Hybrid data-driven feature extraction-enabled surface modeling for metal additive manufacturing

Metal additive manufacturing (AM) has become popular in a large variety of applications due to its excellent capabilities of handling complex geometries and novel materials. However, due to its process complexity, layer-wise surface quality issue is still one of the critical concerns to further broa...

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Published in:	International journal of advanced manufacturing technology Vol. 121; no. 7-8; pp. 4643 - 4662
Main Authors:	Shi, Zhangyue, Mandal, Soumya, Harimkar, Sandip, Liu, Chenang
Format:	Journal Article
Language:	English
Published:	London Springer London 01.08.2022
Subjects:	CAE) and Design Computer-Aided Engineering (CAD Engineering Industrial and Production Engineering Mechanical Engineering Media Management Original Article Surface modeling Robust convolutional autoencoder Additive manufacturing Hybrid feature extraction Machine learning
ISSN:	0268-3768, 1433-3015
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Abstract	Metal additive manufacturing (AM) has become popular in a large variety of applications due to its excellent capabilities of handling complex geometries and novel materials. However, due to its process complexity, layer-wise surface quality issue is still one of the critical concerns to further broaden adoption of metal AM, because of the impact on products’ property and functionality. The existing experimental studies from literature have shown machine parameters could significantly affect the resulting surface morphology of printing products. Consequently, it is urgently necessary to analyze and model printing surface in metal AM, and thereby printing surface can be further correlated with machine parameters, enabling more appropriate quality assurance applications such as process design and post anomaly detection. However, there are two major practical challenges to realize this goal: (1) the printing surface profiles in metal AM are highly nonlinear; and (2) the measured surface profiles usually have significant outliers, shifts, and porosities. To address these two challenges, this paper models surface profile in a decomposition-based framework and develops a hybrid data-driven feature extraction approach, which integrates a robust convolutional autoencoder-based approach and conventional statistics-based approach. Through the incorporation of supervised machine learning algorithm, the underlying relationship between machine parameters and printing surface can be thereby clearly quantified. To validate effectiveness of the proposed method, both simulation and a real-world case study in laser-engineered net shaping (LENS) were conducted in this work. The results demonstrate that the classification accuracy using the proposed method could achieve 86% in simulation cases and 74% in an actual LENS experiment, which outperforms the benchmark methods with better robustness. Therefore, it demonstrates that the developed approach is very promising for surface morphology analysis and process optimization of metal AM.
AbstractList	Metal additive manufacturing (AM) has become popular in a large variety of applications due to its excellent capabilities of handling complex geometries and novel materials. However, due to its process complexity, layer-wise surface quality issue is still one of the critical concerns to further broaden adoption of metal AM, because of the impact on products’ property and functionality. The existing experimental studies from literature have shown machine parameters could significantly affect the resulting surface morphology of printing products. Consequently, it is urgently necessary to analyze and model printing surface in metal AM, and thereby printing surface can be further correlated with machine parameters, enabling more appropriate quality assurance applications such as process design and post anomaly detection. However, there are two major practical challenges to realize this goal: (1) the printing surface profiles in metal AM are highly nonlinear; and (2) the measured surface profiles usually have significant outliers, shifts, and porosities. To address these two challenges, this paper models surface profile in a decomposition-based framework and develops a hybrid data-driven feature extraction approach, which integrates a robust convolutional autoencoder-based approach and conventional statistics-based approach. Through the incorporation of supervised machine learning algorithm, the underlying relationship between machine parameters and printing surface can be thereby clearly quantified. To validate effectiveness of the proposed method, both simulation and a real-world case study in laser-engineered net shaping (LENS) were conducted in this work. The results demonstrate that the classification accuracy using the proposed method could achieve 86% in simulation cases and 74% in an actual LENS experiment, which outperforms the benchmark methods with better robustness. Therefore, it demonstrates that the developed approach is very promising for surface morphology analysis and process optimization of metal AM.
Author	Liu, Chenang Shi, Zhangyue Harimkar, Sandip Mandal, Soumya
Author_xml	– sequence: 1 givenname: Zhangyue surname: Shi fullname: Shi, Zhangyue organization: School of Industrial Engineering and Management, Oklahoma State University – sequence: 2 givenname: Soumya surname: Mandal fullname: Mandal, Soumya organization: School of Mechanical and Aerospace Engineering, Oklahoma State University – sequence: 3 givenname: Sandip surname: Harimkar fullname: Harimkar, Sandip organization: School of Mechanical and Aerospace Engineering, Oklahoma State University – sequence: 4 givenname: Chenang surname: Liu fullname: Liu, Chenang email: chenang.liu@okstate.edu organization: School of Industrial Engineering and Management, Oklahoma State University
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Cites_doi	10.1007/978-3-319-71249-9_3 10.2319/051213-365.1 10.1016/j.optlastec.2018.11.054 10.1007/BF00058655 10.1016/j.optlastec.2018.04.007 10.1016/j.measurement.2014.09.025 10.1016/j.matdes.2017.03.065 10.1016/j.cirp.2008.03.110 10.1016/j.optlastec.2019.105592 10.3390/s17040933 10.1038/nbt1206-1565 10.1016/j.matdes.2020.108762 10.1016/j.precisioneng.2019.06.004 10.1016/j.jmsy.2018.04.001 10.1016/j.msea.2017.07.071 10.1177/0954406218813384 10.1016/j.jmapro.2019.05.001 10.3390/ma13051115 10.1016/j.procir.2016.02.347 10.1016/j.promfg.2021.06.005 10.1007/s00170-015-7077-3 10.1016/j.jmapro.2019.07.027 10.1115/1.4045419 10.1145/3097983.3098052 10.1016/j.precisioneng.2018.01.002 10.1109/TBDATA.2017.2717439 10.1016/j.jmsy.2016.09.007 10.1016/j.promfg.2020.05.112 10.1007/BF01304620 10.1007/s10845-021-01879-9 10.1007/978-3-642-15883-4_27 10.1007/s11665-014-0958-z 10.1023/A:1010933404324 10.1016/j.jmapro.2015.06.026 10.1007/s00170-018-03245-1 10.1115/1.4037891 10.1016/j.surfcoat.2016.09.019 10.1016/j.jmapro.2019.04.020 10.1007/s10856-008-3478-2 10.1016/j.cja.2017.08.019 10.1016/j.matchar.2015.06.017 10.1016/j.apsusc.2015.03.184 10.1109/CVPR.2014.244 10.1016/j.precisioneng.2018.09.007 10.1115/1.4049521 10.1016/j.precisioneng.2016.06.001 10.1016/S0167-9473(01)00065-2 10.1016/j.isprsjprs.2011.11.002 10.1109/ASMC.2017.7969228 10.1016/j.jmsy.2021.04.007 10.1016/j.ceramint.2017.03.085 10.1088/1757-899X/272/1/012017 10.1016/j.compositesb.2018.02.012 10.1561/9781601984616 10.1016/S1526-6125(05)70080-3 10.1201/b11022 10.1007/s10845-022-01933-0 10.1017/CBO9780511804441
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References	Khanzadeh (CR11) 2018; 47 Shi (CR21) 2021; 53 Ferguson (CR28) 2015; 19 Zhai (CR27) 2019; 27 Senthilkumar, Tamizharasan, Anandakrishnan (CR25) 2014; 58 Ansari (CR23) 2019; 112 Cabanettes (CR35) 2018; 52 Shu (CR14) 2020; 142 Fox, Moylan, Lane (CR42) 2016; 45 CR38 Wang (CR47) 2017; 17 CR34 Oliveira, LaLonde, Ma (CR18) 2020; 193 CR33 Jiang (CR19) 2019; 119 Liu (CR10) 2019; 45 Zhang (CR22) 2017; 703 Liu (CR20) 2019; 102 Lin (CR36) 2019; 60 CR49 Kummailil (CR17) 2005; 7 Gong (CR69) 2014; 1 Liu (CR8) 2019; 42 Khalid, Peng (CR26) 2021; 143 CR41 Rodriguez-Galiano (CR60) 2012; 67 Ahmed (CR2) 2019; 42 Townsend (CR43) 2016; 46 Townsend (CR58) 2016; 46 CR16 Frazier (CR3) 2014; 23 CR13 CR12 CR56 Culmone, Smit, Breedveld (CR6) 2019; 27 CR55 CR54 CR53 Lou (CR57) 2019; 57 Jiang, Scott, Whitehouse (CR46) 2008; 57 CR51 Yueling (CR32) 2018; 31 Bandyopadhyay (CR4) 2009; 20 Chacón (CR67) 2017; 124 Tootooni (CR45) 2016; 41 Tsai, Chen, Chen (CR48) 1998; 14 Ding (CR39) 2015; 106 Stašić, Božić (CR31) 2016; 307 Razavi (CR9) 2021; 235 Ding (CR24) 2015; 81 Noble (CR63) 2006; 24 Kustas (CR29) 2018; 21 Friedman (CR65) 2002; 38 Zhang, Sahasrabudhe, Bandyopadhyay (CR1) 2015; 346 Kim, Heo, Lagravère (CR40) 2014; 84 Ngo (CR7) 2018; 143 Ye (CR37) 2020; 48 CR66 CR64 Shi (CR15) 2022; - Hertlein (CR50) 2021; 59 CR62 Breiman (CR59) 2001; 45 Breiman (CR61) 1996; 24 Li (CR5) 2017; 43 Thompson (CR44) 2018; 20 Yu (CR68) 2018; 106 Chen (CR52) 2017; 7 Patalas-Maliszewska (CR30) 2020; 13 N Ahmed (9608_CR2) 2019; 42 VF Rodriguez-Galiano (9608_CR60) 2012; 67 WS Noble (9608_CR63) 2006; 24 Y Zhai (9608_CR27) 2019; 27 Z Ye (9608_CR37) 2020; 48 JC Fox (9608_CR42) 2016; 45 D-M Tsai (9608_CR48) 1998; 14 N Hertlein (9608_CR50) 2021; 59 R Ding (9608_CR39) 2015; 106 AB Kustas (9608_CR29) 2018; 21 X Jiang (9608_CR46) 2008; 57 M Chen (9608_CR52) 2017; 7 9608_CR41 9608_CR49 S Lou (9608_CR57) 2019; 57 TD Ngo (9608_CR7) 2018; 143 L Breiman (9608_CR61) 1996; 24 M Khanzadeh (9608_CR11) 2018; 47 M Zhang (9608_CR22) 2017; 703 L Breiman (9608_CR59) 2001; 45 D Shu (9608_CR14) 2020; 142 A Bandyopadhyay (9608_CR4) 2009; 20 JH Friedman (9608_CR65) 2002; 38 9608_CR51 9608_CR53 9608_CR55 X Wang (9608_CR47) 2017; 17 9608_CR54 9608_CR13 9608_CR12 J Stašić (9608_CR31) 2016; 307 9608_CR56 J-H Yu (9608_CR68) 2018; 106 A Townsend (9608_CR43) 2016; 46 9608_CR16 JP Oliveira (9608_CR18) 2020; 193 N Senthilkumar (9608_CR25) 2014; 58 Y Zhang (9608_CR1) 2015; 346 Z Liu (9608_CR8) 2019; 42 F Cabanettes (9608_CR35) 2018; 52 M Ansari (9608_CR23) 2019; 112 J Kim (9608_CR40) 2014; 84 H Gong (9608_CR69) 2014; 1 9608_CR62 J Patalas-Maliszewska (9608_CR30) 2020; 13 W Lin (9608_CR36) 2019; 60 9608_CR64 Y Li (9608_CR5) 2017; 43 9608_CR66 D Ding (9608_CR24) 2015; 81 A Thompson (9608_CR44) 2018; 20 G Yueling (9608_CR32) 2018; 31 SMJ Razavi (9608_CR9) 2021; 235 WE Frazier (9608_CR3) 2014; 23 M Khalid (9608_CR26) 2021; 143 S Liu (9608_CR10) 2019; 45 C Culmone (9608_CR6) 2019; 27 MS Tootooni (9608_CR45) 2016; 41 A Townsend (9608_CR58) 2016; 46 J Chacón (9608_CR67) 2017; 124 Z Liu (9608_CR20) 2019; 102 H-Z Jiang (9608_CR19) 2019; 119 Z Shi (9608_CR21) 2021; 53 9608_CR33 J Kummailil (9608_CR17) 2005; 7 9608_CR34 J Ferguson (9608_CR28) 2015; 19 Z Shi (9608_CR15) 2022; - 9608_CR38
References_xml	– ident: CR49 – volume: 19 start-page: 163 year: 2015 end-page: 170 ident: CR28 article-title: Semi-empirical model of deposit size and porosity in 420 stainless steel and 4140 steel using laser engineered net shaping publication-title: J Manuf Process – volume: 42 start-page: 167 year: 2019 end-page: 191 ident: CR2 article-title: Direct metal fabrication in rapid prototyping: a review publication-title: J Manuf Process – volume: 38 start-page: 367 issue: 4 year: 2002 end-page: 378 ident: CR65 article-title: Stochastic gradient boosting publication-title: Comput Stat Data Anal – ident: CR16 – ident: CR51 – ident: CR12 – volume: 23 start-page: 1917 issue: 6 year: 2014 end-page: 1928 ident: CR3 article-title: Metal additive manufacturing: a review publication-title: J Mater Eng Perform – volume: - start-page: 1 year: 2022 end-page: 17 ident: CR15 article-title: An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing publication-title: J Intell Manuf – volume: 59 start-page: 675 year: 2021 end-page: 685 ident: CR50 article-title: Generative adversarial network for early-stage design flexibility in topology optimization for additive manufacturing publication-title: J Manuf Syst – ident: CR54 – volume: 20 start-page: 29 issue: 1 year: 2009 ident: CR4 article-title: Application of laser engineered net shaping (LENS) to manufacture porous and functionally graded structures for load bearing implants publication-title: J Mater Sci - Mater Med – volume: 57 start-page: 1 year: 2019 end-page: 15 ident: CR57 article-title: Characterisation methods for powder bed fusion processed surface topography publication-title: Precis Eng – volume: 46 start-page: 34 year: 2016 end-page: 47 ident: CR58 publication-title: Surface texture metrology for metal additive manufacturing: a review – volume: 17 start-page: 933 issue: 4 year: 2017 ident: CR47 article-title: Using wavelet packet transform for surface roughness evaluation and texture extraction publication-title: Sensors – volume: 235 start-page: 1930 issue: 10 year: 2021 end-page: 1937 ident: CR9 article-title: Porosity effect on tensile behavior of Ti-6Al-4V specimens produced by laser engineered net shaping technology publication-title: Proc Inst Mech Eng C J Mech Eng Sci – volume: 52 start-page: 249 year: 2018 end-page: 265 ident: CR35 article-title: Topography of as built surfaces generated in metal additive manufacturing: a multi scale analysis from form to roughness publication-title: Precis Eng – volume: 27 start-page: 461 year: 2019 end-page: 473 ident: CR6 article-title: Additive manufacturing of medical instruments: A state-of-the-art review publication-title: Addit Manuf – volume: 7 start-page: 750 issue: 4 year: 2017 end-page: 758 ident: CR52 article-title: Deep feature learning for medical image analysis with convolutional autoencoder neural network publication-title: IEEE Transactions on Big Data – volume: 142 issue: 7 year: 2020 ident: CR14 article-title: 3d design using generative adversarial networks and physics-based validation publication-title: J Mech Des – volume: 24 start-page: 123 issue: 2 year: 1996 end-page: 140 ident: CR61 article-title: Bagging predictors publication-title: Mach Learn – volume: 45 start-page: 131 year: 2016 end-page: 134 ident: CR42 article-title: Effect of process parameters on the surface roughness of overhanging structures in laser powder bed fusion additive manufacturing publication-title: Procedia Cirp – volume: 24 start-page: 1565 issue: 12 year: 2006 end-page: 1567 ident: CR63 article-title: What is a support vector machine? publication-title: Nat Biotechnol – volume: 84 start-page: 443 issue: 3 year: 2014 end-page: 450 ident: CR40 article-title: Accuracy of laser-scanned models compared to plaster models and cone-beam computed tomography publication-title: Angle Orthod – volume: 45 start-page: 5 issue: 1 year: 2001 end-page: 32 ident: CR59 article-title: Random forests publication-title: Mach Learn – volume: 42 start-page: 96 year: 2019 end-page: 105 ident: CR8 article-title: Influence of energy density on macro/micro structures and mechanical properties of as-deposited Inconel 718 parts fabricated by laser engineered net shaping publication-title: J Manuf Process – volume: 7 start-page: 42 issue: 1 year: 2005 end-page: 50 ident: CR17 article-title: Effect of select LENS™ processing parameters on the deposition of Ti-6Al-4V publication-title: J Manuf Process – volume: 119 year: 2019 ident: CR19 article-title: Factor analysis of selective laser melting process parameters with normalised quantities and Taguchi method publication-title: Opt Laser Technol – volume: 1 start-page: 87 year: 2014 end-page: 98 ident: CR69 article-title: Analysis of defect generation in Ti–6Al–4V parts made using powder bed fusion additive manufacturing processes publication-title: Addit Manuf – volume: 193 year: 2020 ident: CR18 article-title: Processing parameters in laser powder bed fusion metal additive manufacturing publication-title: Mater Des – volume: 48 start-page: 770 year: 2020 end-page: 775 ident: CR37 article-title: A deep learning approach for the identification of small process shifts in additive manufacturing using 3D point clouds publication-title: Procedia Manuf – volume: 14 start-page: 412 issue: 6 year: 1998 end-page: 422 ident: CR48 article-title: A vision system for surface roughness assessment using neural networks publication-title: Int J Adv Manuf Technol – volume: 124 start-page: 143 year: 2017 end-page: 157 ident: CR67 article-title: Additive manufacturing of PLA structures using fused deposition modelling: Effect of process parameters on mechanical properties and their optimal selection publication-title: Mater Des – volume: 13 start-page: 1115 issue: 5 year: 2020 ident: CR30 article-title: Single tracks as a key factor in additive manufacturing technology—analysis of research trends and metal deposition behavior publication-title: Materials – ident: CR64 – volume: 57 start-page: 555 issue: 1 year: 2008 end-page: 558 ident: CR46 article-title: Wavelets and their applications for surface metrology publication-title: CIRP Ann – volume: 47 start-page: 69 year: 2018 end-page: 82 ident: CR11 article-title: Porosity prediction: supervised-learning of thermal history for direct laser deposition publication-title: J Manuf Syst – ident: CR66 – ident: CR53 – volume: 106 start-page: 87 year: 2018 end-page: 93 ident: CR68 article-title: Repairing casting part using laser assisted additive metal-layer deposition and its mechanical properties publication-title: Opt Laser Technol – ident: CR33 – volume: 53 start-page: 16 year: 2021 end-page: 23 ident: CR21 article-title: Surface morphology analysis using convolutional autoencoder in additive manufacturing with laser engineered net shaping publication-title: Procedia Manuf – volume: 112 start-page: 485 year: 2019 end-page: 493 ident: CR23 article-title: Laser directed energy deposition of water-atomized iron powder: Process optimization and microstructure of single-tracks publication-title: Opt Laser Technol – volume: 46 start-page: 34 year: 2016 end-page: 47 ident: CR43 article-title: Surface texture metrology for metal additive manufacturing: a review publication-title: Precis Eng – ident: CR56 – volume: 81 start-page: 465 issue: 1 year: 2015 end-page: 481 ident: CR24 article-title: Wire-feed additive manufacturing of metal components: technologies, developments and future interests publication-title: Int J Adv Manuf Technol – volume: 703 start-page: 251 year: 2017 end-page: 261 ident: CR22 article-title: Fatigue and fracture behaviour of laser powder bed fusion stainless steel 316L: influence of processing parameters publication-title: Mater Sci Eng A – volume: 143 issue: 3 year: 2021 ident: CR26 article-title: Investigation of printing parameters of additive manufacturing process for sustainability using Design of Experiments publication-title: J Mech Des – volume: 27 start-page: 334 year: 2019 end-page: 344 ident: CR27 article-title: Understanding the microstructure and mechanical properties of Ti-6Al-4V and Inconel 718 alloys manufactured by laser engineered net shaping publication-title: Addit Manuf – volume: 21 start-page: 41 year: 2018 end-page: 52 ident: CR29 article-title: Characterization of the Fe-Co-1.5 V soft ferromagnetic alloy processed by Laser Engineered Net Shaping (LENS) publication-title: Addit Manuf – volume: 102 start-page: 969 issue: 1 year: 2019 end-page: 976 ident: CR20 article-title: Effects of deposition variables on molten pool temperature during laser engineered net shaping of Inconel 718 superalloy publication-title: Int J Adv Manuf Technol – volume: 60 start-page: 76 year: 2019 end-page: 84 ident: CR36 article-title: Online quality monitoring in material extrusion additive manufacturing processes based on laser scanning technology publication-title: Precis Eng – volume: 346 start-page: 428 year: 2015 end-page: 437 ident: CR1 article-title: Additive manufacturing of Ti-Si-N ceramic coatings on titanium publication-title: Appl Surf Sci – volume: 106 start-page: 324 year: 2015 end-page: 337 ident: CR39 article-title: Electron microscopy study of direct laser deposited IN718 publication-title: Mater Charact – ident: CR38 – volume: 43 start-page: 7768 issue: 10 year: 2017 end-page: 7775 ident: CR5 article-title: Additive manufacturing of alumina using laser engineered net shaping: Effects of deposition variables publication-title: Ceram Int – ident: CR13 – volume: 41 start-page: 266 year: 2016 end-page: 276 ident: CR45 article-title: Online non-contact surface finish measurement in machining using graph theory-based image analysis publication-title: J Manuf Syst – ident: CR34 – volume: 31 start-page: 860 issue: 4 year: 2018 end-page: 866 ident: CR32 article-title: Single track and single layer formation in selective laser melting of niobium solid solution alloy publication-title: Chin J Aeronaut – ident: CR55 – volume: 67 start-page: 93 year: 2012 end-page: 104 ident: CR60 article-title: An assessment of the effectiveness of a random forest classifier for land-cover classification publication-title: ISPRS J Photogramm Remote Sens – volume: 45 start-page: 579 year: 2019 end-page: 587 ident: CR10 article-title: Multiphysics modeling of phase transformation and microhardness evolution in laser direct deposited Ti6Al4V publication-title: J Manuf Process – volume: 143 start-page: 172 year: 2018 end-page: 196 ident: CR7 article-title: Additive manufacturing (3D printing): a review of materials, methods, applications and challenges publication-title: Compos B Eng – volume: 307 start-page: 407 year: 2016 end-page: 417 ident: CR31 article-title: The effect of NiB additive on surface morphology and microstructure of 316L stainless steel single tracks and layers obtained by SLM publication-title: Surf Coat Technol – ident: CR41 – volume: 20 start-page: 126 year: 2018 end-page: 133 ident: CR44 publication-title: Internal surface measurement of metal powder bed fusion parts – ident: CR62 – volume: 58 start-page: 520 year: 2014 end-page: 536 ident: CR25 article-title: Experimental investigation and performance analysis of cemented carbide inserts of different geometries using Taguchi based grey relational analysis publication-title: Measurement – ident: 9608_CR16 doi: 10.1007/978-3-319-71249-9_3 – volume: 84 start-page: 443 issue: 3 year: 2014 ident: 9608_CR40 publication-title: Angle Orthod doi: 10.2319/051213-365.1 – volume: 112 start-page: 485 year: 2019 ident: 9608_CR23 publication-title: Opt Laser Technol doi: 10.1016/j.optlastec.2018.11.054 – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 9608_CR61 publication-title: Mach Learn doi: 10.1007/BF00058655 – volume: 106 start-page: 87 year: 2018 ident: 9608_CR68 publication-title: Opt Laser Technol doi: 10.1016/j.optlastec.2018.04.007 – volume: 58 start-page: 520 year: 2014 ident: 9608_CR25 publication-title: Measurement doi: 10.1016/j.measurement.2014.09.025 – volume: 124 start-page: 143 year: 2017 ident: 9608_CR67 publication-title: Mater Des doi: 10.1016/j.matdes.2017.03.065 – volume: 57 start-page: 555 issue: 1 year: 2008 ident: 9608_CR46 publication-title: CIRP Ann doi: 10.1016/j.cirp.2008.03.110 – ident: 9608_CR62 – volume: 119 year: 2019 ident: 9608_CR19 publication-title: Opt Laser Technol doi: 10.1016/j.optlastec.2019.105592 – volume: 17 start-page: 933 issue: 4 year: 2017 ident: 9608_CR47 publication-title: Sensors doi: 10.3390/s17040933 – volume: 24 start-page: 1565 issue: 12 year: 2006 ident: 9608_CR63 publication-title: Nat Biotechnol doi: 10.1038/nbt1206-1565 – volume: 193 year: 2020 ident: 9608_CR18 publication-title: Mater Des doi: 10.1016/j.matdes.2020.108762 – ident: 9608_CR66 – volume: 60 start-page: 76 year: 2019 ident: 9608_CR36 publication-title: Precis Eng doi: 10.1016/j.precisioneng.2019.06.004 – volume: 47 start-page: 69 year: 2018 ident: 9608_CR11 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2018.04.001 – volume: 703 start-page: 251 year: 2017 ident: 9608_CR22 publication-title: Mater Sci Eng A doi: 10.1016/j.msea.2017.07.071 – volume: 235 start-page: 1930 issue: 10 year: 2021 ident: 9608_CR9 publication-title: Proc Inst Mech Eng C J Mech Eng Sci doi: 10.1177/0954406218813384 – volume: 42 start-page: 167 year: 2019 ident: 9608_CR2 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2019.05.001 – volume: 13 start-page: 1115 issue: 5 year: 2020 ident: 9608_CR30 publication-title: Materials doi: 10.3390/ma13051115 – volume: 45 start-page: 131 year: 2016 ident: 9608_CR42 publication-title: Procedia Cirp doi: 10.1016/j.procir.2016.02.347 – volume: 53 start-page: 16 year: 2021 ident: 9608_CR21 publication-title: Procedia Manuf doi: 10.1016/j.promfg.2021.06.005 – volume: 81 start-page: 465 issue: 1 year: 2015 ident: 9608_CR24 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-015-7077-3 – volume: 45 start-page: 579 year: 2019 ident: 9608_CR10 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2019.07.027 – volume: 21 start-page: 41 year: 2018 ident: 9608_CR29 publication-title: Addit Manuf – volume: 27 start-page: 334 year: 2019 ident: 9608_CR27 publication-title: Addit Manuf – volume: 142 issue: 7 year: 2020 ident: 9608_CR14 publication-title: J Mech Des doi: 10.1115/1.4045419 – ident: 9608_CR12 doi: 10.1145/3097983.3098052 – volume: 52 start-page: 249 year: 2018 ident: 9608_CR35 publication-title: Precis Eng doi: 10.1016/j.precisioneng.2018.01.002 – volume: 7 start-page: 750 issue: 4 year: 2017 ident: 9608_CR52 publication-title: IEEE Transactions on Big Data doi: 10.1109/TBDATA.2017.2717439 – volume: 41 start-page: 266 year: 2016 ident: 9608_CR45 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2016.09.007 – volume: 48 start-page: 770 year: 2020 ident: 9608_CR37 publication-title: Procedia Manuf doi: 10.1016/j.promfg.2020.05.112 – volume: 14 start-page: 412 issue: 6 year: 1998 ident: 9608_CR48 publication-title: Int J Adv Manuf Technol doi: 10.1007/BF01304620 – volume: 46 start-page: 34 year: 2016 ident: 9608_CR58 publication-title: Surface texture metrology for metal additive manufacturing: a review – volume: - start-page: 1 year: 2022 ident: 9608_CR15 publication-title: J Intell Manuf doi: 10.1007/s10845-021-01879-9 – volume: 1 start-page: 87 year: 2014 ident: 9608_CR69 publication-title: Addit Manuf – ident: 9608_CR13 – ident: 9608_CR56 doi: 10.1007/978-3-642-15883-4_27 – ident: 9608_CR51 – volume: 23 start-page: 1917 issue: 6 year: 2014 ident: 9608_CR3 publication-title: J Mater Eng Perform doi: 10.1007/s11665-014-0958-z – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 9608_CR59 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 19 start-page: 163 year: 2015 ident: 9608_CR28 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2015.06.026 – volume: 102 start-page: 969 issue: 1 year: 2019 ident: 9608_CR20 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-018-03245-1 – ident: 9608_CR38 doi: 10.1115/1.4037891 – volume: 307 start-page: 407 year: 2016 ident: 9608_CR31 publication-title: Surf Coat Technol doi: 10.1016/j.surfcoat.2016.09.019 – volume: 42 start-page: 96 year: 2019 ident: 9608_CR8 publication-title: J Manuf Process doi: 10.1016/j.jmapro.2019.04.020 – ident: 9608_CR64 – volume: 20 start-page: 29 issue: 1 year: 2009 ident: 9608_CR4 publication-title: J Mater Sci - Mater Med doi: 10.1007/s10856-008-3478-2 – volume: 31 start-page: 860 issue: 4 year: 2018 ident: 9608_CR32 publication-title: Chin J Aeronaut doi: 10.1016/j.cja.2017.08.019 – volume: 106 start-page: 324 year: 2015 ident: 9608_CR39 publication-title: Mater Charact doi: 10.1016/j.matchar.2015.06.017 – volume: 346 start-page: 428 year: 2015 ident: 9608_CR1 publication-title: Appl Surf Sci doi: 10.1016/j.apsusc.2015.03.184 – volume: 27 start-page: 461 year: 2019 ident: 9608_CR6 publication-title: Addit Manuf – ident: 9608_CR53 doi: 10.1109/CVPR.2014.244 – volume: 57 start-page: 1 year: 2019 ident: 9608_CR57 publication-title: Precis Eng doi: 10.1016/j.precisioneng.2018.09.007 – volume: 143 issue: 3 year: 2021 ident: 9608_CR26 publication-title: J Mech Des doi: 10.1115/1.4049521 – volume: 46 start-page: 34 year: 2016 ident: 9608_CR43 publication-title: Precis Eng doi: 10.1016/j.precisioneng.2016.06.001 – volume: 38 start-page: 367 issue: 4 year: 2002 ident: 9608_CR65 publication-title: Comput Stat Data Anal doi: 10.1016/S0167-9473(01)00065-2 – volume: 67 start-page: 93 year: 2012 ident: 9608_CR60 publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2011.11.002 – ident: 9608_CR34 doi: 10.1109/ASMC.2017.7969228 – volume: 59 start-page: 675 year: 2021 ident: 9608_CR50 publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2021.04.007 – volume: 43 start-page: 7768 issue: 10 year: 2017 ident: 9608_CR5 publication-title: Ceram Int doi: 10.1016/j.ceramint.2017.03.085 – ident: 9608_CR41 doi: 10.1088/1757-899X/272/1/012017 – volume: 143 start-page: 172 year: 2018 ident: 9608_CR7 publication-title: Compos B Eng doi: 10.1016/j.compositesb.2018.02.012 – ident: 9608_CR55 doi: 10.1561/9781601984616 – volume: 7 start-page: 42 issue: 1 year: 2005 ident: 9608_CR17 publication-title: J Manuf Process doi: 10.1016/S1526-6125(05)70080-3 – ident: 9608_CR33 doi: 10.1201/b11022 – ident: 9608_CR49 doi: 10.1007/s10845-022-01933-0 – ident: 9608_CR54 doi: 10.1017/CBO9780511804441 – volume: 20 start-page: 126 year: 2018 ident: 9608_CR44 publication-title: Internal surface measurement of metal powder bed fusion parts
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Snippet	Metal additive manufacturing (AM) has become popular in a large variety of applications due to its excellent capabilities of handling complex geometries and...
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Title	Hybrid data-driven feature extraction-enabled surface modeling for metal additive manufacturing
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