Abstract
An in-process surface roughness adaptive control (ISRAC) system in end milling operations was researched and developed. A multiple regression algorithm was employed to establish two subsystems: the in-process surface roughness evaluation (ISRE) subsystem and the in-process adaptive parameter control (IAPC) subsystem. These systems included not only machine cutting parameters such as feed rate, spindle speed, and depth of cut, but also cutting force signals detected by a dynamometer sensor. The multiple-regression-based ISRE subsystem predicted surface roughness during the finish cutting process with an accuracy of 91.5%. The integration of the two subsystems led to the ISRAC system. The testing resulted in a 100% success rate for adaptive control, proving that this proposed system could be implemented to adaptively control surface roughness during milling operations. This research suggests that multiple linear regression used in this study was straightforward and effective for in-process adaptive control.
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References
Lee TS, Lin YJ (2000) A 3D predictive cutting force model for end milling of parts having sculptured surfaces. Int J Adv Manuf Technol 16:773–783
Altintas Y (1994) Direct adaptive control of end milling process. Int J Mach Tools Manuf 34(4):461–472
DeGarmo EP, Black JT, Kohser RA (1997) Materials and processes in manufacturing, 8th edn. Prentice Hall, Upper Saddle River, NJ
Babin TS, Lee JM, Sutherland JM, Kapoor SG (1985) A model for end milling surface topography. Proc 13th North American Metalworking Research Conference, pp 362–368
Kline WA, DeVor RE, Shareef IA (1982) The prediction of surface accuracy in end milling. ASME J Eng Ind 115:245–252
Sutherlan JW, DeVor RE (1986) Improved method for cutting force and surface error prediction in flexible end milling systems. J Eng Ind, Transactions ASME, 108(4):269–279
Baek DK, Ko TJ, Kim HS (2001) Optimization of feed rate in a face milling operation using surface roughness model. Int J Mach Tools Manuf 41(3):451–462
Liu J, Yamazaki K, Zhou Y, Matsumiya S (2002) A reflective fiber optic sensor for surface roughness in-process measurement. J Manu Sci Eng-T ASME 124(3):515–522
Susic E, Grabec I (1995) Application of a neural network to the estimation of surface roughness from AE signals generated by friction process. Int J Mach Tools Manuf 35(8):1077–1086
Pepper DM, Dunning GJ, David BG, Pouet B, Klein MB (1999) Real-time laser ultrasonic process control using adaptive photodetectors. International SAMPE Symposium and Exhibition (Proceedings) 44(1):341–348
Coker SA, Oh SJ, Shin YC (1998) In-process monitoring of surface roughness utilizing ultrasound. J Manuf Sci Eng-T ASME 120(1):197–200
Coker SA, Shin YC (1996) In-process control of surface roughness due to tool wear using a new ultrasonic system. Int J Mach Tools Manuf 36(3):411–422
Shin YC, Oh SJ, Coker SA (1995) Surface roughness measurement by ultrasonic sensing for in-process monitoring. J Eng Ind-T ASME 117(3):439–447
Yan D, Kaye JE, Balarkrishnan S, Popplewell N (1996). Surface roughness measurements in finish turning. Int J Adv Manuf Technol 11(2):91–100
Martellotti ME (1941) An analysis of the milling process. Transactions of ASME 63:677–700
Martellotti ME (1945) An analysis of the milling process, part II-Down milling. Transaction of ASME 67:633–251
Melkote SN, Thangaraj AR (1994) An enhanced end milling surface texture model including the effects of radial rake and primary relief angles. ASME J Eng Ind 116(1):166–174
Fuh KH, Wu CF (1995) A proposed statistical model for surface quality prediction in end-milling of Al alloy. Int J Mach Tools Manuf 35(8):1187–1200
Baek DK, Ko TJ, Kim HS (1997) Dynamic surface roughness model for face milling. Precis Eng 20(3):171–178
Huang B, Chen JC (2003) An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Advanced Manuf Technol 21(5):339–347
Zhang GM, Kapoor SG (1991) Dynamic generation of machine surface, part1: Description of a random excitation system and part 2: Construction of surface topography. ASME J Eng Ind 113(3):137–153
Montgomery Y, Altintas Y (1991) Mechanism of cutting force and surface generation in dynamic milling. ASME J Eng Ind 113(1):160–168
Ismail F, Elbestawi MA, Du R, Urbasik K (1993) Generation of milled surfaces including tool dynamics and wear. ASME J Eng Ind 115(3):245–252
Chen JC, Lou MS (2000) Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operations. Int Comput Integrated Manuf 13(4):358–368
Chen JC, Savage M (2001) A fuzzy-net-based multilevel in-process surface roughness prediction system in milling operations. Int J Adv Manuf Technol 17(9):670–676
Chen JC, Lou SJ (1998) Statistical and fuzzy-logic approaches in online surface roughness recognition systems for end-milling operations. Int J Flex Autom Integr Manuf l6:53–78
Honna M (1999) Modeling product quality in a machine center using fuzzy petri nets with neural networks. Proceedings - IEEE Int Conf Robot Autom 2:1502–1507
Stark GA, Moon KS (1999) Modeling surface texture in the peripheral milling processing using neural network, spline, and fractal methods with evidence of chaos. J Manuf Sci Eng 121(2):251–256
Lin JC, Tai CC (1999) Accuracy optimization for mould surface profile milling. Int J Adv Manuf Technol 15(1):15–25
Ko TJ, Cho DW (1998) Adaptive optimization of face milling operations using neural networks. J Manuf Sci Eng 120(2):443–451
Tsai YH, Chen JC, Lou SJ (1999) In-process surface recognition system based on neural networks in end milling cutting operations. Int J Mach Tools Manuf 39(4):583–605
Yang WH, Tarng YS (1998) Design optimization of cutting parameters for turning operations based on the taguchi method. J Mater Process Technol 84(1–3):122–129
Bradley C (2000) Automated surface roughness measurement. Int J Adv Manuf Tech 16(9):668–674
Wang W, Wong PL, Luo JB, Zhang Z (1998) New optical technique for roughness measurement on moving surface. Tribol Int 31(5):281–287
Wong PL, Li KY (1999) In-process roughness measurement on moving surfaces. Opt Laser Technol 31(8):543–548
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Zhang, J.Z., Chen, J.C. The development of an in-process surface roughness adaptive control system in end milling operations. Int J Adv Manuf Technol 31, 877–887 (2007). https://doi.org/10.1007/s00170-005-0262-z
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DOI: https://doi.org/10.1007/s00170-005-0262-z