[1] [1] Jabbar A, Jamshed M A, Abbasi Q, Imran M A and Ur-Rehman M 2023 Leveraging the role of dynamic reconfigurable antennas in viewpoint of industry 4.0 and beyond Research6 0110

[2] [2] Soori M and Asmael M 2023 Minimization of deflection error in five axis milling of impeller blades Facta Univ. Ser.: Mech. Eng.21 175–90

[3] [3] Yan B L, Hao Y P, Zhu L D and Liu C F 2022 Towards high milling accuracy of turbine blades: a review Mech. Syst. Signal Process.170 108727

[4] [4] Huang F Q and Jin X L 2021 Surface texture generation using high-feed milling with spindle speed modulation Precis. Eng.72 13–24

[5] [5] Quintana G and Ciurana J 2011 Chatter in machining processes: a review Int. J. Mach. Tools Manuf.51 363–76

[6] [6] Hao Y P, Zhu L D, Yan B L, Qin S Q, Cui D Y and Lu H 2022 Milling chatter detection with WPD and power entropy for Ti-6Al-4V thin-walled parts based on multi-source signals fusion Mech. Syst. Signal Process.177 109225

[7] [7] Marousi M, Rimpault X, Turenne S and Balazinski M 2023 Initial tool wear and process monitoring during titanium metal matrix composite machining (TiMMC) J. Manuf. Process.86 208–20

[8] [8] Wang R Q, Song Q H, Peng Y Z, Qin J, Liu Z Q and Liu Z J 2023 Self-adaptive fusion of local-temporal features for tool condition monitoring: a human experience free model Mech. Syst. Signal Process.195 110310

[9] [9] Sepahi-Boroujeni S, Mayer J R R and Khameneifar F 2020 Repeatability of on-machine probing by a five-axis machine tool Int. J. Mach. Tools Manuf.152 103544

[10] [10] Lv Z H 2023 Digital twins in industry 5.0 Research6 0071

[11] [11] Zhuang Q X, Wan N, Guo Y H, Zhu G X and Qian D 2024 A state-of-the-art review on the research and application of on-machine measurement with a touch-trigger probe Measurement224 113923

[12] [12] Ding D W, Ding W F, Huang R, Fu Y C and Xu F Y 2023 Research progress of laser triangulation on-machine measurement technology for complex surface: a review Measurement216 113001

[13] [13] Wang W Z, He Y H, Liao R Y, Cai Y Q, Zheng X and Zhao Y 2022 Functional healthy state evaluation approach for manufacturing systems considering imperfect inspection based on extended stochastic flow network J. Manuf. Syst.64 170–80

[14] [14] Zhu Q Y, Zhang Y X, Luan J B and Hu L H 2022 A machine vision development framework for product appearance quality inspection Appl. Sci.12 11565

[15] [15] Zhao W Z, Liu Y H, Li Y Z, Hu C W and Sun R 2023 Multi-robot coverage path planning for dimensional inspection of large free-form surfaces based on hierarchical optimization Int. J. Adv. Manuf. Technol.127 5471–86

[16] [16] Zhang B Y, Feng P F, Sun Z, Cheng X, Zeng L and Fan C R 2023 Efficient sampling method based on co-kriging for free-form surface measurement Precis. Eng.84 136–47

[17] [17] Yin Z Y, Liu H A, Zhao L J, Cheng J, Tan C, Li X T, Chen Y K, Lin Z Y and Chen M J 2023 Efficient and precise detection for surface flaws on large-aperture optics based on machine vision and machine learning Opt. Laser Technol.159 109011

[18] [18] Yan Y C, He G Y, Sang Y C, Yao C L, Wang S T and Chen F Z 2022 A two-module automated scanning inspection planning methodology for complex surfaces on coordinate measuring machine Measurement202 111827

[19] [19] Abdulhameed O, Mian S H, Al-Ahmari A and Alkhalefah H 2020 Patch and curvature specific estimation of efficient sampling scheme for complex surface inspection Int. J. Adv. Manuf. Technol.110 3407–22

[20] [20] Badar M A, Raman S and Pulat P S 2005 Experimental verification of manufacturing error pattern and its utilization in form tolerance sampling Int. J. Mach. Tools Manuf.45 63–73

[21] [21] Obeidat S M and Raman S 2011 Process-guided coordinate sampling of end-milled flat plates Int. J. Adv. Manuf. Technol.53 979–91

[22] [22] Obeidat S M and Raman S 2009 An intelligent sampling method for inspecting free-form surfaces Int. J. Adv. Manuf. Technol.40 1125–36

[23] [23] Zhang K D, Zhao B L, Zhang W H, Wu X Q and Chen Y H 2022 Efficient adaptive surface measurement based on multilevel gaussian process modeling and scanning path optimization IEEE Trans. Instrum. Meas.71 1005111

[24] [24] He G Y, Sang Y C, Pang K R and Sun G M 2018 An improved adaptive sampling strategy for freeform surface inspection on CMM Int. J. Adv. Manuf. Technol.96 1521–35

[25] [25] Guo F Y, Zhang X D, Zou F, Liu J H and Wang Z Q 2019 An adaptive sampling methodology for measuring blades with CMM based on dominant feature points Meas. Sci. Technol.30 045007

[26] [26] Cheng X, Liu X P, Feng P F, Zeng L, Jiang H Y, Sun Z and Zhang S Q 2022 Efficient adaptive sampling methods based on deviation analysis for on-machine inspection Measurement188 110497

[27] [27] Zhao Z C, Li Y and Fu Y C 2022 Collision-free path planning for efficient inspection of free-form surface by using a trigger probe Int. J. Adv. Manuf. Technol.120 2183–200

[28] [28] Mazur T, Rucki M and Gutsalenko Y 2023 Accuracy analysis of the curved profile measurement with CMM: a case study Facta Univ. Ser.: Mech. Eng.21 121–35

[29] [29] Shen Y J, Ren J J, Huang N D, Zhang Y, Zhang X Q and Zhu L M 2023 Surface form inspection with contact coordinate measurement: a review Int. J. Extrem. Manuf.5 022006

[30] [30] Qin S Q, Hao Y P, Zhu L D, Wiercigroch M, Yuan Z H, Shi C L and Cui D Y 2023 CWE identification and cutting force prediction in ball-end milling process Int. J. Mech. Sci.239 107863

[31] [31] Hao Y P, Zhu L D, Yan B L, Ren T Y, Zhao J Z, Ning X F and Lu H 2023 Stiffness design and multi-objective optimization of machine tool structure based on biological inspiration J. Vib. Control29 2774–88

[32] [32] Sun Y, Jin L Y, Gong Y D, Wen X L, Yin G Q, Wen Q and Tang B J 2022 Experimental evaluation of surface generation and force time-varying characteristics of curvilinear grooved micro end mills fabricated by EDM J. Manuf. Process.73 799–814

[33] [33] Wang C Y, Miao H H, Li C Y, Li J Q, Xu M T and Liu Z D 2023 Effect of cutting vibration on the surface quality of commercially pure titanium (TA2) based on rotor dynamics model analysis Int. J. Mech. Mater. Des.19 241–60

[34] [34] Wang X J and Song Q H 2023 Dynamic analysis and surface morphology prediction for deep cavity turning of bent-blade cutter Int. J. Adv. Manuf. Technol.129 4435–55

[35] [35] Phan N D M, Quinsat Y and Lartigue C 2019 Optimal scanning strategy for on-machine inspection with laser-plane sensor Int. J. Adv. Manuf. Technol.103 4563–76

[36] [36] Lim H S, Son S M, Wong Y S and Rahman M 2007 Development and evaluation of an on-machine optical measurement device Int. J. Mach. Tools Manuf.47 1556–62

[37] [37] Guiassa R and Mayer J R R 2011 Predictive compliance based model for compensation in multi-pass milling by on-machine probing CIRP Ann.60 391–4

[38] [38] Li S M, Zeng L, Feng P F, Li Y M, Xu C and Ma Y 2019 Accurate compensation method for probe pre-travel errors in on-machine inspections Int. J. Adv. Manuf. Technol.103 2401–10

[39] [39] Wang H and Jiang X G 2022 Geometric error identification of five-axis machine tools using dual quaternion Int. J. Mech. Sci.229 107522

[40] [40] Wang L P, Kong X Y, Yu G, Li W T, Li M Y and Jiang A B 2022 Error estimation and cross-coupled control based on a novel tool pose representation method of a five-axis hybrid machine tool Int. J. Mach. Tools Manuf.182 103955

[41] [41] Onishi S, Ibaraki S, Kato T, Yamaguchi M and Sugimoto T 2022 A self-calibration scheme to monitor long-term changes in linear and rotary axis geometric errors Measurement196 111183

[42] [42] Osei S, Wang W and Ding Q C 2023 A new method to identify the position-independent geometric errors in the rotary axes of five-axis machine tools J. Manuf. Process.87 46–53

[43] [43] Guo X P, Yin S H, Shu C S, Wu S H and Liu J 2023 Contact on-machine measurement probe error correction method for optical aspheric surface ultraprecision machining Measurement214 112731

[44] [44] Li S M, Feng P F, Zeng L, Wang S, Li Y M and Zhang S Q 2021 A rigid-flexible coupling pretravel error model for a touch-trigger probe in an on-machine inspection system Int. J. Adv. Manuf. Technol.114 2991–3003

[45] [45] Rpalska M and Woniak A 2022 The share of the probe errors in on-machine measurements Precis. Eng.75 111–9

[46] [46] Woniak A and Mczyska K 2020 Measurement hysteresis of touch-trigger probes for CNC machine tools Measurement156 107568

[47] [47] Papananias M, McLeay T E, Mahfouf M and Kadirkamanathan V 2022 A Bayesian information fusion approach for end product quality estimation using machine learning and on-machine probing J. Manuf. Process.76 475–85

[48] [48] Sepahi-Boroujeni S, Mayer J R R, Khameneifar F and Woniak A 2021 A full-covariance uncertainty assessment in on-machine probing Int. J. Mach. Tools Manuf.167 103768

[49] [49] El Ghazouali S, Vissiere A, Lafon L F, Bouazizi M L and Nouira H 2022 Optimised calibration of machine vision system for close range photogrammetry based on machine learning J. King Saud Univ.—Comput. Inf. Sci.34 7406–18

[50] [50] Wang S and Zhao Q L 2022 Development of an on-machine measurement system with chromatic confocal probe for measuring the profile error of off-axis biconical free-form optics in ultra-precision grinding Measurement202 111825

[51] [51] Xiao Y K, Ge G Y, Feng X B, Yang J G and Du Z C 2022 Analysis and compensation of surface flatness of thin-walled valve body parts Int. J. Adv. Manuf. Technol.123 1679–93

[52] [52] Iglesias I, Sanchez A and Silva F J G 2024 Robotic path compensation training method for optimizing face milling operations based on non-contact CMM techniques Robot. Comput.-Integr. Manuf.85 102623

[53] [53] Zhang Y Q, Shen Y J, Zhang W Z, Zhang L, Zhu L M and Zhang Y 2022 Generation of efficient and interference-free scanning path for inspecting impeller on a cylindrical CMM Measurement198 111352

[54] [54] Irino N, Kobayashi A, Shinba Y, Kawai K, Spescha D and Wegener K 2023 Digital twin based accuracy compensation CIRP Ann.72 345–8

[55] [55] Osei S, Wei W, Yu J H and Ding Q C 2023 A new effective decoupling method to identify the tracking errors of the motion axes of the five-axis machine tools J. Intell. Manuf. 1–16

[56] [56] Maged A and Xie M 2022 Uncertainty utilization in fault detection using Bayesian deep learning J. Manuf. Syst.64 316–29

[57] [57] Yoon S, Kim A, Cantwell W J, Yeun C Y, Cho C-S, Byon Y-J and Kim T-Y 2023 Defect detection in composites by deep learning using solitary waves Int. J. Mech. Sci.239 107882

[58] [58] He C L, Yan J W, Wang S Q, Zhang S, Chen G and Ren C Z 2023 A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials Int. J. Extrem. Manuf.5 035102

[59] [59] Li Y M, Zeng L, Tang K and Li S M 2019 A dynamic pre-travel error prediction model for the kinematic touch trigger probe Measurement146 689–704

[60] [60] Cheng M H, Jiao L, Yan P, Jiang H S, Wang R B, Qiu T Y and Wang X B 2022 Intelligent tool wear monitoring and multi-step prediction based on deep learning model J. Manuf. Syst.62 286–300

[61] [61] Mirjalili S, Mirjalili S M and Hatamlou A 2016 Multi-verse optimizer: a nature-inspired algorithm for global optimization Neural Comput. Appl.27 495–513

[62] [62] Mirjalili S, Jangir P, Mirjalili S Z, Saremi S and Trivedi I N 2017 Optimization of problems with multiple objectives using the multi-verse optimization algorithm Knowl.-Based Syst.134 50–71

[63] [63] Zhu L D, Yan B L, Wang Y L, Dun Y C, Ma J and Li C L 2021 Inspection of blade profile and machining deviation analysis based on sample points optimization and NURBS knot insertion Thin-Walled Struct.162 107540

[64] [64] Borr A, Seman L O, Camponogara E, Stefenon S F, Mariani V C and Dos Santos Coelho L 2023 Machine fault detection using a hybrid CNN-LSTM attention-based model Sensors23 4512

[65] [65] Deng F Y, Bi Y, Liu Y Q and Yang S P 2022 Remaining useful life prediction of machinery: a new multiscale temporal convolutional network framework IEEE Trans. Instrum. Meas.71 2516913

[66] [66] Marei M and Li W D 2022 Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning Int. J. Adv. Manuf. Technol.118 817–36

[67] [67] Zhang X Y, Lu X, Li W D and Wang S 2021 Prediction of the remaining useful life of cutting tool using the Hurst exponent and CNN-LSTM Int. J. Adv. Manuf. Technol.112 2277–99

[68] [68] Li B H, Lu Z H, Jin X L and Zhao L P 2023 Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM J. Intell. Manuf.35 2547–66