[1] Moon S K, Tan Y E, Hwang J and Yoon Y J 2014 Application of 3D printing technology for designing light-weight unmanned aerial vehicle wing structures Int. J. Precis. Eng. Manuf. Green Technol. 1 223–8

[2] Frazier W E 2014 Metal additive manufacturing: a review J. Mater. Eng. Perform. 23 1917–28

[3] Zhu J Q, Ling Z M, Du F R, Ding X P and Li H M 2018 Monitoring of laser metal-wire additive manufacturing temperature field using infrared thermography Infrared Laser Eng. 47 0604002

[4] Berumen S, Bechmann F, Lindner S, Kruth J P and Craeghs T 2010 Quality control of laser- and powder bed-based Additive Manufacturing (AM) technologies Phys. Proc. 5 617–22

[5] Han C J, Li Y, Wang Q, Cai D S, Wei Q S, Yang L, Wen S F, Liu J and Shi Y S 2018 Titanium/hydroxyapatite (Ti/HA) gradient materials with quasi-continuous ratios fabricated by SLM: material interface and fracture toughness Mater. Des. 141 256–66

[6] ASTM-F42-Committee 2012 Standard Terminology for Additive Manufacturing Technologies pp 1–3 (West Conshohocken, PA: ASTM International)

[7] Vaezi M, Chianrabutra S, Mellor B and Yang S F 2013 Multiple material additive manufacturing-part 1: a review Virtual Phys. Prototype 8 19–50

[8] Leach R K, Bourell D, Carmignato S, Donmez A, Senin N and Dewulf W 2019 Geometrical metrology for metal additive manufacturing CIRP Ann. 68 677–700

[9] Craeghs T, Bechmann F, Berumen S and Kruth J P 2010 Feedback control of layerwise laser melting using optical sensors Phys. Proc. 5 505–14

[10] Liu Y T and Zhang Y Z 2020 Microstructure and mechanical properties of TA15-Ti2AlNb bimetallic structures by laser additive manufacturing Mater. Sci. Eng. A 795 140019

[11] Oliveira J P, LaLonde A D and Ma J 2020 Processing parameters in laser powder bed fusion metal additive manufacturing Mater. Des. 193 108762

[12] Kruth J P, Mercelis P, Van Vaerenbergh J and Craeghs T 2008 Feedback control of selective laser melting Proc. 3rd Int. Conf. on Advanced Research in Virtual and Rapid Prototyping pp 521–8 (https://lirias.kuleuven.be/handle/ 123456789/185342)

[13] Grasso M and Colosimo B M 2017 Process defects and in situ monitoring methods in metal powder bed fusion: a review Meas. Sci. Technol. 28 044005

[14] Charalampous P, Kostavelis I and Tzovaras D 2020 Non-destructive quality control methods in additive manufacturing: a survey Rapid Proto. J. 26 777–90

[15] Thompson S M, Bian L, Shamsaei N and Yadollahi A 2015 An overview of Direct Laser Deposition for additive manufacturing; part I: transport phenomena, modeling and diagnostics Addit. Manuf. 8 36–62

[16] Tan H, Fang Y B, Zhong C L, Yuan Z H, Fan W, Li Z, Chen J and Lin X 2020 Investigation of heating behavior of laser beam on powder stream in directed energy deposition Surf. Coat. Technol. 397 126061

[17] Pyka G, Kerckhofs G, Papantoniou I, Speirs M, Schrooten J and Wevers M 2013 Surface roughness and morphology customization of additive manufactured open porous Ti6Al4V structures Materials 6 4737–57

[18] Turner B N and Gold S A 2015 A review of melt extrusion additive manufacturing processes: II. Materials, dimensional accuracy, and surface roughness Rapid Proto. J. 21 250–61

[19] Gisario A, Kazarian M, Martina F and Mehrpouya M 2019 Metal additive manufacturing in the commercial aviation industry: a review J. Manuf. Syst. 53 124–49

[20] Leung C L A, Marussi S, Towrie M, Atwood R C, Withers P J and Lee P D 2019 The effect of powder oxidation on defect formation in laser additive manufacturing Acta Mater. 166 294–305

[21] Cerniglia D and Montinaro N 2018 Defect detection in additively manufactured components: laser ultrasound and laser thermography comparison Proc. Struct. Integr. 8 154–62

[22] Tino R, Leary M, Yeo A, Kyriakou E, Kron T and Brandt M 2020 Additive manufacturing in radiation oncology: a review of clinical practice, emerging trends and research opportunities Int. J. Extrem. Manuf. 2 012003

[23] Varela J, Merino J, Pickett C, Abu-Issa A, Arrieta E, Murr L E, Wicker R B, Ahlfors M, Godfrey D and Medina F 2020 Performance characterization of laser powder bed fusion fabricated Inconel 718 treated with experimental hot isostatic processing cycles J. Manuf. Mater. Process. 4 73

[24] Di Angelo L, Di Stefano P and Guardiani E 2020 Search for the optimal build direction in additive manufacturing technologies: a review J. Manuf. Mater. Process. 4 71

[25] Peng X, Kong L B, Chen Y, Wang J H and Xu M 2019 A preliminary study of in-situ defects measurement for additive manufacturing based on multi-spectrum Proc. SPIE 10842 1084217

[26] Bahnini I, Rivette M, Rechia A, Siadat A and Elmesbahi A 2018 Additive manufacturing technology: the status, applications, and prospects Int. J. Adv. Manuf. Technol. 97 147–61

[27] Stavroulakis P I and Leach R K 2016 Invited review article: review of post-process optical form metrology for industrial-grade metal additive manufactured parts Rev. Sci. Instrum. 87 041101

[28] Gao F, Zhou H and Huang C 2020 Defect detection using the phased-array laser ultrasonic crack diffraction enhancement method Opt. Commun. 474 126070

[29] Kang D H, Benipal S S, Gopal D L and Cha Y J 2020 Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning Autom. Constr. 118 103291

[30] Tian L, Fan Y, Li L and Mousseau N 2020 Identifying flow defects in amorphous alloys using machine learning outlier detection methods Scr. Mater. 186 185–9

[31] Bernhard R, Neef P, Wiche C, Hoff C, Hermsdorf J, Kaierle S and Wesling V 2010 Defect detection in additive manufacturing via a toolpath overlaid melt-pool-temperature tomography J. Laser Appl. 32 022055

[32] Chen D B, Cao P and Deng Q L 2014 Research on measurement of the molten pool image during laser cladding process Electromach. Mould. 6 45–49

[33] Tan H, Chen J, Zhang F Y, Lin X and Huang W D 2010 Estimation of laser solid forming process based on temperature measurement Opt. Laser Technol. 42 47–54

[34] Baumgartl H, Tomas J, Buettner R and Merkel M 2020 A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring Prog. Addit. Manuf. 5 277–85

[35] Lopez A, Bacelar R, Pires I, Santos T G, Sousa J P and Quintino L 2018 Non-destructive testing application of radiography and ultrasound for wire and arc additive manufacturing Addit. Manuf. 21 298–306

[36] Rieder H, Dillh?efer A, Spies M, Bamberg J and Hess T 2014 Online monitoring of additive manufacturing processes using ultrasound Proc. 11th European Conf. on Non-Destructive Testing (ECNDT 2014) (Prague, Czech Republic)

[37] Cerniglia D, Scafidi M, Pantano A and Rudlin J 2015 Inspection of additive-manufactured layered components Ultrasonics 62 292–8

[38] Zhang X, Hao Y W, Shangguan H, Zhang P C and Wang A H 2020 Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks Infrared Phys. Technol. 108 103334

[39] Yao B, Imani F, Sakpal A S, Reutzel E W and Yang H 2018 Multifractal analysis of image profiles for the characterization and detection of defects in additive manufacturing J. Manuf. Sci. Eng. 140 031014

[40] Grasso M, Laguzza V, Semeraro Q and Colosimo B M 2017 In-process monitoring of selective laser melting: spatial detection of defects via image data analysis J. Manuf. Sci. Eng. 139 051001

[41] Abdelrahman M, Reutzel E W, Nassar A R and Starr T L 2017 Flaw detection in powder bed fusion using optical imaging Addit. Manuf. 15 1–11

[42] Malekipour E and El-Mounayri H 2018 Common defects and contributing parameters in powder bed fusion AM process and their classification for online monitoring and control: a review Int. J. Adv. Manuf. Technol. 95 527–50

[43] Yao Y S, Wang J, Chen Q B, Ding C, Tang J P and Ge Z S 2019 Research status of defects and defect treatment technology for laser additive manufactured products Laser Optoelect. Prog. 56 100004

[44] Harrison N J, Todd I and Mumtaz K 2015 Reduction of micro-cracking in nickel superalloys processed by selective laser melting: a fundamental alloy design approach Acta Mater. 94 59–68

[45] Carter L N, Attallah M M and Reed R C 2012 Laser powder bed fabrication of nickel-base superalloys: influence of parameters; characterisation, quantification and mitigation of cracking Superalloys 2012 ed E S Huron, R C Reed, M C Hardy, M J Mills, R E Montero, P D Portella and J Telesman (Pittsburgh, PA: The Minerals, Metals, & Materials Society) pp 577–86

[46] Carter L N, Martin C, Withers P J and Attallah M M 2016 The influence of the laser scan strategy on grain structure and cracking behaviour in SLM powder-bed fabricated nickel superalloy J. Alloys Compd. 12 240–51

[47] Montazeri M and Ghaini F M 2012 The liquation cracking behavior of IN738LC superalloy during low power Nd:YAG pulsed laser welding Mater. Charact. 67 65–73

[48] Ojo O A 2007 Intergranular liquation cracking in heat affected zone of a welded nickel based superalloy in as cast condition Mater. Sci. Technol. 23 1149–55

[49] Dye D, Hunziker O and Reed R C 2001 Numerical analysis of the weldability of superalloys Acta Mater. 49 683–97

[50] Demir A G and Previtali B 2017 Investigation of remelting and preheating in SLM of 18Ni300 maraging steel as corrective and preventive measures for porosity reduction Int. J. Adv. Manuf. Technol. 93 2697–709

[51] Gu D D, Hagedorn Y C, Meiners W, Meng G B, Batista R J S, Wissenbach K and Poprawe R 2012 Densification behavior, microstructure evolution, and wear performance of selective laser melting processed commercially pure titanium Acta Mater. 60 3849–60

[52] Zhong M L, Sun H Q, Liu W J, Zhu X F and He J J 2005 Boundary liquation and interface cracking characterization in laser deposition of Inconel 738 on directionally solidified Ni-based superalloy Scr. Mater. 53 159–64

[53] Mercelis P and Kruth J P 2006 Residual stresses in selective laser sintering and selective laser melting Rapid Proto. J. 12 254–65

[54] Kruth J P, Froyen L, Van Vaerenbergh J, Mercelis P, Rombouts M and Lauwers B 2004 Selective laser melting of iron-based powder J. Mater. Process. Technol. 149 616–22

[55] Azarmi F and Sevostianov I 2020 Evaluation of the residual stresses in metallic materials produced by additive manufacturing technology: effect of microstructure Curr. Opin. Chem. Eng. 28 21–27

[56] Vrancken B, Cain V, Knutsen R and Van Humbeeck J 2014 Residual stress via the contour method in compact tension specimens produced via selective laser melting Scr. Mater. 87 29–32

[57] Aslani K E, Chaidas D, Kechagias J, Kyratsis P and Salonitis K 2020 Quality performance evaluation of thin walled PLA 3D printed parts using the Taguchi method and grey relational analysis J. Manuf. Mater. Process. 4 47

[58] Yuan D, Gao H B, Sun X J, Zhou C P, Sun L B, Chen Y J, Guo C H, Niu Z Y and Jiang F C 2018 Methods and techniques for improving microstructure and performance of metal additively manufactured materials Aeronaut. Manuf. Technol. 61 40–48

[59] Edwards P, O’Conner A and Ramulu M 2013 Electron beam additive manufacturing of titanium components: properties and performance J. Manuf. Sci. Eng. 135 061016

[60] Gu H, Gong H, Pal D, Rafi K, Starr T and Stucker B 2013 Influences of energy density on porosity and microstructure of selective laser melted 17-4PH stainless steel Proc. 24th Annual Int. Solid Freeform Fabrication Symp. pp 474–89

[61] Weingarten C, Buchbinder D, Pirch N, Meiners W, Wissenbach K and Poprawe R 2015 Formation and reduction of hydrogen porosity during selective laser melting of AlSi10Mg J. Mater. Process. Technol. 221 112–20

[62] Haghighi A and Li L 2020 A hybrid physics-based and data-driven approach for characterizing porosity variation and filament bonding in extrusion-based additive manufacturing Addit. Manuf. 36 101399

[63] Shamir M, Syed A K, Janik V, Biswal R and Zhang X 2020 The role of microstructure and local crystallographic orientation near porosity defects on the high cycle fatigue life of an additive manufactured Ti-6Al-4V Mater. Charact. 169 110576

[64] Günther J, Krewerth D, Lippmann T, Leuders S, Tr?ster T, Weidner A, Biermann H and Niendorf T 2017 Fatigue life of additively manufactured Ti–6Al–4V in the very high cycle fatigue regime Int. J. Fatigue 94 236–45

[65] Zhang P Y, Zhou X, Cheng X, Sun H M, Ma H Q and Li Y H 2020 Elucidation of bubble evolution and defect formation in directed energy deposition based on direct observation Addit. Manuf. 32 101026

[66] Rane K, Petr`o S and Strano M 2020 Evolution of porosity and geometrical quality through the ceramic extrusion additive manufacturing process stages Addit. Manuf. 32 101038

[67] Zhao T et al 2020 Laser metal deposition for additive manufacturing of AA5024 and nanoparticulate TiC modified AA5024 alloy composites prepared with balling milling process Opt. Laser Technol. 131 106438

[68] Li R D, Liu J H, Shi Y S, Wang L and Jiang W 2012 Balling behavior of stainless steel and nickel powder during selective laser melting process Int. J. Adv. Manuf. Technol. 59 1025–35

[69] Tolochko N K, Mozzharov S E, Yadroitsev I A, Laoui T, Froyen L, Titov V I and Ignatiev M B 2004 Balling processes during selective laser treatment of powders Rapid Prototyp. J. 10 78–87

[70] Qiu Y D, Wu J M, Chen A N, Chen P, Yang Y, Liu R Z, Chen G, Chen S, Shi Y S, Li C H 2020 Balling phenomenon and cracks in alumina ceramics prepared by direct selective laser melting assisted with pressure treatment Ceram. Int. 46 13854–61

[71] Wilbig J, de Oliveira F B, Obaton A F, Schwentenwein M, Rübner K and Gunster J 2020 Defect detection in additively manufactured lattices Open Ceram. 3 10020

[72] Montinaro N, Cerniglia D and Pitarresi G 2018 Defect detection in additively manufactured titanium prosthesis by flying laser scanning thermography Proced. Struct. Integr. 12 165–72

[73] Zhang B, Li Y and Bai Q 2017 Defect formation mechanisms in selective laser melting: a review Chin. J. Mech. Eng. 30 515–27

[74] Honarvar F and Varvani-Farahani A 2020 A review of ultrasonic testing applications in additive manufacturing: defect evaluation, material characterization, and process control Ultrasonics 108 106227

[75] Schwerdtfeger J, Singer R F and K?rner C 2012 In situ flaw detection by IR-imaging during electron beam melting Rapid Prototyp. J. 18 259–63

[76] Bartlett J L, Heim F M, Murty Y V, Li X D 2018 In situ defect detection in selective laser melting via full-field infrared thermography Addit. Manuf. 24 595–605

[77] Guo C Y, Zhang Y and Pu C H 2018 Combined detection technology for micro leakage of titanium/steel and zirconium/steel clad plate vessel Nondestr. Test. 40 59–61

[78] Waller J M, Saulsberry R L, Parker B H, Hodges K L, Burke E R and Taminger K M 2015 Summary of NDE of additive manufacturing efforts in NASA AIP Conf. Proc. 1 650

[79] Liu Z H, Zhu K W and Zhang W 2020 Eddy current testing of deep-lying defects in paramagnetic metals by flux-gate sensor Nondestr. Test. 42 1–6

[80] Du W, Bai Q, Wang Y B and Zhang B 2018 Eddy current detection of subsurface defects for additive/subtractive hybrid manufacturing Int. J. Adv. Manuf. Technol. 95 3185–95

[81] Zhou Z G and Sun G K 2017 New progress of the study and application of advanced ultrasonic testing technology J. Mech. Eng. 53 1–10

[82] Wang X G, Wu W L, Chen Z L and Wu N X 2017 Air-coupling ultrasonic testing of defects in LY12 duralumin alloys China Mech. Eng. 28 2582–7

[83] Millon C, Vanhoye A, Obaton A F and Penot J D 2018 Development of laser ultrasonics inspection for online monitoring of additive manufacturing Weld. World 62 653–61

[84] Yang J, Li S B, Gao Z, Wang Z and Liu W 2018 Real-time recognition method for 0.8 cm darning needles and KR22 bearings based on convolution neural networks and data increase Appl. Sci. 8 1857

[85] Yang G C, Yang J, Sheng W H, Junior F E F and Li S B 2018 Convolutional neural network-based embarrassing situation detection under camera for social robot in smart homes Sensors 18 1530

[86] Qian X L, Zhang H Q, Zhang H L, He Z D and Yang C X 2017 Solar cell surface defect detection based on visual saliency Chin. J. Sci. Instrum. 38 1570–8

[87] Tao X, Wang Z H, Zhang Z T, Zhang D P, Xu D, Gong X Y and Zhang L 2018 Wire defect recognition of spring-wire socket using multitask convolutional neural networks IEEE Trans. Compon. Pack. Manuf. Technol. 8 689–98

[88] Cheng J C P and Wang M Z 2018 Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques Autom. Constr. 95 155–71

[89] Du X L, Chen Z G and Xu X 2020 Method of improved deep wavelet auto-encoder in bearing fault diagnosis J. Comput. Eng. Appl. 56 263–9

[90] Yang J and Yang G C 2018 Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer Algorithms 11 28

[91] Lin J H, Yao Y, Ma L and Wang Y J 2018 Detection of a casting defect tracked by deep convolution neural network Int. J. Adv. Manuf. Technol. 97 573–81

[92] Yuan Z C, Zhang Z T, Su H, Zhang L, Shen F and Zhang F 2018 Vision-based defect detection for mobile phone cover glass using deep neural networks Int. J. Precis. Eng. Manuf. 19 801–10

[93] Xie Z, Wang L, Yin X Y and Yin G F 2020 Classification and recognition method of sheet metal parts surface defects based on convolution neural network Comput. Meas. Control 28 187–190, 196

[94] Lu H, Liu S X, Wei H and Tu J J 2020 Multi-kernel fuzzy clustering based on auto-encoder for fMRI functional network Expert Syst. Appl. 159 113513

[95] Wang Y R, Sun G D and Jin Q 2020 Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network Appl. Soft Comput. 92 106333

[96] Ren Z J, Zhu Y S, Yan K, Chen K D, Kang W, Yue Y and Gao D W 2020 A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis Mech. Syst. Signal Process. 138 106608

[97] Tang Y L, Huang J S, Zhang F E and Gong W G 2020 Deep residual networks with a fully connected reconstruction layer for single image super-resolution Neurocomputing 405 186–99

[98] Che C C, Wang H W and Ni X M 2020 Fault diagnosis of rolling bearing based on deep residual shrinkage network J. Beijing Univer. Aeronaut. Astronaut. 7 1–11

[99] Lei J, Gao X, Feng Z L, Qiu H M and Song M L 2018 Scale insensitive and focus driven mobile screen defect detection in industry Neurocomputing 294 72–81

[100] Zhang L, Wang L and Gao Y 2020 Fault prediction research of high voltage circuit breakers based on LSTM recurrent neural network optimized by CAS J. Chongqing Univ. Technol. (Nat. Sci.) 34 181–7

[101] Wen L W, Song Q H, Qin L H and Xiao J 2015 Defect detection and closed-loop control system for automated fiber placement forming components based on machine vision and UMAC Acta Aeronaut. Astronaut. Sin. 36 3991–4000

[102] Shen H Y, Du W Z, Sun W J, Xu Y T and Fu J Z 2020 Visual detection of surface defects based on selffeature comparison in robot 3D printing Appl. Sci. 10 235

[103] Zhang P 2018 Research of a monitoring system of deposition quality in selective laser melting based on machine vision. (Wuhan: Huazhong Uniersity of Science and Technology). Scime L, Beuth J Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm Addit. Manuf. 19 114–26

[104] Scime L and Beuth J 2018 Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm Addit. Manuf. 19 114–26

[105] Zhang Y J, Soon H G, Ye D S, Fuh J Y H and Zhu K P 2020 Powder-bed fusion process monitoring by machine vision with hybrid convolutional neural networks IEEE Trans. Ind. Inform. 16 5769–79

[106] Shi T Y, Zhou L Z, Wang C M, Mi G Y and Jiang P 2019 Machine vision-based real-time monitor system for laser cleaning aluminum alloy Chin. J. Lasers 46 0402007

[107] Zhou D Q, Wang J, Zhang Q J, Wu J J and Zhang H 2015 Research on sensing mechanism of ferromagnetic component flaw using pulsed eddy current testing Chin. J. Sci. Instrum. 36 989–95

[108] Meng X Z, Li Z W, Zhu W F, He Y L and Lu H Y 2020 Detection of internal defect in metal plate structures using air-coupled ultrasonic Lamb waves J. Appl. Acoust. 39 316–24

[109] Qu E Q, Cui Y J, Xu S and Sun H X 2017 Saliency defect detection in strip steel by improved Gabor filter J. Huazhong Univ. Sci. Technol. (Nat. Sci. Ed.) 45 12–17

[110] Tian Y, Du D, Cai G R, Wang L and Zhang H 2006 Automatic defect detection in x-ray images using image data fusion Tsinghua Sci. Technol. 11 720–4

[111] Liu H and Guo R Y 2018 Detection and identification of SAWH pipe weld defects based on x-ray image and CNN Chin. J. Sci. Instrum. 39 247–56

[112] Lin N C, Yang X X, Lin W J and Zhu Z B 2014 Defect detection of TOFD D scanning image based on parabola fitting Trans. China Weld. Inst. 35 105–8

[113] Chi D Z and Gang T 2015 Defect detection method based on ultrasonic clutter wave suppression Trans. China Weld. Inst. 36 17–20

[114] Zeng W, Wang H T, Tian G Y, Fang L, Wang W, Wan M and Yang X M 2014 Research on laser ultrasonic defect signal detection technology based on energy analysis Chin. J. Sci. Instrum. 35 650–5

[115] Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L 2018 Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs IEEE Trans. Pattern Anal. Mach. Intell. 40 834–48

[116] Redmon J and Farhadi A 2017 YOLO9000: better, faster, stronger Proc. 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) Honolulu, HI (Piscataway, NJ: IEEE) pp 6517–25

[117] Fu C Y, Liu W, Ranga A, Tyagi A and Berg A C 2017 DSSD: deconvolutional single shot detector (arXiv:1701.06659)

[118] Ma L, Lu Y, Jiang H Q and Liu Y M 2020 An automatic small sample learning-based detection method for LCD product defects CAAI Trans. Intell. Syst. 15 560–7

[119] Dai J F, Qi H Z, Xiong Y W, Li Y, Zhang G D, Hu H and Wei Y C 2017 Deformable convolutional networks Proc. 2017 IEEE Int. Conf. on Computer Vision (ICCV) Venice (Piscataway, NJ: IEEE) pp 764–73

[120] Bodla N, Singh B, Chellappa R and Davis L S 2017 Soft-NMS—improving object detection with one line of code Proc. 2017 IEEE Int. Conf. on Computer Vision Venice (Piscataway, NJ: IEEE) pp 5562–70

[121] Liu D, Zhang B, Li H X, Song W H, Li F Y and Yang T D 2018 Detection of micro-cylinder end face defect in complex background Laser Optoelect. Prog. 55 134–40