Quality Oriented Self-Optimizing Control in Smart Machining Systems

Abstract:

In recent years, due to the hard competitiveness through globalization most manufacturing companies have focused on manufacturing higher value added products with low production costs. So, it is inevitable to apply the advanced technology to carry out it in effective and efficient way. Monitoring and control of machining processes are becoming increasingly important for keeping a consistent quality of machined parts. The paper presents in-process sensing and control techniques with applying information science for the next generation of quality control that eliminates the need for expensive post-process inspection. In this research, a machining process of machine tool is considered in terms of a quality oriented control. The self-optimizing control is proposed for enabling intelligent process monitoring and control which can detect the cutting conditions and status of machining operations. To prove the proposed self-optimizing control system, self-monitoring and self-adjusting, the machining process related to the tool condition such as tool wear was considered in this paper. The most crucial and determining factor to successful maximization of the manufacturing processes in any typical metal cutting process is tool wear. The evolutionary algorithm was developed to diagnose the tool wear. If the amount of tool wear is out of the allowed limit, the new cutting parameters should be generated and adjusted autonomously according to affecting the quality of the machined part through the tool wear.

• Periodical: Advanced Materials Research (Volume 837)
• Pages: 152-157
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.837.152
• Online since: November 2013
• Authors: Hong Seok Park, Ngoc Hien Tran
• Keywords: In-Process Monitoring, Evolutionary Algorithm (EA), Information Convergence Technology, Quality Oriented Process Control, Self-Optimization

References

[1] L. Wang, R.X. Gao, Condition Monitoring and Control for Intelligent Manufacturing, Springer, (2006).

[2] Y. Koren, Adaptive control systems for machining, Manufacturing Review. 2 (1989) 6-15.

[3] J.A. Silva, J.V. Abellán-Nebot, H.R. Siller, F. Guedea-Elizalde, Adaptive control optimisation system for minimising production cost in hard milling operations, Int. J. Computer Integrated Manufacturing. DOI: 10. 1080/0951192X. 2012. 749535 (2012).

DOI: 10.1080/0951192x.2012.749535

[4] A. Ghasempoor, J. Jeswiet, T.N. Moore, Real time implementation of on-line tool condition monitoring in turning, Int. J. Machine Tools & Manufacture. 39 (1999) 1883–(1902).

DOI: 10.1016/s0890-6955(99)00035-8

[5] S.Y. Nof, Handbook of Automation, Springer, (2009).

[6] U. Thombansen et al., Model-based self-optimization for manufacturing systems, Proceedings of the 17th International Conference on Concurrent Enterprising, (2011).

[7] S. Pal, P. S. Heyns, B. H. Freyer, N. J. Theron, S. K. Pal, Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties, J. Intel. Manuf. 22 (2011) 491-504.

DOI: 10.1007/s10845-009-0310-x