Design of an Intelligent Adaptive Control with Optimization System to Produce Parts with Uniform Surface Roughness in Finish Hard Turning

Subject Areas : computer integrated manufacturing

vahid pourmostaghimi ¹ , Mohammad Zadshakoyan ²

1 - Ph.D. student, Department of Mechanical ans Manufacturing Engineering, University of Tabriz, Tabriz, Iran,
2 - Department of Manufacturing and Production Engineering, University of Tabriz, Iran

Received: 2019-11-23 Accepted : 2020-04-19 Published : 2020-06-01

Keywords: Hard Turning, particle swarm optimization, Adaptive control, Optimization, Genetic Programming, Artificial Neural Networks,

Abstract :

In this paper, a real-time intelligent adaptive control with optimization methodology is proposed to produce parts with uniform surface roughness in finish turning of hardened AISI D2. Unlike traditional optimization approaches, the proposed methodology considers cutting tool real condition. Wavelet packet transform of cutting tool vibration signals followed by neural network was used to estimate tool flank wear. Intelligent models (artificial neural networks and genetic programming) were utilized to predict surface roughness and tool wear during machining process. Particle swarm optimization algorithm determined optimum feed rate that resulted in desired surface roughness. Performed confirmatory experiments indicated that the proposed adaptive control method not only resulted in parts with acceptable uniform quality, but also decreased the machining cost up to 8.8% and increased material removal rate up to 20% in comparison with those of traditional CNC turning systems.

References:

[1]     Gaitonde, V., Karnik, S., Figueira, L., and Davim, J. P., Machinability Investigations in Hard Turning of AISI D2 Cold Work Tool Steel with Conventional and Wiper Ceramic Inserts, International Journal of Refractory Metals and Hard Materials, Vol. 27, No. 4, 2009, pp. 754-63.

[2]     Pereira, R. B. D., Da Silva, L. A., Lauro, C. H., Brandão, L. C., Ferreira, J. R., and Davim, J. P., Multi-Objective Robust Design of Helical Milling Hole Quality on AISI H13 Hardened Steel by Normalized Normal Constraint Coupled with Robust Parameter Design, Applied Soft Computing, Vol. 75, 2019, pp. 652-85.

[3]     Pourmostaghimi, V., Zadshakoyan, M., and Badamchizadeh, M. A., Intelligent Model-Based Optimization of Cutting Parameters for High Quality Turning of Hardened AISI D2, AI EDAM, Vol. 26, No. 7, 2020, pp. 1725-30.

[4]     Silva, J. A., Abellán-Nebot, J. V., Siller, H. R., and Guedea-Elizalde, F., Adaptive Control Optimisation System for Minimising Production Cost in Hard Milling Operations, International Journal of Computer Integrated Manufacturing, Vol. 27, No. 4, 2014, pp. 348-60.

[5]     Wang, G., Feng, X., Tool Wear State Recognition Based On Linear Chain Conditional Random Field Model, Engineering Applications of Artificial Intelligence, Vol. 26, No. 4, 2013, pp. 1421-7.

[6]     Zadshakoyan, M., Pourmostaghimi, V., Cutting Tool Crater Wear Measurement in Turning Using Chip Geometry and Genetic Programming, International Journal of Applied Metaheuristic Computing (IJAMC), Vol. 6, No. 1, 2015, pp. 47-60.

[7]     Shukla, R., Singh, D., Experimentation Investigation of Abrasive Water Jet Machining Parameters Using Taguchi and Evolutionary Optimization Techniques, Swarm and Evolutionary Computation, Vol. 32, 2017, pp. 167-83.

[8]     Coppel, R., Abellan-Nebot, J. V., Siller, H. R., Rodriguez, C. A., and Guedea, F., Adaptive Control Optimization in Micro-Milling of Hardened Steels—Evaluation of Optimization Approaches, The International Journal of Advanced Manufacturing Technology, Vol. 84, No. 9-12, 2016, pp. 2219-38.

[9]     Koren, Y., Editor Adaptive Control Systems for Machining, American Control Conference, 1988; 1988, IEEE, pp. 1161-7.

[10]  Chiang, S. T., Liu, D. I., Lee, A. C., and Chieng, W. H., Adaptive-Control Optimization in End Milling Using Neural Networks, International Journal of Machine Tools & Manufacture, Vol. 35, No. 4, 1995, pp. 637-60.

[11]  Ko, T. J., Cho, D. W., Adaptive Optimization of Face Milling Operations Using Neural Networks, Journal of Manufacturing Science and Engineering, Vol. 120, No. 2, 1998, pp. 443-51.

[12]  Ko, T. J., Kim, H. S., Autonomous Cutting Parameter Regulation Using Adaptive Modeling and Genetic Algorithms, Precision Engineering, Vol. 22, No. 4, 1998, pp. 243-51.

[13]  Liu, Y., Wang, C., Neural Network Based Adaptive Control and Optimisation in the Milling Process, The International Journal of Advanced Manufacturing Technology, Vol. 15, No. 11, 1999, pp. 791-5.

[14]  Ivester, R. W., Heigel, J. C., Smart Machining Systems: Robust Optimization and Adaptive Control Optimization for Turning Operations: Society of Manufacturing Engineers; 2000.

[15]  Abellan, J., Romero, F., Siller, H., Estruch, A., and Vila, C., Book Title: Advances in Robotics, Automation and Control, CHAPTER 1: Adaptive Control Optimization of Cutting Parameters for High Quality Machining Operations Based On Neural Networks and Search Algorithms, 1 st Edition, InTech, Vienna, Austria, 2008, pp. 472-81.

[16]  Chandrasekaran, M., Muralidhar, and M., Dixit, U., Online Optimization of a Finish Turning Process: Strategy and Experimental Validation, The International Journal of Advanced Manufacturing Technology, Vol. 75, No. 5-8,

[17]  Zakeri Mehrabad, V., Pourmostaghimi, V., Tool Wear Modeling in Drilling Process of AISI1020 and AISI8620 Using Genetic Programming, ADMT Journal, Vol. 10, No. 1, 2017, pp. 93-100.

[18]  Zadshakoyan, M., Pourmostaghimi, V., Genetic Equation for The Prediction of Tool–Chip Contact Length in Orthogonal Cutting, Engineering Applications of Artificial Intelligence, Vol. 26, No. 7, 2013, pp. 1725-30.

[19]  Cakar, T., Cil, I., Artificial Neural Networks for Design of Manufacturing Systems and Selection of Priority Rules, International Journal of Computer Integrated Manufacturing, Vol. 17, No. 3, 2004, pp. 195-211.

[20]  Kumru, M., Kumru, P. Y., Using Artificial Neural Networks to Forecast Operation Times in Metal Industry, International Journal of Computer Integrated Manufacturing, Vol. 27, No. 1, 2014, pp. 48-59.

[21]  Zadshakoyan, M., Pourmostaghimi, V., Book Title: Advancements in Applied Metaheuristic Computing,  CHAPTER 5: Metaheuristics in Manufacturing: Predictive Modeling of Tool Wear in Machining Using Genetic Programming, 1 st edition, IGI Global, USA, 2018, pp. 118-42.

[22]  Pourmostaghimi, V., Zadshakoyan, M., Book Title: Optimization for Engineering Problems,  CHAPTER 4: Optimization of Cutting Parameters during Hard Turning using Evolutionary Algorithms, 1 st edition, Wiley Online Library, USA, 2019, pp. 77-99.

[23]  Pourmostaghimi, V., Zadshakoyan, M., and Sadeghi, M. H., Vibration Based Assessment of Tool Wear in Hard Turning using Wavelet Packet Transform and Neural Networks, International Journal of Advanced Design & Manufacturing Technology, Vol. 12, No. 2, 2019, pp. 17-26.

[24]  Bhuiyan, M. S. H., Choudhury, I. A., Investigation of Tool Wear and Surface Finish by Analyzing Vibration Signals in Turning Assab-705 Steel, Machining Science and Technology, Vol. 19, No. 2, 2015, pp. 236-61.

[25]  Siddhpura, M. A., Siddhpura, A. M., and Bhave, S., Editors. Vibration as a Parameter for Monitoring the Health of Precision Machine Tools, International Conference on Frontiers in Design and Manufacturing Engineering (ICDM), Coimbatore, India; 2008

[26]  Fang, N., Pai, P. S., and Mosquea, S., Effect of Tool Edge Wear On the Cutting Forces and Vibrations in High-Speed Finish Machining of Inconel 718: an Experimental Study and Wavelet Transform Analysis, The International Journal of Advanced Manufacturing Technology, Vol. 52, No. 1-4, 2011, pp. 65-77.

[27]  Zuperl, U., Cus, F., Optimization of Cutting Conditions During Cutting by Using Neural Networks, Robotics and Computer-Integrated Manufacturing, Vol. 19, No. 1-2, 2003, pp. 189-99.

[28]  Xiaoli, L., Zhejun, Y., Tool Wear Monitoring with Wavelet Packet Transform—Fuzzy Clustering Method, Wear, Vol. 219, No. 2, 1998, pp. 145-54.