Prediction of surface roughness in extrusion-based additive manufacturing with machine learning
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Bourell, 2009, 11
Srivatsan, 2015
Asadi-Eydivand, 2016, Effect of technical parameters on porous structure and strength of 3D printed calcium sulfate prototypes, Rob. Comput. Integr. Manuf., 37, 57, 10.1016/j.rcim.2015.06.005
Zhang, 2002, Model layout optimization for solid ground curing rapid prototyping processes, Rob. Comput. Integr. Manuf., 18, 41, 10.1016/S0736-5845(01)00022-9
Galantucci, 2009, Experimental study aiming to enhance the surface finish of fused deposition modeled parts, CIRP Ann., 58, 189, 10.1016/j.cirp.2009.03.071
Ahn, 2009, Surface roughness prediction using measured data and interpolation in layered manufacturing, J. Mater. Process. Technol., 209, 664, 10.1016/j.jmatprotec.2008.02.050
Byun, 2006, Determination of the optimal build direction for different rapid prototyping processes using multi-criterion decision making, Rob. Comput. Integr. Manuf., 22, 69, 10.1016/j.rcim.2005.03.001
Turner, 2015, "A review of melt extrusion additive manufacturing processes: II. Materials, dimensional accuracy, and surface roughness, Rapid Prototyping J., 21, 250, 10.1108/RPJ-02-2013-0017
Strano, 2013, Surface roughness analysis, modelling and prediction in selective laser melting, J. Mater. Process. Technol., 213, 589, 10.1016/j.jmatprotec.2012.11.011
Calignano, 2013, Influence of process parameters on surface roughness of aluminum parts produced by DMLS, Int. J. Adv. Manuf. Technol., 67, 2743, 10.1007/s00170-012-4688-9
Benardos, 2002, Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments, Rob. Comput. Integr. Manuf., 18, 343, 10.1016/S0736-5845(02)00005-4
Abburi, 2006, A knowledge-based system for the prediction of surface roughness in turning process, Rob. Comput. Integr. Manuf., 22, 363, 10.1016/j.rcim.2005.08.002
Wu, 2019, Predictive modeling of surface roughness in fused deposition modeling using data fusion, Int. J. Prod. Res., 10.1080/00207543.2018.1505058
Tapia, 2014, A review on process monitoring and control in metal-based additive manufacturing, J. Manuf. Sci. Eng., 136, 10.1115/1.4028540
Everton, 2016, Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing, Mater. Des., 95, 431, 10.1016/j.matdes.2016.01.099
Frazier, 2014, Metal additive manufacturing: a review, J. Mater. Eng. Perform., 23, 1917, 10.1007/s11665-014-0958-z
Reutzel, 2015, A survey of sensing and control systems for machine and process monitoring of directed-energy, metal-based additive manufacturing, Rapid Prototyping J., 21, 159, 10.1108/RPJ-12-2014-0177
Rao, 2015, Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors, J. Manuf. Sci. Eng., 137, 10.1115/1.4029823
Zhao, 2017, A data mining approach in real-time measurement for polymer additive manufacturing process with exposure controlled projection lithography, J. Manuf. Syst., 43, 271, 10.1016/j.jmsy.2017.01.005
Kousiatza, 2016, In-situ monitoring of strain and temperature distributions during fused deposition modeling process, Mater. Des., 97, 400, 10.1016/j.matdes.2016.02.099
Galantucci, 2009, Experimental study aiming to enhance the surface finish of fused deposition modeled parts, CIRP Ann. Manuf. Technol., 58, 189, 10.1016/j.cirp.2009.03.071
Boschetto, 2013, 3D roughness profile model in fused deposition modelling, Rapid Prototyping J., 19, 240, 10.1108/13552541311323254
Boschetto, 2015, Roughness prediction in coupled operations of fused deposition modeling and barrel finishing, J. Mater. Process. Technol., 219, 181, 10.1016/j.jmatprotec.2014.12.021
Reeves, 1997, Reducing the surface deviation of stereolithography using in-process techniques, Rapid Prototyping J., 3, 20, 10.1108/13552549710169255
Bonnans, 2013
Liaw, 2002, Classification and regression by randomForest, R News, 2, 18
Wu, 2017, A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests, J. Manuf. Sci. Eng., 139, 10.1115/1.4036350
Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., 55, 119, 10.1006/jcss.1997.1504
Friedman, 2001
Smola, 1997, "Support vector regression machines, Adv. Neural Inf. Process. Syst., 9, 155
Hoerl, 1970, Ridge regression: biased estimation for nonorthogonal problems, Technometrics, 12, 55, 10.1080/00401706.1970.10488634
Zhang, 2016, A comprehensive evaluation of random vector functional link networks, Inf. Sci., 367, 1094, 10.1016/j.ins.2015.09.025