{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T17:49:32Z","timestamp":1776361772896,"version":"3.51.2"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["SIADD"],"award-info":[{"award-number":["SIADD"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>For an increasing number of applications, the quality and the stability of manufacturing processes can be determined via image and video-image data analysis and new techniques are required to extract and synthesize the relevant information content enclosed in big sensor data to draw conclusions about the process and the final part quality. This paper focuses on video image data where the phenomena under study is captured by a point process whose spatial signature is of interest. A novel approach is proposed which combines spatial data modeling via Ripley\u2019s K-function with Functional Analysis of Variance (FANOVA), i.e., Analysis of Variance on Functional data. The K-function allows to synthesize the spatial pattern information in a function while preserving the capability to capture changes in the process behavior. The method is applicable to quantities and phenomena that can be represented as clusters, or clouds, of spatial points evolving over time. In our case, the motivating case study regards the analysis of spatter ejections caused by the laser-material interaction in Additive Manufacturing via Laser Powder Bed Fusion (L-PBF). The spatial spread of spatters, captured in the form of point particles through in-situ high speed machine vision, can be used as a proxy to select the best conditions to avoid defects (pores) in the manufactured part. The proposed approach is shown to be not only an efficient way to translate the high-dimensional video image data into a lower dimensional format (the K-function curves), but also more effective than benchmark methods in detecting departures from a stable and in-control state.<\/jats:p>","DOI":"10.1007\/s10845-022-02055-3","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T04:27:24Z","timestamp":1669609644000},"page":"429-447","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Modeling spatial point processes in video-imaging via Ripley\u2019s K-function: an application to spatter analysis in additive manufacturing"],"prefix":"10.1007","volume":"35","author":[{"given":"Bianca Maria","family":"Colosimo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Pagani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3233-4198","authenticated-orcid":false,"given":"Marco","family":"Grasso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"2055_CR1","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.addma.2017.12.009","volume":"20","author":"MT Andani","year":"2018","unstructured":"Andani, M. T., Dehghani, R., Karamooz-Ravari, M. R., Mirzaeifar, R., & Ni, J. (2018). A study on the effect of energy input on spatter particles creation during selective laser melting process. Additive Manufacturing, 20, 33\u201343.","journal-title":"Additive Manufacturing"},{"key":"2055_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-31144-0","volume-title":"Case Studies in Spatial Point Process Modeling","author":"A Baddeley","year":"2006","unstructured":"Baddeley, A., Gregori, P., Mateu, J., Stoica, R., & Stoyan, D. (2006). Case Studies in Spatial Point Process Modeling. Springer."},{"issue":"7","key":"2055_CR3","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1007\/s10845-021-01769-0","volume":"32","author":"A Barari","year":"2021","unstructured":"Barari, A., de Sales Guerra\u00a0Tsuzuki, M., & Cohen, Y. (2021). Intelligent manufacturing systems towards industry 4.0 era. Journal of Intelligent Manufacturing, 32(7), 1793\u20131796.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"9","key":"2055_CR4","doi-asserted-by":"publisher","first-page":"1844","DOI":"10.1007\/s11837-018-3025-7","volume":"70","author":"C Barrett","year":"2018","unstructured":"Barrett, C., MacDonald, E., Conner, B., & Persi, F. (2018). Micron-level layer-wise surface profilometry to detect porosity defects in powder bed fusion of Inconel 718. JOM, 70(9), 1844\u20131852.","journal-title":"JOM"},{"key":"2055_CR5","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.actamat.2017.09.051","volume":"142","author":"P Bidare","year":"2018","unstructured":"Bidare, P., Bitharas, I., Ward, R., Attallah, M., & Moore, A. (2018). Fluid and particle dynamics in laser powder bed fusion. Acta Materialia, 142, 107\u2013120.","journal-title":"Acta Materialia"},{"key":"2055_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-81-322-1907-1_2","volume-title":"Exploring Image Binarization Techniques","author":"N Chaki","year":"2014","unstructured":"Chaki, N., Shaikh, S. H., & Saeed, K. (2014). A comprehensive survey on image binarization techniques. In N. Chaki, S. H. Shaikh, & K. Saeed (Eds.), Exploring Image Binarization Techniques (pp. 5\u201315). Springer."},{"key":"2055_CR7","first-page":"1","volume-title":"Quality Monitoring and Control in Additive Manufacturing","author":"BM Colosimo","year":"2014","unstructured":"Colosimo, B. M. (2014). Quality Monitoring and Control in Additive Manufacturing (pp. 1\u20137). Wiley StatsRef: Statistics Reference Online."},{"issue":"1","key":"2055_CR8","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1080\/08982112.2017.1366512","volume":"30","author":"BM Colosimo","year":"2018","unstructured":"Colosimo, B. M. (2018). Modeling and monitoring methods for spatial and image data. Quality Engineering, 30(1), 94\u2013111.","journal-title":"Quality Engineering"},{"issue":"5","key":"2055_CR9","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1080\/00224065.2021.1987806","volume":"53","author":"BM Colosimo","year":"2021","unstructured":"Colosimo, B. M., del Castillo, E., Jones-Farmer, L. A., & Paynabar, K. (2021). Artificial intelligence and statistics for quality technology: An introduction to the special issue. Journal of Quality Technology, 53(5), 443\u2013453.","journal-title":"Journal of Quality Technology"},{"issue":"4","key":"2055_CR10","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1080\/00224065.2018.1507563","volume":"50","author":"BM Colosimo","year":"2018","unstructured":"Colosimo, B. M., & Grasso, M. (2018). Spatially weighted PCA for monitoring video image data with application to additive manufacturing. Journal of Quality Technology, 50(4), 391\u2013417.","journal-title":"Journal of Quality Technology"},{"issue":"3","key":"2055_CR11","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1080\/00224065.2018.1487726","volume":"50","author":"BM Colosimo","year":"2018","unstructured":"Colosimo, B. M., Huang, Q., Dasgupta, T., & Tsung, F. (2018). Opportunities and challenges of quality engineering for additive manufacturing. Journal of Quality Technology, 50(3), 233\u2013252.","journal-title":"Journal of Quality Technology"},{"issue":"3","key":"2055_CR12","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1007\/s11749-010-0215-1","volume":"20","author":"C Comas","year":"2011","unstructured":"Comas, C., Delicado, P., & Mateu, J. (2011). A second order approach to analyse spatial point patterns with functional marks. Test, 20(3), 503\u2013523.","journal-title":"Test"},{"issue":"2","key":"2055_CR13","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1239\/aap\/1013540166","volume":"32","author":"PJ Diggle","year":"2000","unstructured":"Diggle, P. J., Mateu, J., & Clough, H. E. (2000). A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns. Advances in Applied Probability, 32(2), 331\u2013343.","journal-title":"Advances in Applied Probability"},{"issue":"3","key":"2055_CR14","first-page":"645","volume":"54","author":"P Diggle","year":"2005","unstructured":"Diggle, P., Zheng, P., & Durr, P. (2005). Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in Cornwall, UK. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(3), 645\u2013658.","journal-title":"Journal of the Royal Statistical Society: Series C (Applied Statistics)"},{"issue":"3","key":"2055_CR15","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1080\/00401706.2016.1186563","volume":"59","author":"L Dong","year":"2017","unstructured":"Dong, L., Li, X., Yu, D., Zhang, H., Zhang, Z., Qian, Y., & Ding, Y. (2017). Quantifying nanoparticle mixing state to account for both location and size effects. Technometrics, 59(3), 391\u2013403.","journal-title":"Technometrics"},{"issue":"3","key":"2055_CR16","doi-asserted-by":"publisher","first-page":"035002","DOI":"10.1088\/2631-7990\/ab3de9","volume":"1","author":"E Eschner","year":"2019","unstructured":"Eschner, E., Staudt, T., & Schmidt, M. (2019). 3D particle tracking velocimetry for the determination of temporally resolved particle trajectories within laser powder bed fusion of metals. International Journal of Extreme Manufacturing, 1(3), 035002.","journal-title":"International Journal of Extreme Manufacturing"},{"key":"2055_CR17","volume-title":"Additive Manufacturing Technologies","author":"I Gibson","year":"2014","unstructured":"Gibson, I., Rosen, D. W., Stucker, B., et al. (2014). Additive Manufacturing Technologies. Cham: Springer."},{"issue":"4","key":"2055_CR18","doi-asserted-by":"publisher","first-page":"044005","DOI":"10.1088\/1361-6501\/aa5c4f","volume":"28","author":"M Grasso","year":"2017","unstructured":"Grasso, M., & Colosimo, B. M. (2017). Process defects and in situ monitoring methods in metal powder bed fusion: A review. Measurement Science and Technology, 28(4), 044005.","journal-title":"Measurement Science and Technology"},{"issue":"11","key":"2055_CR19","doi-asserted-by":"publisher","first-page":"112001","DOI":"10.1088\/1361-6501\/ac0b6b","volume":"32","author":"M Grasso","year":"2021","unstructured":"Grasso, M., Remani, A., Dickins, A., Colosimo, B., & Leach, R. (2021). In-situ measurement and monitoring methods for metal powder bed fusion: An updated review. Measurement Science and Technology, 32(11), 112001.","journal-title":"Measurement Science and Technology"},{"key":"2055_CR20","series-title":"Geospatial Data Science: Techniques and Applications","volume-title":"Spatiotemporal point pattern analysis using Ripley\u2019s k function","author":"A Hohl","year":"2017","unstructured":"Hohl, A., Zheng, M., Tang, W., Delmelle, E., & Casas, I. (2017). Spatiotemporal point pattern analysis using Ripley\u2019s k function. Geospatial Data Science: Techniques and Applications. Taylor & Francis."},{"issue":"3","key":"2055_CR21","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1080\/00224065.2017.11917991","volume":"49","author":"X Huang","year":"2017","unstructured":"Huang, X., Xu, J., & Zhou, Q. (2017). Multi-scale diagnosis of spatial point interaction via decomposition of the k function-based t2 statistic. Journal of Quality Technology, 49(3), 213\u2013227.","journal-title":"Journal of Quality Technology"},{"issue":"2","key":"2055_CR22","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1109\/TASE.2015.2479088","volume":"14","author":"X Huang","year":"2015","unstructured":"Huang, X., Zhou, Q., Zeng, L., & Li, X. (2015). Monitoring spatial uniformity of particle distributions in manufacturing processes using the k function. IEEE Transactions on Automation Science and Engineering, 14(2), 1031\u20131041.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"2055_CR23","first-page":"9","volume":"4","author":"M Jafari Mamaghani","year":"2010","unstructured":"Jafari Mamaghani, M., Andersson, M., & Krieger, P. (2010). Spatial point pattern analysis of neurons using Ripley\u2019s k-function in 3D. Frontiers in Neuroinformatics, 4, 9.","journal-title":"Frontiers in Neuroinformatics"},{"key":"2055_CR24","volume-title":"Principal Component Analysis","author":"I Jolliffe","year":"2002","unstructured":"Jolliffe, I. (2002). Principal Component Analysis. Springer."},{"issue":"1","key":"2055_CR25","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.jmsy.2012.07.018","volume":"32","author":"KM Kam","year":"2013","unstructured":"Kam, K. M., Zeng, L., Zhou, Q., Tran, R., & Yang, J. (2013). On assessing spatial uniformity of particle distributions in quality control of manufacturing processes. Journal of Manufacturing Systems, 32(1), 154\u2013166.","journal-title":"Journal of Manufacturing Systems"},{"key":"2055_CR26","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.actamat.2016.02.014","volume":"108","author":"SA Khairallah","year":"2016","unstructured":"Khairallah, S. A., Anderson, A. T., Rubenchik, A., & King, W. E. (2016). Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones. Acta Materialia, 108, 36\u201345.","journal-title":"Acta Materialia"},{"issue":"4","key":"2055_CR27","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/j.bpj.2009.05.039","volume":"97","author":"MA Kiskowski","year":"2009","unstructured":"Kiskowski, M. A., Hancock, J. F., & Kenworthy, A. K. (2009). On the use of Ripley\u2019s k-function and its derivatives to analyze domain size. Biophysical Journal, 97(4), 1095\u20131103.","journal-title":"Biophysical Journal"},{"key":"2055_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-022-02029-5","author":"S Kumar","year":"2022","unstructured":"Kumar, S., Gopi, T., Harikeerthana, N., Gupta, M. K., Gaur, V., Krolczyk, G. M., & Wu, C. (2022). Machine learning techniques in additive manufacturing: A state of the art review on design, processes and production control. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-022-02029-5.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"8","key":"2055_CR29","doi-asserted-by":"publisher","first-page":"2008","DOI":"10.1007\/s10845-020-01549-2","volume":"31","author":"X Li","year":"2020","unstructured":"Li, X., Jia, X., Yang, Q., & Lee, J. (2020). Quality analysis in metal additive manufacturing with deep learning. Journal of Intelligent Manufacturing, 31(8), 2008.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"2055_CR30","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1080\/24725854.2018.1478169","volume":"51","author":"J Liu","year":"2019","unstructured":"Liu, J., Liu, C., Bai, Y., Rao, P., Williams, C. B., & Kong, Z. (2019). Layer-wise spatial modeling of porosity in additive manufacturing. IISE Transactions, 51(2), 109\u2013123.","journal-title":"IISE Transactions"},{"key":"2055_CR31","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1016\/j.matdes.2015.08.086","volume":"87","author":"Y Liu","year":"2015","unstructured":"Liu, Y., Yang, Y., Mai, S., Wang, D., & Song, C. (2015). Investigation into spatter behavior during selective laser melting of AISI 316l stainless steel powder. Materials & Design, 87, 797\u2013806.","journal-title":"Materials & Design"},{"key":"2055_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-022-02012-0","author":"J Liu","year":"2022","unstructured":"Liu, J., Ye, J., Silva Izquierdo, D., Vinel, A., Shamsaei, N., & Shao, S. (2022). A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-022-02012-0.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"1","key":"2055_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-04237-z","volume":"7","author":"S Ly","year":"2017","unstructured":"Ly, S., Rubenchik, A. M., Khairallah, S. A., Guss, G., & Matthews, M. J. (2017). Metal vapor micro-jet controls material redistribution in laser powder bed fusion additive manufacturing. Scientific Reports, 7(1), 1\u201312.","journal-title":"Scientific Reports"},{"key":"2055_CR34","doi-asserted-by":"publisher","first-page":"102058","DOI":"10.1016\/j.addma.2021.102058","volume":"45","author":"R McCann","year":"2021","unstructured":"McCann, R., Obeidi, M. A., Hughes, C., McCarthy, \u00c9., Egan, D. S., & Vijayaraghavan, R. K. (2021). In-situ sensing, process monitoring and machine control in laser powder bed fusion: A review. Additive Manufacturing, 45, 102058.","journal-title":"Additive Manufacturing"},{"issue":"8","key":"2055_CR35","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1002\/qre.1287","volume":"28","author":"FM Megahed","year":"2012","unstructured":"Megahed, F. M., Wells, L. J., Camelio, J. A., & Woodall, W. H. (2012). A spatiotemporal method for the monitoring of image data. Quality and Reliability Engineering International, 28(8), 967\u2013980.","journal-title":"Quality and Reliability Engineering International"},{"issue":"2","key":"2055_CR36","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1080\/00224065.2011.11917848","volume":"43","author":"FM Megahed","year":"2011","unstructured":"Megahed, F. M., Woodall, W. H., & Camelio, J. A. (2011). A review and perspective on control charting with image data. Journal of Quality Technology, 43(2), 83\u201398.","journal-title":"Journal of Quality Technology"},{"key":"2055_CR37","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1146\/annurev-statistics-060116-054055","volume":"4","author":"J M\u00f8ller","year":"2017","unstructured":"M\u00f8ller, J., & Waagepetersen, R. (2017). Some recent developments in statistics for spatial point patterns. Annual Review of Statistics and Its Application, 4, 317\u2013342.","journal-title":"Annual Review of Statistics and Its Application"},{"issue":"1","key":"2055_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-41415-7","volume":"9","author":"AR Nassar","year":"2019","unstructured":"Nassar, A. R., Gundermann, M. A., Reutzel, E. W., Guerrier, P., Krane, M. H., & Weldon, M. J. (2019). Formation processes for large ejecta and interactions with melt pool formation in powder bed fusion additive manufacturing. Scientific Reports, 9(1), 1\u201311.","journal-title":"Scientific Reports"},{"key":"2055_CR39","doi-asserted-by":"publisher","DOI":"10.1002\/0471733156","volume-title":"Image Processing and Jump Regression Analysis","author":"P Qiu","year":"2005","unstructured":"Qiu, P. (2005). Image Processing and Jump Regression Analysis (Vol. 599). Wiley."},{"key":"2055_CR40","doi-asserted-by":"crossref","unstructured":"Ramsay, J.O. (2004). Functional data analysis. Encyclopedia of Statistical Sciences.","DOI":"10.1002\/0471667196.ess0646"},{"issue":"2","key":"2055_CR41","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/j.cirp.2007.10.001","volume":"56","author":"J Ramsden","year":"2007","unstructured":"Ramsden, J., Allen, D., Stephenson, D., Alcock, J., Peggs, G., Fuller, G., & Goch, G. (2007). The design and manufacture of biomedical surfaces. CIRP Annals, 56(2), 687\u2013711.","journal-title":"CIRP Annals"},{"key":"2055_CR42","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.addma.2017.05.004","volume":"16","author":"G Repossini","year":"2017","unstructured":"Repossini, G., Laguzza, V., Grasso, M., & Colosimo, B. M. (2017). On the use of spatter signature for in-situ monitoring of laser powder bed fusion. Additive Manufacturing, 16, 35\u201348.","journal-title":"Additive Manufacturing"},{"issue":"2","key":"2055_CR43","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1111\/j.2517-6161.1977.tb01615.x","volume":"39","author":"BD Ripley","year":"1977","unstructured":"Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B (Methodological), 39(2), 172\u2013192.","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"4","key":"2055_CR44","first-page":"459","volume":"56","author":"RS Stoica","year":"2007","unstructured":"Stoica, R. S., Mart\u00ednez, V. J., & Saar, E. (2007). A three-dimensional object point process for detection of cosmic filaments. Journal of the Royal Statistical Society: Series C (Applied Statistics), 56(4), 459\u2013477.","journal-title":"Journal of the Royal Statistical Society: Series C (Applied Statistics)"},{"key":"2055_CR45","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1007\/s10845-022-01963-8","volume":"33","author":"H Tercan","year":"2022","unstructured":"Tercan, H., & Meisen, T. (2022). Machine learning and deep learning based predictive quality in manufacturing: A systematic review. Journal of Intelligent Manufacturing, 33, 1879\u20131905.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"11\u201312","key":"2055_CR46","doi-asserted-by":"publisher","first-page":"2438","DOI":"10.1016\/j.compscitech.2006.12.023","volume":"67","author":"D Trias","year":"2007","unstructured":"Trias, D., Garc\u00eda, R., Costa, J., Blanco, N., & Hurtado, J. (2007). Quality control of CFRP by means of digital image processing and statistical point pattern analysis. Composites Science and Technology, 67(11\u201312), 2438\u20132446.","journal-title":"Composites Science and Technology"},{"issue":"11","key":"2055_CR47","doi-asserted-by":"publisher","first-page":"1042","DOI":"10.1111\/ecog.01579","volume":"39","author":"E Vel\u00e1zquez","year":"2016","unstructured":"Vel\u00e1zquez, E., Mart\u00ednez, I., Getzin, S., Moloney, K. A., & Wiegand, T. (2016). An evaluation of the state of spatial point pattern analysis in ecology. Ecography, 39(11), 1042\u20131055.","journal-title":"Ecography"},{"issue":"2","key":"2055_CR48","first-page":"97","volume":"35","author":"I Yamada","year":"2003","unstructured":"Yamada, I., & Rogerson, P. A. (2003). An empirical comparison of edge effect correction methods applied to k-function analysis. Geographical Analysis, 35(2), 97\u2013109.","journal-title":"Geographical Analysis"},{"issue":"5","key":"2055_CR49","first-page":"464","volume":"54","author":"H Yan","year":"2021","unstructured":"Yan, H., Grasso, M., Paynabar, K., & Colosimo, B. (2021). Real-time detection of clustered events in video-imaging data with applications to additive manufacturing. IISE Transactions, 54(5), 464\u201380.","journal-title":"IISE Transactions"},{"issue":"1","key":"2055_CR50","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1109\/TASE.2014.2327029","volume":"12","author":"H Yan","year":"2015","unstructured":"Yan, H., Paynabar, K., & Shi, J. (2015). Image-based process monitoring using low-rank tensor decomposition. IEEE Transactions on Automation Science and Engineering, 12(1), 216\u2013227.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"1","key":"2055_CR51","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1080\/00401706.2015.1102764","volume":"59","author":"H Yan","year":"2017","unstructured":"Yan, H., Paynabar, K., & Shi, J. (2017). Anomaly detection in images with smooth background via smooth-sparse decomposition. Technometrics, 59(1), 102\u2013114.","journal-title":"Technometrics"},{"key":"2055_CR52","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.addma.2018.10.020","volume":"25","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Fuh, J. Y., Ye, D., & Hong, G. S. (2019). In-situ monitoring of laser-based PBF via off-axis vision and image processing approaches. Additive Manufacturing, 25, 263\u2013274.","journal-title":"Additive Manufacturing"},{"key":"2055_CR53","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hong, G. S., Ye, D., Zhu, K., & Fuh, J. Y. (2018). Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion am process monitoring. Materials & Design, 156, 458\u2013469.","DOI":"10.1016\/j.matdes.2018.07.002"},{"key":"2055_CR54","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Safdar, M., Xie, J., Li, J., Sage, M., & Zhao, Y. F. (2022). A systematic review on data of additive manufacturing for machine learning applications: The data quality, type, preprocessing, and management. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-022-02017-9.","DOI":"10.1007\/s10845-022-02017-9"},{"issue":"2","key":"2055_CR55","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1080\/00401706.2013.804440","volume":"56","author":"Q Zhou","year":"2014","unstructured":"Zhou, Q., Zhou, J., De Cicco, M., Zhou, S., & Li, X. (2014). Detecting 3D spatial clustering of particles in nanocomposites based on cross-sectional images. Technometrics, 56(2), 212\u2013224.","journal-title":"Technometrics"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-02055-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-022-02055-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-02055-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T16:07:02Z","timestamp":1728490022000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-022-02055-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,28]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["2055"],"URL":"https:\/\/doi.org\/10.1007\/s10845-022-02055-3","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,28]]},"assertion":[{"value":"4 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2022","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 paper has been submitted with full responsibility, following due ethical procedure, and there is no duplicate publication, fraud, plagiarism. None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}