{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T16:24:02Z","timestamp":1788539042205,"version":"build-2803163510"},"reference-count":45,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>Reliable in-situ defect classification is essential for ensuring the mechanical performance of parts produced by material extrusion. However, machine learning and deep learning models often suffer from poor generalization because training datasets typically consist of many correlated signal segments generated from only a limited number of independent print jobs. To address this problem, we analyze how job-specific characteristics are distributed across signal components and find that they are concentrated primarily in the signal level rather than in the residual component. Motivated by this observation, we propose Level-Aware Residual Mixup(LARM), a data augmentation method that separately interpolates the level and residual components of sensor signals. LARM preserves realistic job-level characteristics by restricting level interpolation to values observed in real print jobs while allowing flexible mixing of residual components within the same defect class. We evaluate LARM against representative augmentation methods across diverse classification models under a realistic job-level leave-one-group-out cross-validation protocol. Experimental results demonstrate that LARM achieves better generalization than both training without augmentation and representative augmentation methods.<\/jats:p>","DOI":"10.3390\/ma19173769","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T16:07:32Z","timestamp":1788538052000},"page":"3769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Level-Aware Residual Mixup for Defect Classification in Material Extrusion with Limited Print Jobs"],"prefix":"10.3390","volume":"19","author":[{"given":"Yeonggyeom","family":"Kim","sequence":"first","affiliation":[{"name":"School of Industrial and Systems Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7784-8515","authenticated-orcid":false,"given":"Sangho","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Industrial and Systems Engineering, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of Korea"},{"name":"Graduate School of Aerospace and Defense Convergence, Gyeongsang National University, 501 Jinju-daero, Jinju 52828, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.compositesb.2018.02.012","article-title":"Additive manufacturing (3D printing): A review of materials, methods, applications and challenges","volume":"143","author":"Ngo","year":"2018","journal-title":"Compos. Part B Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gao, G., Xu, F., Xu, J., Tang, G., and Liu, Z. (2022). A survey of the influence of process parameters on mechanical properties of fused deposition modeling parts. Micromachines, 13.","DOI":"10.3390\/mi13040553"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1108\/13552540210441166","article-title":"Anisotropic material properties of fused deposition modeling ABS","volume":"8","author":"Ahn","year":"2002","journal-title":"Rapid Prototyp. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.polymertesting.2018.05.020","article-title":"FDM process parameters influence over the mechanical properties of polymer specimens: A review","volume":"69","author":"Popescu","year":"2018","journal-title":"Polym. Test."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1108\/13552540810862028","article-title":"Effect of processing conditions on the bonding quality of FDM polymer filaments","volume":"14","author":"Sun","year":"2008","journal-title":"Rapid Prototyp. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1007\/s40964-021-00192-4","article-title":"Process monitoring for material extrusion additive manufacturing: A state-of-the-art review","volume":"6","author":"Oleff","year":"2021","journal-title":"Prog. Addit. Manuf."},{"key":"ref_7","first-page":"101749","article-title":"In situ monitoring for fused filament fabrication process: A review","volume":"38","author":"Fu","year":"2021","journal-title":"Addit. Manuf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1002\/pol.20230649","article-title":"Application of machine learning in polymer additive manufacturing: A review","volume":"62","author":"Nasrin","year":"2024","journal-title":"J. Polym. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114656","DOI":"10.1016\/j.commatsci.2026.114656","article-title":"Research on defect detection performance of silicon carbide wafer surface based on ESN-YOLOv8 algorithm","volume":"268","author":"Yang","year":"2026","journal-title":"Comput. Mater. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8503","DOI":"10.1038\/s41467-026-75353-6","article-title":"High temperature Nb-Si alloys using data science: Optimization of fracture toughness and high-temperature strength","volume":"17","author":"Xu","year":"2026","journal-title":"Nat. Commun."},{"key":"ref_11","first-page":"102535","article-title":"Machine learning for the intelligent analysis of 3D printing conditions using environmental sensor data to support quality assurance","volume":"50","author":"Westphal","year":"2022","journal-title":"Addit. Manuf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10501","DOI":"10.1007\/s13369-023-08340-4","article-title":"Deep learning-based multi-sensor fusion for process monitoring: Application to fused deposition modeling","volume":"49","author":"Khusheef","year":"2024","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"gix019","DOI":"10.1093\/gigascience\/gix019","article-title":"The need to approximate the use-case in clinical machine learning","volume":"6","author":"Saeb","year":"2017","journal-title":"Gigascience"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rashid, A.A., and Ko\u00e7, M. (2021). Fused filament fabrication process: A review of numerical simulation techniques. Polymers, 13.","DOI":"10.3390\/polym13203534"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., and Xu, H. (2020). Time series data augmentation for deep learning: A survey. arXiv.","DOI":"10.24963\/ijcai.2021\/631"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Iwana, B.K., and Uchida, S. (2021). An empirical survey of data augmentation for time series classification with neural networks. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254841"},{"key":"ref_18","first-page":"217","article-title":"Deep learning for time series classification and extrinsic regression: A current survey","volume":"56","author":"Miller","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_19","unstructured":"Jin, X., Zhu, H., Li, S., Wang, Z., Liu, Z., Tian, J., Yu, C., Qin, H., and Li, S.Z. (2024). A survey on mixup augmentations and beyond. arXiv."},{"key":"ref_20","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (May, January 30). mixup: Beyond Empirical Risk Minimization. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_21","first-page":"37","article-title":"A survey of mix-based data augmentation: Taxonomy, methods, applications, and explainability","volume":"57","author":"Cao","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_22","unstructured":"Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.H., and Choo, J. (2022, January 25\u201329). Reversible instance normalization for accurate time-series forecasting against distribution shift. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.jprocont.2012.12.001","article-title":"A steady-state detection (SSD) algorithm to detect non-stationary drifts in processes","volume":"23","author":"Kelly","year":"2013","journal-title":"J. Process Control"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"131668","DOI":"10.1016\/j.snb.2022.131668","article-title":"Drift in a popular metal oxide sensor dataset reveals limitations for gas classification benchmarks","volume":"361","author":"Dennler","year":"2022","journal-title":"Sens. Actuators B Chem."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107446","DOI":"10.1016\/j.ijpe.2019.07.019","article-title":"Distinguishing between common cause variation and special cause variation in a manufacturing system: A simulation of decision making for different types of variation","volume":"220","author":"Lei","year":"2020","journal-title":"Int. J. Prod. Econ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","article-title":"A review on machinery diagnostics and prognostics implementing condition-based maintenance","volume":"20","author":"Jardine","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106139","DOI":"10.1016\/j.engappai.2023.106139","article-title":"Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research","volume":"123","author":"Gawde","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gul\u00e7ehre, \u00c7., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014, January 25\u201329). Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, W., Peng, G., Li, C., Chen, Y., and Zhang, Z. (2017). A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 17.","DOI":"10.20944\/preprints201701.0132.v1"},{"key":"ref_34","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_35","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., and Kalagnanam, J. (2023, January 1\u20135). A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. Proceedings of the Eleventh International Conference on Learning Representations, Kigali, Rwanda."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-validatory choice and assessment of statistical predictions","volume":"36","author":"Stone","year":"1974","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"ref_37","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2382577.2382579","article-title":"Leakage in data mining: Formulation, detection, and avoidance","volume":"6","author":"Kaufman","year":"2012","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Um, T.T., Pfister, F.M., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., and Kuli\u0107, D. (2017, January 13\u201317). Data augmentation of wearable sensor data for parkinson\u2019s disease monitoring using convolutional neural networks. Proceedings of the 19th ACM International Conference on Multimodal Interaction, Glasgow, Scotland.","DOI":"10.1145\/3136755.3136817"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1016\/j.patcog.2010.09.013","article-title":"A global averaging method for dynamic time warping, with applications to clustering","volume":"44","author":"Petitjean","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Park, D.S., Chan, W., Zhang, Y., Chiu, C.C., Zoph, B., Cubuk, E.D., and Le, Q.V. (2019). Specaugment: A simple data augmentation method for automatic speech recognition. arXiv.","DOI":"10.21437\/Interspeech.2019-2680"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_43","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). Pytorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1037\/1082-989X.1.1.30","article-title":"Forming inferences about some intraclass correlation coefficients","volume":"1","author":"McGraw","year":"1996","journal-title":"Psychol. Methods"}],"container-title":["Materials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1944\/19\/17\/3769\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T16:09:59Z","timestamp":1788538199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1944\/19\/17\/3769"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,4]]},"references-count":45,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["ma19173769"],"URL":"https:\/\/doi.org\/10.3390\/ma19173769","relation":{},"ISSN":["1996-1944"],"issn-type":[{"value":"1996-1944","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,4]]}}}