zaeh, M. F., 2012, “Thermography for Monitoring the Selective Laser Melting Process,” 23rd Annual International Solid Freeform Fabrication Symposium—An Additive Manufacturing Conference, Aug. 6–8, University of Texas, TX, pp. 999–1014.
Zhao, C., Fezzaa, K., Cunningham, R. W., Wen, H., De Carlo, F., Chen, L., Rollett, A. D., and Sun, T., 2017, “Real-Time Monitoring of Laser Powder Bed Fusion Process Using High-Speed X-Ray Imaging and Diffraction,” Sci. Rep., 7(1), 3602. [CrossRef] [PubMed]
Rieder, H., Dillhöfer, A., Spies, M., Bamberg, J., and Hess, T., 2014, “Online Monitoring of Additive Manufacturing Processes Using Ultrasound,” Proceedings of the 11th European Conference on Non-Destructive Testing, Prague, Czech Republic, Oct. 6–10, Vol. 1, pp. 2194–2201.
Gaja, H., and Liou, F., 2016, “Defects Monitoring of Laser Metal Deposition Using Acoustic Emission Sensor,” Int. J. Adv. Manuf. Technol., 90(1–4), pp. 561–574.
Addison, R. C., McKie, A. D. W., Liao, T.-L. T., and Ryang, H.-S., 1992, “In Situ Process Monitoring Using Laser-Based Ultrasound,” IEEE Ultrasonics Symposium Proceedings, Tucson, AZ, Oct. 20–23, IEEE, New York, pp. 783–786.
Thompson, A., Maskery, I., and Leach, R. K., 2016, “X-Ray Computed Tomography for Additive Manufacturing: A Review,” Meas. Sci. Technol., 27(7), 72001. [CrossRef]
Wu, H., Yu, Z., and Wang, Y., 2017, “Real-Time FDM Machine Condition Monitoring and Diagnosis based on Acoustic Emission and Hidden Semi-Markov Model,” Int. J. Adv. Manuf. Technol., 90(5), pp. 2027–2036. [CrossRef]
Rogers, L. M., 1979, “The Application of Vibration Signature Analysis and Acoustic Emission Source Location to On-Line Condition Monitoring of Anti-Friction Bearings,” Tribol. Int., 12, pp. 51–59. [CrossRef]
Nikravesh, S. M. Y., Taheri, H., and Wagstaff, P., 2013, “Identification of Appropriate Wavelet for Vibration Study of Mechanical Impacts,” ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) Volume 14: Vibration, Acoustics and Wave Propagation, San Diego, CA, United States, V014T15A022.
Yadavar Nikravesh, S. M., and Taheri, H., 2018, “Onset of Nucleate Boiling Detection in a Boiler Tube by Wavelet Transformation of Vibration Signals,” J. Nondestruct. Eval. Diagnostics Progn. Eng. Syst., 1(3), pp. 31005–31007. [CrossRef]
Grondel, S., Delebarre, C., Assaad, J., Dupuis, J. P., and Reithler, L., 2002, “Fatigue Crack Monitoring of Riveted Aluminium Strap Joints by Lamb Wave Analysis and Acoustic Emission Measurement Techniques,” NDT E Int., 35(3), pp. 137–146. [CrossRef]
Behrens, B.-A., Santangelo, A., and Buse, C., 2013, “Acoustic Emission Technique for Online Monitoring During Cold Forging of Steel Components: A Promising Approach for Online Crack Detection in Metal Forming Processes,” Prod. Eng., 7(4), pp. 423–432. [CrossRef]
Arul, S., Vijayaraghavan, L., and Malhotra, S. K., 2007, “Online Monitoring of Acoustic Emission for Quality Control in Drilling of Polymeric Composites,” J. Mater. Process. Technol., 185(1–3), pp. 184–190. [CrossRef]
Montazeri, M., Yavari, R., Rao, P., and Boulware, P., 2018, “In-Process Monitoring of Material Cross-Contamination Defects in Laser Powder Bed Fusion,” ASME J. Manuf. Sci. Eng., 140(11), pp. 111001–111019. [CrossRef]
Coates, P. D., Barnes, S. E., Sibley, M. G., Brown, E. C., Edwards, H. G. M., and Scowen, I. J., 2003, “In-Process Vibrational Spectroscopy and Ultrasound Measurements in Polymer Melt Extrusion,” Polymer (Guildf)., 44(19), pp. 5937–5949. [CrossRef]
NIST, 2013, “NIST-Report on Measurement Science Roadmap for Metal Based Additive Manufacturing”.
Landau, S., and Chis Ster, I., 2010, “Cluster Analysis: Overview,” Int. Encycl. Educ., pp. 72–83.
Malekipour, E., and El-Mounayri, H., 2018, “Defects, Process Parameters and Signatures for Online Monitoring and Control in Powder-Based Additive Manufacturing,” Conference Proceedings of the Society for Experimental Mechanics Series, Springer, New York, pp. 83–90.
Koester, L. W., Taheri, H., Bigelow, T. A., Bond, L. J., and Faierson, E. J., 2018, “In-Situ Acoustic Signature Monitoring in Additive Manufacturing Processes,” AIP Conf. Proc., 1949, 020006.
Kozhisseri, S., and Bikdash, M., 2009, “Spectral Features for the Classification of Civilian Vehicles using Acoustic Sensors,” IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, Nashville, TN, IEEE, New York, pp. 93–100.
Ludeña-Choez, J., and Gallardo-Antolín, A., 2016, “Acoustic Event Classification Using Spectral Band Selection and Non-Negative Matrix Factorization-based Features,” Expert Syst. Appl., 46(Suppl. C), pp. 77–86. [CrossRef]
Proakis, J. G., and Manolakis, D. G., 1996, Digital Signal Processing: Principles, Algorithms, and Applications, 3rd ed., Prentice-Hall, Englewood Cliffs, NJ.
Ulrych, T., 1971, “Application of Homomorphic Deconvolution to Seismology,” Geophysics, 36(4), pp. 650–660. [CrossRef]
Merla, C., Paffi, A., Apollonio, F., Orcioni, S., and Liberti, M., 2017, “Portable System for Practical Permittivity Measurements Improved by Homomorphic Deconvolution,” IEEE Trans. Instrum. Meas., 66(3), pp. 514–521. [CrossRef]
Oppenheim, A. V., and Schafer, R. W., 1989, Discrete Time Signal Processing, Prentice-Hall, London.
Jung, Y. G., Kang, M. S., and Heo, J., 2014, “Clustering Performance Comparison using K-means and Expectation Maximization Algorithms,” Biotechnol. Biotechnol. Equip., 28(suppl. 1), pp. S44–S48. [CrossRef] [PubMed]
Koester, L. W., Taheri, H., Bigelow, T. A., Collins, P. C., and Bond, L. J., 2018, “Nondestructive Testing for Metal Parts Fabricated using Powder-Based Additive Manufacturing,” Mater. Eval., 76(4), pp. 514–524.
Zanon, M., Susto, G. A., and McLoone, S., 2014, “Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach,” IFAC Proc., 19, pp. 1947–1952. [CrossRef]
Guo, H., Paynabar, K., and Jin, J., 2012, “Multiscale Monitoring of Autocorrelated Processes Using Wavelets Analysis,” IIE Trans. (Institute Ind. Eng.), 44(4), pp. 312–326.
Barga, R. S., Friesel, M. A., Melton, R. B., Friesel, M. A., and Melton, R. B., 1990, “Classification of Acoustic Emission Waveforms for Nondestructive Evaluation using Neural Networks,” SPIE International Symposium on Optical Engineering and Photonics in Aerospace Sensing in the Applications of Artificial Neural Networks, Orlando, FL, United States, Vol. 1294, pp. 545–556.
Alexander, F. J., and Lookman, T., 2013, “Novel Approaches to Statistical Learning in Materials Science,” Informatics for Materials Science and Engineering, K. Rajan, ed., Butterworth-Heinemann, Elsevier, London, pp. 37–51.

Figures

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Fig. 1

Monitoring fixture: (a) upper and lower adapter, build plate, and mounting posts and (b) attachment of the mounting plate and sensors to the upper adapter plate [36]

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Fig. 2

Direct energy deposition (DED) system: (a) measurement system placed in the machine and (b) deposition of the materials (powder) occurring

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Fig. 3

Configuration of 5 × 5 deposition locations array

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Fig. 4

Schematic representation of DED processes at different process conditions; (a) baseline: BL, (b) control: CO, (c) normal: C1, (d) low power: C2, and (e) low powder: C3

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Fig. 5

An example of (a) the original acoustic signal and (b) frequency bands segments

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Fig. 6

K-mean clustering algorithm [34]

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Fig. 7

An example of application of the Cf and CA acoustic spectral features for data clustering

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Fig. 8

Flowchart for the steps of algorithm for evaluation of the clustering method

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Fig. 9

A schematic representation and example of silhouette mean values of the clusters and the Euclidean distance of a data point from all the silhouette mean values

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Fig. 10

An example of 2D graphical representation of AM process condition classification using three spectral features (Cf and CA) of acoustic signals: (a) representing all data points and the silhouette means and (b) silhouette means of each cluster

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Fig. 11

An example of 3D graphical representation of AM process condition classification using three spectral features (Cf, CA, and PA) of acoustic signals: (a) representing all data points and the silhouette means and (b) silhouette means of each cluster

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Fig. 12

Efficiency of clustering compared to the baseline condition (BL) in (a) low (LF) and (b) high (HF) frequency bands

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Fig. 13

Efficiency of clustering compared to the normal condition (C1) in (a) low (LF) and (b) high (HF) frequency bands

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