{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:19:04Z","timestamp":1769635144078,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T00:00:00Z","timestamp":1682121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006463","name":"StMWI\u2014Bayern Innovativ Gesellschaft f\u00fcr Innovation und Wissenstransfer mbH","doi-asserted-by":"publisher","award":["41-6562b\/25\/2-VAL-2103-0006"],"award-info":[{"award-number":["41-6562b\/25\/2-VAL-2103-0006"]}],"id":[{"id":"10.13039\/501100006463","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monitoring the metal Additive Manufacturing (AM) process is an important task within the scope of quality assurance. This article presents a method to gain insights into process quality by comparing the actual and target layers. Images of the powder bed were captured and segmented using an Xception\u2013style neural network to predict the powder and part areas. The segmentation result of every layer is compared to the reference layer regarding the area, centroids, and normalized area difference of each part. To evaluate the method, a print job with three parts was chosen where one of them broke off and another one had thermal deformations. The calculated metrics are useful for detecting if a part is damaged or for identifying thermal distortions. The method introduced by this work can be used to monitor the metal AM process for quality assurance. Due to the limited camera resolutions and inconsistent lighting conditions, the approach has some limitations, which are discussed at the end.<\/jats:p>","DOI":"10.3390\/s23094183","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T03:04:08Z","timestamp":1682305448000},"page":"4183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Powder Bed Monitoring Using Semantic Image Segmentation to Detect Failures during 3D Metal Printing"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2256-0741","authenticated-orcid":false,"given":"Anna-Maria","family":"Schmitt","sequence":"first","affiliation":[{"name":"Institute Digital Engineering (IDEE), Technical University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"given":"Christian","family":"Sauer","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), Technical University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"given":"Dennis","family":"H\u00f6fflin","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), Technical University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1447-7331","authenticated-orcid":false,"given":"Andreas","family":"Schiffler","sequence":"additional","affiliation":[{"name":"Institute Digital Engineering (IDEE), Technical University of Applied Sciences, W\u00fcrzburg-Schweinfurt, Ignaz-Sch\u00f6n-Strasse 11, 97421 Schweinfurt, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s40964-019-00078-6","article-title":"Powders for powder bed fusion: A review","volume":"4","author":"Vock","year":"2019","journal-title":"Prog. 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Materials, 15.","DOI":"10.3390\/ma15207166"},{"key":"ref_8","first-page":"517","article-title":"Application of Supervised Machine Learning for Defect Detection during Metallic Powder Bed Fusion Additive Manufacturing Using High Resolution Imaging","volume":"21","author":"Gobert","year":"2018","journal-title":"Addit. Manuf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101692","DOI":"10.1016\/j.media.2020.101692","article-title":"Fully Automatic Brain Tumor Segmentation with Deep Learning-Based Selective Attention Using Overlapping Patches and Multi-Class Weighted Cross-Entropy","volume":"63","author":"Akil","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_10","first-page":"101435","article-title":"Automated Layerwise Detection of Geometrical Distortions in Laser Powder Bed Fusion","volume":"36","author":"Pagani","year":"2020","journal-title":"Addit. Manuf."},{"key":"ref_11","first-page":"183","article-title":"Characterization of In-Situ Measurements Based on Layerwise Imaging in Laser Powder Bed Fusion","volume":"24","author":"Caltanissetta","year":"2018","journal-title":"Addit. Manuf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s40964-018-0068-9","article-title":"In Situ Measurement of Part Geometries in Layer Images from Laser Beam Melting Processes","volume":"4","author":"Achterhold","year":"2019","journal-title":"Prog. Addit. Manuf."},{"key":"ref_13","first-page":"114","article-title":"Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process Using a Trained Computer Vision Algorithm","volume":"19","author":"Scime","year":"2018","journal-title":"Addit. Manuf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s40964-019-00108-3","article-title":"A Deep Learning-Based Model for Defect Detection in Laser-Powder Bed Fusion Using in-Situ Thermographic Monitoring","volume":"5","author":"Baumgartl","year":"2020","journal-title":"Prog. Addit. Manuf."},{"key":"ref_15","first-page":"101453","article-title":"Layer-Wise Anomaly Detection and Classification for Powder Bed Additive Manufacturing Processes: A Machine-Agnostic Algorithm for Real-Time Pixel-Wise Semantic Segmentation","volume":"36","author":"Scime","year":"2020","journal-title":"Addit. Manuf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111001","DOI":"10.1115\/1.4044420","article-title":"Deep learning of variant geometry in layerwise imaging profiles for additive manufacturing quality control","volume":"141","author":"Imani","year":"2019","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rahman, M.A., and Wang, Y. (2016, January 12\u201314). Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation. Proceedings of the Advances in Visual Computing, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-50835-1_22"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TPAMI.1987.4767941","article-title":"Image Analysis Using Mathematical Morphology","volume":"PAMI-9","author":"Haralick","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5133","DOI":"10.1007\/s00170-022-08995-7","article-title":"A Convolutional Neural Network (CNN) classification to identify the presence of pores in powder bed fusion images","volume":"120","author":"Ansari","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_22","first-page":"1","article-title":"Flaw detection in powder bed fusion using optical imaging","volume":"15","author":"Abdelrahman","year":"2017","journal-title":"Addit. Manuf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_24","unstructured":"Phan, T.H., and Yamamoto, K. (2023, March 10). Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses. Available online: https:\/\/arxiv.org\/abs\/2006.01413."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4183\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:21:11Z","timestamp":1760124071000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,22]]},"references-count":24,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094183"],"URL":"https:\/\/doi.org\/10.3390\/s23094183","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,22]]}}}