{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:03:45Z","timestamp":1772823825415,"version":"3.50.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T00:00:00Z","timestamp":1703289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt e. V. (DLR)"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Additive Manufacturing (AM) and in particular has gained significant attention due to its capability to produce complex geometries using various materials, resulting in cost and mass reduction per part. However, metal AM parts often contain internal defects inherent to the manufacturing process. Non-Destructive Testing (NDT), particularly Computed Tomography (CT), is commonly employed for defect analysis. Today adopted standard inspection techniques are costly and time-consuming, therefore an automatic approach is needed. This paper presents a novel eXplainable Artificial Intelligence (XAI) methodology for defect detection and characterization. To classify pixel data from CT images as pores or inclusions, the proposed method utilizes Support Vector Machine (SVM), a supervised machine learning algorithm, trained with an Area Under the Curve (AUC) of 0.94. Density-Based Spatial Clustering with the Application of Noise (DBSCAN) is subsequently applied to cluster the identified pixels into separate defects, and finally, a convex hull is employed to characterize the identified clusters based on their size and shape. The effectiveness of the methodology is evaluated on Ti6Al4V specimens, comparing the results obtained from manual inspection and the ML-based approach with the guidance of a domain expert. This work establishes a foundation for automated defect detection, highlighting the crucial role of XAI in ensuring trust in NDT, thereby offering new possibilities for the evaluation of AM components.<\/jats:p>","DOI":"10.1007\/s10845-023-02272-4","type":"journal-article","created":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T11:01:56Z","timestamp":1703329316000},"page":"957-974","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["eXplainable artificial intelligence for automatic defect detection in additively manufactured parts using CT scan analysis"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6261-2211","authenticated-orcid":false,"given":"Harsh","family":"Bordekar","sequence":"first","affiliation":[]},{"given":"Nicola","family":"Cersullo","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Brysch","sequence":"additional","affiliation":[]},{"given":"Jens","family":"Philipp","sequence":"additional","affiliation":[]},{"given":"Christian","family":"H\u00fchne","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,23]]},"reference":[{"key":"2272_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2019.102139","volume":"107","author":"D Bacioiu","year":"2019","unstructured":"Bacioiu, D., Melton, G., Papaelias, M., & Shaw, R. (2019). Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning. NDT & E International, 107, 102139. https:\/\/doi.org\/10.1016\/j.ndteint.2019.102139","journal-title":"NDT & E International"},{"key":"2272_CR2","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.ijfatigue.2016.06.020","volume":"94","author":"S Beretta","year":"2017","unstructured":"Beretta, S., & Romano, S. (2017). A comparison of fatigue strength sensitivity to defects for materials manufactured by AM or traditional processes. International Journal of Fatigue, 94, 178\u2013191. https:\/\/doi.org\/10.1016\/j.ijfatigue.2016.06.020","journal-title":"International Journal of Fatigue"},{"key":"2272_CR3","doi-asserted-by":"publisher","unstructured":"Bhagat, R. C., & Patil, S. S. (2015). Enhanced SMOTE algorithm for classification of imbalanced big-data using Random Forest. 2015 IEEE International Advance Computing Conference (IACC). https:\/\/doi.org\/10.1109\/iadcc.2015.7154739","DOI":"10.1109\/iadcc.2015.7154739"},{"issue":"2","key":"2272_CR4","doi-asserted-by":"publisher","first-page":"229","DOI":"10.2217\/iim.12.13","volume":"4","author":"FE Boas","year":"2012","unstructured":"Boas, F. E., & Fleischmann, D. (2012). CT artifacts: causes and reduction techniques. Imaging in Medicine, 4(2), 229\u2013240. https:\/\/doi.org\/10.2217\/iim.12.13","journal-title":"Imaging in Medicine"},{"key":"2272_CR5","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1613\/jair.1.12228","volume":"70","author":"N Burkart","year":"2021","unstructured":"Burkart, N., & Huber, M. F. (2021). A Survey on the Explainability of Supervised Machine Learning. Journal of Artificial Intelligence Research, 70, 245\u2013317. https:\/\/doi.org\/10.1613\/jair.1.12228","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"1","key":"2272_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s10614-020-10042-0","volume":"57","author":"N Bussmann","year":"2020","unstructured":"Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020). Explainable Machine Learning in Credit Risk Management. Computational Economics, 57(1), 203\u2013216. https:\/\/doi.org\/10.1007\/s10614-020-10042-0","journal-title":"Computational Economics"},{"issue":"1","key":"2272_CR7","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1016\/j.cirp.2019.03.021","volume":"68","author":"A Caggiano","year":"2019","unstructured":"Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., & Teti, R. (2019). Machine learning-based image processing for on-line defect recognition in additive manufacturing. CIRP Annals, 68(1), 451\u2013454. https:\/\/doi.org\/10.1016\/j.cirp.2019.03.021","journal-title":"CIRP Annals"},{"issue":"19","key":"2272_CR8","doi-asserted-by":"publisher","first-page":"6882","DOI":"10.3390\/ma15196882","volume":"15","author":"N Cersullo","year":"2022","unstructured":"Cersullo, N., Mardaras, J., Emile, P., Nickel, K., Holzinger, V., & H\u00fchne, C. (2022). Effect of Internal Defects on the Fatigue Behavior of Additive Manufactured Metal Components: A Comparison between Ti6Al4V and Inconel 718. Materials, 15(19), 6882. https:\/\/doi.org\/10.3390\/ma15196882","journal-title":"Materials"},{"key":"2272_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"2","key":"2272_CR10","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1016\/j.cirp.2014.05.011","volume":"63","author":"L De Chiffre","year":"2014","unstructured":"De Chiffre, L., Carmignato, S., Kruth, J.-P., Schmitt, R., & Weckenmann, A. (2014). Industrial applications of computed tomography. CIRP Annals, 63(2), 655\u2013677. https:\/\/doi.org\/10.1016\/j.cirp.2014.05.011","journal-title":"CIRP Annals"},{"key":"2272_CR11","doi-asserted-by":"publisher","unstructured":"Douglass, M. J. J. (2020). Book Review: Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow. In Aur\u00e9lien G\u00e9ron (ed). Physical and Engineering Sciences in Medicine, 2nd edition. 43(3), 1135-1136. https:\/\/doi.org\/10.1007\/s13246-020-00913-z","DOI":"10.1007\/s13246-020-00913-z"},{"key":"2272_CR12","doi-asserted-by":"publisher","unstructured":"du Plessis, A., le Roux, S. G., Booysen, G., & Els, J. (2016). Directionality of cavities and porosity formation in powder-bed laser additive manufacturing of metal components investigated using X-ray tomography. 3D Printing and Additive Manufacturing 3(1): 48\u201355. https:\/\/doi.org\/10.1089\/3dp.2015.0034","DOI":"10.1089\/3dp.2015.0034"},{"key":"2272_CR13","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1016\/j.mex.2018.09.005","volume":"5","author":"A du Plessis","year":"2018","unstructured":"du Plessis, A., Sperling, P., Beerlink, A., Tshabalala, L., Hoosain, S., Mathe, N., & le Roux, S. G. (2018). Standard method for microCT-based additive manufacturing quality control 1: Porosity analysis. MethodsX, 5, 1102\u20131110. https:\/\/doi.org\/10.1016\/j.mex.2018.09.005","journal-title":"MethodsX"},{"key":"2272_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2019.102144","volume":"107","author":"W Du","year":"2019","unstructured":"Du, W., Shen, H., Fu, J., Zhang, G., & He, Q. (2019). Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning. NDT & E International, 107, 102144. https:\/\/doi.org\/10.1016\/j.ndteint.2019.102144","journal-title":"NDT & E International"},{"key":"2272_CR15","doi-asserted-by":"publisher","unstructured":"Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014). Scalable Object Detection Using Deep Neural Networks. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https:\/\/doi.org\/10.1109\/cvpr.2014.276","DOI":"10.1109\/cvpr.2014.276"},{"issue":"34","key":"2272_CR16","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, 96(34), 226\u2013231.","journal-title":"In kdd"},{"issue":"8","key":"2272_CR17","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861\u2013874. https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recognition Letters"},{"key":"2272_CR18","doi-asserted-by":"crossref","unstructured":"Fuchs, P., Kr\u00f6ger, T., Dierig, T., Garbe, C. S. (2019). Generating Meaningful Synthetic Ground Truth for Pore Detection in Cast Aluminum Parts. E-Journal of Nondestructive Testing. https:\/\/doi.org\/10.58286\/23730","DOI":"10.58286\/23730"},{"key":"2272_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-56127-7","volume-title":"Additive manufacturing technologies","author":"I Gibson","year":"2021","unstructured":"Gibson, I., Rosen, D. W., Stucker, B., Khorasani, M., Rosen, D., Stucker, B., & Khorasani, M. (2021). Additive manufacturing technologies (Vol. 17). Cham, Switzerland: Springer. https:\/\/doi.org\/10.1007\/978-3-030-56127-7"},{"key":"2272_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2020.101460","volume":"36","author":"C Gobert","year":"2020","unstructured":"Gobert, C., Kudzal, A., Sietins, J., Mock, C., Sun, J., & McWilliams, B. (2020). Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning. Additive Manufacturing, 36, 101460. https:\/\/doi.org\/10.1016\/j.addma.2020.101460","journal-title":"Additive Manufacturing"},{"issue":"2","key":"2272_CR21","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/technologies7020044","volume":"7","author":"H Gong","year":"2019","unstructured":"Gong, H., Nadimpalli, V. K., Rafi, K., Starr, T., & Stucker, B. (2019). Micro-CT Evaluation of Defects in Ti-6Al-4V Parts Fabricated by Metal Additive Manufacturing. Technologies, 7(2), 44. https:\/\/doi.org\/10.3390\/technologies7020044","journal-title":"Technologies"},{"issue":"2017","key":"2272_CR22","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.ijfatigue.2016.05.001","volume":"94","author":"D Greitemeier","year":"2017","unstructured":"Greitemeier, D., Palm, F., Syassen, F., & Melz, T. (2017). Fatigue performance of additive manufactured TiAl6V4 using electron and laser beam melting. International Journal of Fatigue, 94(2017), 211\u2013217. https:\/\/doi.org\/10.1016\/j.ijfatigue.2016.05.001","journal-title":"International Journal of Fatigue"},{"key":"2272_CR23","unstructured":"https:\/\/www.bakerhughesds.com\/industrial-x-ray-ct-scanners\/phoenix-vtomex-s-micro-ct, note = Accessed: 2023-11-02"},{"key":"2272_CR24","doi-asserted-by":"publisher","unstructured":"Kasperovich, G., Haubrich, J., Gussone, J., & Requena, G. (2016). Correlation between porosity and processing parameters in TiAl6V4 produced by selective laser melting. Materials & Design. https:\/\/doi.org\/10.3390\/met8100830","DOI":"10.3390\/met8100830"},{"key":"2272_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-12-814062-8.00020-0","author":"LW Koester","year":"2019","unstructured":"Koester, L. W., Bond, L. J., Taheri, H., & Collins, P. C. (2019). Nondestructive evaluation of additively manufactured metallic parts. Additive Manufacturing for the Aerospace Industry. https:\/\/doi.org\/10.1016\/b978-0-12-814062-8.00020-0","journal-title":"Additive Manufacturing for the Aerospace Industry"},{"issue":"1","key":"2272_CR26","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2020","unstructured":"Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23(1), 18. https:\/\/doi.org\/10.3390\/e23010018","journal-title":"Entropy"},{"issue":"1","key":"2272_CR27","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/e23010018","volume":"23","author":"P Linardatos","year":"2020","unstructured":"Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy, 23(1), 18. https:\/\/doi.org\/10.3390\/e23010018","journal-title":"Entropy"},{"key":"2272_CR28","doi-asserted-by":"publisher","unstructured":"Liu, P., Zhou, D., & Wu, N. (2007). VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise. 2007 International Conference on Service Systems and Service Management. https:\/\/doi.org\/10.1109\/icsssm.2007.4280175","DOI":"10.1109\/icsssm.2007.4280175"},{"key":"2272_CR29","doi-asserted-by":"publisher","unstructured":"Liu, P., Zhou, D., & Wu, N. (2007). VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise. 2007 International Conference on Service Systems and Service Management. https:\/\/doi.org\/10.1109\/icsssm.2007.4280175","DOI":"10.1109\/icsssm.2007.4280175"},{"key":"2272_CR30","unstructured":"Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30."},{"issue":"1","key":"2272_CR31","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56\u201367. https:\/\/doi.org\/10.1038\/s42256-019-0138-9","journal-title":"Nature Machine Intelligence"},{"key":"2272_CR32","unstructured":"Montero, R. S., & Bribiesca, E. (2009). State of the art of compactness and circularity measures. In International mathematical forum (Vol. 4, No. 27, pp. 1305-1335)."},{"key":"2272_CR33","unstructured":"Mutiargo, B., Pavlovic, M., Malcolm, A. A., Goh, B., Krishnan, M., Shota, T., and Putro, M. I. S. (2019). Evaluation of X-ray Computed Tomography (CT) images of additively manufactured components using deep learning. In Proceedings of the 3rd Singapore International Non-Destructive Testing Conference and Exhibition (SINCE2019), Singapore (p. 9)."},{"key":"2272_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-0958-5-8","author":"F Nelli","year":"2015","unstructured":"Nelli, F. (2015). Machine Learning with scikit-learn. Python Data Analytics. https:\/\/doi.org\/10.1007\/978-1-4842-0958-5-8","journal-title":"Python Data Analytics"},{"issue":"1","key":"2272_CR35","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/tsmc.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62\u201366. https:\/\/doi.org\/10.1109\/tsmc.1979.4310076","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"2272_CR36","doi-asserted-by":"publisher","unstructured":"Ribeiro, M., Singh, S., & Guestrin, C. (2016). Why Should I Trust You?: Explaining the Predictions of Any Classifier. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations. https:\/\/doi.org\/10.18653\/v1\/n16-3020","DOI":"10.18653\/v1\/n16-3020"},{"key":"2272_CR37","volume-title":"Classification of medical images based on deep learning network (CNN) for both brain tumors and covid-19","author":"TM Sadoon","year":"2021","unstructured":"Sadoon, T. M. (2021). Classification of medical images based on deep learning network (CNN) for both brain tumors and covid-19. Diss: Ministry of Higher Education."},{"key":"2272_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6-1","author":"W Samek","year":"2019","unstructured":"Samek, W., & M\u00fcller, K.-R. (2019). Towards Explainable Artificial Intelligence. Lecture Notes in Computer Science. https:\/\/doi.org\/10.1007\/978-3-030-28954-6-1","journal-title":"Lecture Notes in Computer Science"},{"issue":"7","key":"2272_CR39","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1038\/nmeth.2019","volume":"9","author":"J Schindelin","year":"2012","unstructured":"Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676\u2013682. https:\/\/doi.org\/10.1038\/nmeth.2019","journal-title":"Nature Methods"},{"issue":"3","key":"2272_CR40","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1080\/10589759.2020.1785446","volume":"35","author":"M Schlotterbeck","year":"2020","unstructured":"Schlotterbeck, M., Schulte, L., Alkhaldi, W., Krenkel, M., Toeppe, E., Tschechne, S., & Wojek, C. (2020). Automated defect detection for fast evaluation of real inline CT scans. Nondestructive Testing and Evaluation, 35(3), 266\u2013275. https:\/\/doi.org\/10.1080\/10589759.2020.1785446","journal-title":"Nondestructive Testing and Evaluation"},{"key":"2272_CR41","doi-asserted-by":"publisher","unstructured":"Seeram, E., & Sil, J. (2013). Computed Tomography: Physical Principles, Instrumentation,and Quality Control. Practical SPECT\/CT in Nuclear Medicine.https:\/\/doi.org\/10.1007\/978-1-4471-4703-9-5","DOI":"10.1007\/978-1-4471-4703-9-5"},{"issue":"3","key":"2272_CR42","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s11837-017-2265-2","volume":"69","author":"M Seifi","year":"2017","unstructured":"Seifi, M., Gorelik, M., Waller, J., Hrabe, N., Shamsaei, N., Daniewicz, S., & Lewandowski, J. J. (2017). Progress Towards Metal Additive Manufacturing Standardization to Support Qualification and Certification. JOM, 69(3), 439\u2013455. https:\/\/doi.org\/10.1007\/s11837-017-2265-2","journal-title":"JOM"},{"issue":"3","key":"2272_CR43","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1007\/s11837-015-1810-0","volume":"68","author":"M Seifi","year":"2016","unstructured":"Seifi, M., Salem, A., Beuth, J., Harrysson, O., & Lewandowski, J. J. (2016). Overview of Materials Qualification Needs for Metal Additive Manufacturing. JOM, 68(3), 747\u2013764. https:\/\/doi.org\/10.1007\/s11837-015-1810-0","journal-title":"JOM"},{"key":"2272_CR44","doi-asserted-by":"publisher","unstructured":"Shi, R., Ngan, K. N., & Li, S. (2014). Jaccard index compensation for object segmentation evaluation. 2014 IEEE International Conference on Image Processing (ICIP). https:\/\/doi.org\/10.1109\/icip.2014.7025904","DOI":"10.1109\/icip.2014.7025904"},{"key":"2272_CR45","doi-asserted-by":"publisher","unstructured":"Shipway, N. J., Huthwaite, P., Lowe, M. J. S., & Barden, T. J. (2021). Using ResNets to perform automated defect detection for Fluorescent Penetrant Inspection. NDT & E International, 119, 102400. https:\/\/doi.org\/10.1016\/j.ndteint.2020.102400","DOI":"10.1016\/j.ndteint.2020.102400"},{"issue":"6","key":"2272_CR46","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/jimaging6060052","volume":"6","author":"A Singh","year":"2020","unstructured":"Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020). Explainable Deep Learning Models in Medical Image Analysis. Journal of Imaging, 6(6), 52. https:\/\/doi.org\/10.3390\/jimaging6060052","journal-title":"Journal of Imaging"},{"issue":"5","key":"2272_CR47","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1108\/13552541111156504","volume":"17","author":"AB Spierings","year":"2011","unstructured":"Spierings, A. B., Schneider, M., & Eggenberger, R. (2011). Comparison of density measurement techniques for additive manufactured metallic parts. Rapid Prototyping Journal, 17(5), 380\u2013386. https:\/\/doi.org\/10.1108\/13552541111156504","journal-title":"Rapid Prototyping Journal"},{"key":"2272_CR48","doi-asserted-by":"crossref","unstructured":"Syarif, I., Prugel-Bennett, A., Wills, G. (2016). SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance. TELKOMNIKA (Telecommunication Computing Electronics and Control), 14(4), 1502.https:\/\/doi.org\/10.12928\/telkomnika.v14i4.3956","DOI":"10.12928\/telkomnika.v14i4.3956"},{"key":"2272_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiomech.2021.105452","volume":"89","author":"W Teufl","year":"2021","unstructured":"Teufl, W., Taetz, B., Miezal, M., Dindorf, C., Fr\u00f6hlich, M., Trinler, U., Hogan, A., & Bleser, G. (2021). Automated detection and explainability of pathological gait patterns using a one-class support vector machine trained on inertial measurement unit based gait data. Clinical Biomechanics, 89, 105452. https:\/\/doi.org\/10.1016\/j.clinbiomech.2021.105452","journal-title":"Clinical Biomechanics"},{"issue":"7","key":"2272_CR50","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1007\/s11837-021-04706-x","volume":"73","author":"H Torbati-Sarraf","year":"2021","unstructured":"Torbati-Sarraf, H., Niverty, S., Singh, R., Barboza, D., De Andrade, V., Turaga, P., & Chawla, N. (2021). Machine-Learning-based Algorithms for Automated Image Segmentation Techniques of Transmission X-ray Microscopy (TXM). JOM, 73(7), 2173\u20132184. https:\/\/doi.org\/10.1007\/s11837-021-04706-x","journal-title":"JOM"},{"key":"2272_CR51","doi-asserted-by":"publisher","unstructured":"Torens, C., Durak, U., & Dauer, J. C. (2022). Guidelines and Regulatory Framework for Machine Learning in Aviation. AIAA SCITECH 2022 Forum. https:\/\/doi.org\/10.2514\/6.2022-1132","DOI":"10.2514\/6.2022-1132"},{"key":"2272_CR52","doi-asserted-by":"publisher","unstructured":"Vargas-Lopez, O., Perez-Ramirez, C. A., Valtierra-Rodriguez, M., Yanez-Borjas, J. J., & Amezquita-Sanchez, J. P. (2021). An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals. Sensors, 21(9), 3155. https:\/\/doi.org\/10.3390\/s21093155","DOI":"10.3390\/s21093155"},{"key":"2272_CR53","doi-asserted-by":"publisher","unstructured":"Wong, V. W. H., Ferguson, M., Law, K. H., Lee, Y.-T. T., & Witherell, P. (2021). Segmentation of Additive Manufacturing Defects Using U-Net. Volume 2: 41st Computers and Information in Engineering Conference (CIE). https:\/\/doi.org\/10.1115\/detc2021-68885","DOI":"10.1115\/detc2021-68885"},{"key":"2272_CR54","doi-asserted-by":"publisher","DOI":"10.17485\/ijst\/2015\/v8i14\/68808","author":"A Yadollahi","year":"2017","unstructured":"Yadollahi, A., & Shamsaei, N. (2017). Additive manufacturing of fatigue resistant materials: Challenges and opportunities. International Journal of Fatigue. https:\/\/doi.org\/10.17485\/ijst\/2015\/v8i14\/68808","journal-title":"International Journal of Fatigue"},{"key":"2272_CR55","doi-asserted-by":"publisher","unstructured":"Yap, C. Y., Chua, C. K., Dong, Z. L., Liu, Z. H., Zhang, D. Q., Loh, L. E., & Sing, S. L. (2015). Review of selective laser melting: Materials and applications. Applied physics reviews, 2(4), 041101. https:\/\/doi.org\/10.1063\/1.4935926","DOI":"10.1063\/1.4935926"},{"key":"2272_CR56","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.ndteint.2018.09.010","volume":"101","author":"P Zhu","year":"2019","unstructured":"Zhu, P., Cheng, Y., Banerjee, P., Tamburrino, A., & Deng, Y. (2019). A novel machine learning model for eddy current testing with uncertainty. NDT & E International, 101, 104\u2013112. https:\/\/doi.org\/10.1016\/j.ndteint.2018.09.010","journal-title":"NDT & E International"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02272-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02272-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02272-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T22:29:13Z","timestamp":1738621753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02272-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,23]]},"references-count":56,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2272"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02272-4","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,23]]},"assertion":[{"value":"24 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing financial interests or personal relationships that could influence the presented work","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}