Abstract
Grinding wheel wearing fast and metal adhering were severe in hard sphere grinding, which led to wheel overload and clogging. If a fixed-feed grinding was used, the normal pressure between the workpiece and the grinding wheel increased rapidly. Once the grinding load on the grinding wheel was greater than the strength of the retaining bond bridges, a large amount of grains dropped out, which can even damage the wheel. This led to the sphere surface to be scratched. In this study, a dynamic threshold-based fuzzy adaptive control algorithm (DTbFACA) is proposed for hard sphere grinding to avoid scratches on the workpiece. The grinding force was indirectly obtained by measuring the motorized spindle current which was used as a feedback to control hard sphere grinding process. The current threshold in DTbFACA was obtained and online-rectified automatically. The depth of cut and the cup wheel swing speed that affect the motorized spindle current was online-adjusted by fuzzy algorithm. The experimental results indicated that DTbFACA can avoid scratches on the workpiece without sacrificing the sphere form error and grinding efficiency. DTbFACA has been implemented on MD6050 sphere grinding machine tool in production.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Wu Q (2006) Research on precision grinding technology in machining high rigidity rotary sphere. Dissertation, Shanghai Jiao Tong University, Shanghai, China
Rowe WB, Li Y, Mills B, Allanson DR (1996) Application of intelligent CNC in grinding. Computers in industry 31:45–60
Amitay G, Malkin S, Karen Y (1981) Adaptive control optimization of grinding. Journal of Engineering for Industry 103(1):103–108
Kelly S, Rowe WB, Moruzzi JL (1989) Adaptive grinding control. Advanced Manufacturing Engineering 1(5):287–295
Li XM, Ding N (2010) Adaptive fuzzy neural network control system in cylindrical process. Key Engineering Materials 426–427:220–224
König W, Altintas Y, Memis F (1995) Direct adaptive control of plunge grinding process using acoustic emission sensor. International Journal of Machine tools & Manufacture 35(10):1445–1457
Wang JZ (2006) Study on the key techniques of intelligent cylindrical traverse grinding. Dissertation, Jilin University, Jilin, China
Lu YY, Dong JH (2010) The study of force control with artificial intelligence in ceramic grinding process. Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on 208–211
Chen X, Rowe WB, Allanson DR, Mills B (1999) A grinding power model for selection of dressing and grinding condition. Journal of Manufacturing Science and Engineering 121:632–637
Tönshoff HK, Friemuth T, Becker JC (2002) Process monitoring in grinding. Manufacturing Technology 51(2):551–571
Malkin S, Guo CS (2008) Grinding technology: theory and application of machining with abrasives, 2nd edn. Industrial, New York
Xiao G, Malkin S (1996) On-line optimization for internal plunge grinding. Manufacturing Technology 45(1):287–292
Xu CY, Shin YC (2007) Control of cutting force for creep-feed grinding processes using a multi-level fuzzy controller. Journal of Dynamic Systems Measurement and Control 129(4):480–493
Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller-part Ι. System, Man and Cybernetics 20(2):404–418
Hsu PL, Fann WR (1996) Fuzzy adaptive control of machining processes with a self-learning algorithm. Journal of Manufacturing Science and Engineering 118(4):522–530
Linkens DA, Abbod MF (1992) Self-organising fuzzy logic control and the selection of its scaling factors. Transactions of the Institute of Measurement and Control 14(3):114–125
Linkens DA, Abbod MF (1991) Self-organising fuzzy logic control for real time processes. International Conference on Control 2:971–976
He GY, Wang TY, Zhao J, Yu BQ, Li GQ (2006) Algorithm for sphericity error and the number of measured points. Chinese Journal of Mechanical Engineering 19(3):460–463
Cui CC, Fu SW, Huang FG (2009) Research on the uncertainties from different form error evaluation methods by CMM sampling. International Journal of Advanced Manufacturing Technology 43:136–145
Jung M, Cross KJ, McBride JW, Hill M (2000) A method for the selection of algorithm for form characterization of nominally spherical surfaces. Precision Engineering 24:127–138
Chen KW, Papadoupoulos AS (1996) Comparison of the linear least squares and nonlinear least squares spheres. Microelectronics and Reliability 36(1):37–46
Liu B, Xu LM, Chai YD, Xu KZ, Dd L (2011) On-site measurement of sphericity based on high-precision sphere grinder. Journal of Shanghai Jiaotong University 45(1):66–70
Samuel GL, Shunmugam MS (2003) Evaluation of circularity and sphericity from coordinate measurement data. Journal of Materials Processing Technology 139:90–95
Zhu XS, Ren XS, Yang JG, Xue BY (1998) General formula for the least square sphericity. Journal of Shanghai Jiaotong University 32(5):43–45
Jenkins HE, Kurfess TR (1999) Adaptive pole-zero cancellation in grinding force control. Control Systems Technology 7(3):363–370
Acknowledgments
This research is sponsored by the National Natural Science Foundation (no. 51075273), State Key Lab of Digital Manufacturing Equipment & Technology of Huazhong University of Science and Technology (no. 2008-DMET-KF-001), and State Key Laboratory of Mechanical System and Vibration of Shanghai Jiao Tong University (no. MSVMS201104).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Li, D., Xu, M., Wei, C. et al. A dynamic threshold-based fuzzy adaptive control algorithm for hard sphere grinding. Int J Adv Manuf Technol 60, 923–932 (2012). https://doi.org/10.1007/s00170-011-3661-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-011-3661-3