Abstract
The manufacturing error is a very important factor that influences the quality of workpieces, which will obviously reduce the manufacturing accuracy of the workpieces. Excessive manufacturing errors may even cause the workpieces to be scrapped and seriously affect the manufacturing efficiency and benefits.
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Yang MY, Choi JG (1998) Tool deflection compensation system for end milling accuracy improvement. J Manuf Sci Eng 120(2):222–229
Johnstone S, Peyton AJ (2001) The application of parametric 3D finite element modeling techniques to evaluate the performance of a magnetic sensor system. Sensors Actuat 93(2):109–116
Lee DM, Choi SG (2004) Application of on-line adaptable Neural Network for the rolling force set-up of a plate mill. Eng Appl Artif Intell 17(5):557–565
Watanabe T, Iwai S (2006) A control system to improve the accuracy of finished surfaces in milling. J Dyn Syst Meas Contr 105(3):192–199
Liang HB, Li X (2009) A 5-axis milling system based on a New G code for NURBS surface. In: IEEE international conference on intelligent computing and intelligent systems, pp 600–603
Yuan G (2010) Online detecting system of roller wear based on laser-linear array CCD technology. Int Soc Opt Eng 76(1):467–479
Chen LX (2018) Online real-time control method for product manufacturing process. U.S. Patent No. 9891614
Available: introducing the latest in high-definition, non-contact metrology Shapix 1500 series. http://www.coherix.com
Du S, Liu C, Huang D (2015) A shearlet-based separation method of 3D engineering surface using high definition metrology. Precis Eng 40:55–73
Du S, Liu C, Xi L (2015) A selective multiclass support vector machine ensemble classifier for engineering surface classification using high definition metrology. J Manuf Sci Eng 137(1):011003-1-15
Du SC, Huang DL, Wang H (2015) An adaptive support vector machine-based workpiece surface classification system using high-definition metrology. IEEE Trans Instrum Meas 64(10):2590–2604
Du S, Fei L (2016) Co-kriging method for form error estimation incorporating condition variable measurements. J Manuf Sci Eng 138(4):041003-1-16
Wang M, Ken T, Du S, Xi L (2015) Tool wear monitoring of wiper inserts in multi-insert face milling using three-dimensional surface form indicators. J Manuf Sci Eng 137(3):031006-1-8
Wang M, Shao YP, Du SC, Xi LF (2015) A diffusion filter for discontinuous surface measured by high definition metrology. Int J Prec Eng Manuf 16(10):2057–2062
Wang M, Xi L, Du S (2014) 3D surface form error evaluation using high definition metrology. Prec Eng 38(1):230–236
Suriano S, Wang H, Shao C, Hu SJ, Sekhar P (2015) Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations. IIE Trans (ahead-of-print), pp 1–20
Nguyen HT, Wang H, Tai BL, Ren J, Hu SJ, Shih A (2016) High-definition metrology enabled surface variation control by cutting load balancing. J Manuf Sci Eng 138(2):021010-1-11
Cho H, Luck R, Stevens JW (2015) An improvement on the standard linear uncertainty quantification using a least-squares method. J Uncert Anal Appl 3(1):1–13
Hongn M, Larsen SF, Gea M, Altamirano M (2015) Least square based method for the estimation of the optical end loss of linear Fresnel concentrators. Sol Energy 111:264–276
Anselone P, Rall L (1968) The solution of characteristic value-vector problems by Newton’s method. Numer Math 11(1):38–45
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Kim T, Im YJ (2003) Automatic satellite image registration by combination of matching and random sample consensus. IEEE Trans Geosci Remote Sens 41(5):1111–1117
Yaniv Z (2010) Random sample consensus (RANSAC) algorithm, a generic implementation. Imaging
Raguram R, Chum O, Pollefeys M, Matas J, Frahm J (2013) Usac: a universal framework for random sample consensus. IEEE Trans Pattern Anal Mach Intell 35(8):2022–2038
Leon SJ, Björck Å, Gander W (2013) Gram-schmidt orthogonalization: 100 years and more. Numer Linear Algebra Appl 20(3):492–532
Pomerleau F, Colas F, Siegwart R, Magnenat S (2013) Comparing ICP variants on real-world data sets. Auton Robots 34(3):133–148
Di Maio F, Bandini A, Zio E, Alfonsi A, Rabiti C (2016) An approach based on support vector machines and a K-D Tree search algorithm for identification of the failure domain and safest operating conditions in nuclear systems. Prog Nucl Energy 88:297–309
Schauer J, Nüchter A (2014) Efficient point cloud collision detection and analysis in a tunnel environment using kinematic laser scanning and KD tree search. Int Arch Photogramm Remote Sens Spatial Inf Sci 40(3):289–295
Besl PJ, McKay HD (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256
Du S, Xi L (2011) Fault diagnosis in assembly processes based on engineering-driven rules and PSOSAEN algorithm. Comput Ind Eng 60(1):77–88
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intelligence 1(1):33–57
Coello CAC, LechugaMS (2002) MOPSO: a proposal for multiple objective particle Sswarm optimization. In: IEEE Proceedings of the 2002 congress on evolutionary computation, pp 1051–1056
Reyes-Sierra M, Coello CC (2006) Multi-objective particle swarm optimizers: a survey of the state-of-the-art. Int J Comput Intell Res 2(3):287–308
Zhang Y, Gong D, Zhang J (2013) Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103:172–185
Halcon Solution Guide III-C 3D Vision. http://download.mvtec.com/halcon-9.0-solution-guide-iii-c-3d-vision.pdf
Dorsch R, Häusler G, Herrmann J (1994) Laser triangulation: fundamental uncertainty in distance measurement. Appl Opt 33(7):1306–1314
Huang DL, Du SC, Li GL, Wu ZQ (2017) A Systemic approach for on-line minimizing volume difference of multiple chambers with casting surfaces in machining processes based on high definition metrology. J Manuf Sci Eng 139(8):081003-1-17
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Du, S., Xi, L. (2019). Online Compensation Manufacturing. In: High Definition Metrology Based Surface Quality Control and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-15-0279-8_8
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DOI: https://doi.org/10.1007/978-981-15-0279-8_8
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