The prediction of profile deviations from multi process machining of complex geometrical features using combined evolutionary and neural network algorithms with embedded simulation
- 207 Downloads
The capability to generate complex geometrical features at tight tolerances and fine surface roughness is a key element in the implementation of Creep Feed grinding process in specialist applications such as the aerospace manufacturing environment. Based on the analysis of 3D cutting forces this paper proposes a novel method of predicting the profile deviations of tight geometrical features generated using Creep Feed grinding. In this application, there are several grinding passes made at varying depths providing an incremental geometrical change with the last cut generating the final complex feature. With repeatable results from co-ordinate measurements both the radial and tangential forces can be gauged versus the accuracy of the ground features. The tangential force was found more sensitive to the deviation of actual cut depth from the theoretical one. However, to make a more robust prediction on the profile deviation its values were considered as a function of both force components (proportional to force: power was also included). For multi process, one machining platforms hole making was also investigated in terms of monitoring the force to ensure the mean cylinder was kept within required tolerances and with minimal subsequent machining (due to these imposed accuracies this is also considered a complex feature). Genetic programming (GP), an evolutionary programming technique, has been used to compute the prediction rules of part profile deviations based on the extracted radial and tangential force correlated with the said chosen “gauging” methodology (for grinding process). GP was also used to correlate the force and flank wear (VB) for hole deviations. It was found that using this technique, complex rules can be achieved and used online to dynamically control the geometrical accuracy of ground and drilled hole features. The GP complex rules are based on the correlation between the measured forces and recorded deviation of the theoretical profile (both grinding and hole making). The mathematical rules are generated from Darwinian evolutionary strategy which provides the mapping between different output classes. GP works from crossover recombination of different rules and the best individual is evaluated in terms of the given ‘best fitness value so far’ which closes on an optimal solution. The best obtained GP terminal sets were realised in rule-based embedded coded systems which were finally implemented into a real-time Simulink simulation. This realisation gives a view of how such a control regime can be utilised within an industrial capacity. Neural networks were used for GP decision verification ensuring less sensitivity to possible outliers giving more robustness to the integrated system.
KeywordsGrinding Hole making Cutting forces Spindle power Profile and cylindrical deviations Genetic Programming Neural networks and real-time simulations
The authors are grateful to The University of Nottingham for their valuable technical support during the experimental investigation. Please also note that the experimental work was carried out at The University of Nottingham. The Grant for carrying out this work was awarded by The University of Nottingham Doctoral Awards.
- DeHart, A., & Murphy, D. (2004). Machine shopping: How to become a better-informed machine tool consumer, American Machinist. http://americanmachinist.com/machining-cutting/machine-shopping.
- Furness, R. (1992). Supervisory Control of the Drilling Process, Ph.D. dissertation. Department of Mechanical Engineering and Applied Mechanics, University of Michigan.Google Scholar
- Griffin, J., & Chen, X. (2014, February). Real-time Neural Network classifications of characteristics from emitted Acoustic Emission during Horizontal Single Grit Scratch Tests. Journal of Intelligent Manufacturing, 1–17.Google Scholar
- Haber, R. E., Gajate, A., Liang, S. Y., Haber, R. H., & del Toro, R. M. (2011). An optimal fuzzy controller for a high-performance drilling process implementation over and industrial network. International Journal of Innovative Computing, Information and Control, 7(3), 1481–1498.Google Scholar
- Pratap, S., Daultani, Y., Tiwari, M.K., & Mahanty, B. (2015). Rule based optimization for a bulk handling port operations. Journal of Intelligent Manufacturing, 1–25.Google Scholar
- Ren, Q., Balazinski, M. & Baron, L. (2012). Fuzzy Identification of Cutting Acoustic Emission with Extended Subtractive Cluster Analysis. Nonlinear Dynamics, 67(4), 2599–2608.Google Scholar
- Ren, Q., Balazinski, M., Jemielniak, K., Baron, L. & Achiche, S. (2013) Experimental and Fuzzy Modelling Analysis on Dynamic Cutting Force in Micro Milling. Soft Computing, 17, 1687–1697.Google Scholar
- Ritou, M., Garnier, S., Furet, B., & Hascoët, J. -Y. (2014). Angular approach combined to mechanical model for tool breakage detection by eddy current sensors. Mechanical Systems and Signal Processing, 44(1–2), 211–220.Google Scholar
- Sharma, V., Dhiman, S., Sehgal, R., & Sharma, S. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 8, 215–226.Google Scholar
- Silva, S., & Tseng, Y. T. (2005). Classification of Seafloor Habitats using Genetic Programming. In GECCO’05. Washington.Google Scholar
- Silva, S. (2004). A genetic programming toolbox for Matlab—version 2. ECOS—Evolutionary and Complex Systems Group, University of Coimbra, Portugal. http://gplab.sourceforge.net/.
- Srivastava, A. K., & Elbestawi, M. A. (1995). Control strategy for mutlipass robotic grinding. International Journal of Robotics and Automation, 10(3), 114–119.Google Scholar
- Thoma, S., & Weikert, S. (2012). Compensation strategies for axis coupling effects. In 5th CIRP Conference on High Performance 2012 (vol. 1, pp. 255–259).Google Scholar
- Yoo, S. M. (1990). Computer simulation of the flexible disk grinding process: flat surface control using variable vertical feed speed. In Symposium on monitoring and control for manufacturing processes. ASME WAM PED (Vol. 44, pp. 123–32).Google Scholar