Development of Surface Roughness Prediction and Monitoring System in Milling Process
Received: 24 November 2023 | Revised: 18 December 2023 | Accepted: 20 December 2023 | Online: 8 February 2024
Corresponding author: Jui-Pin Hung
Abstract
High surface quality is an important indicator for high-performance machining during the manufacturing process. The surface roughness generated in machining can be affected by cutting parameters and machining vibration. To achieve processing efficiency, monitoring surface quality within the desired range is important. This study aimed to develop a surface roughness prediction system for the milling process. The predictive model was established based on data collected from machining experiments with the response surface methodology. The surface roughness is related to independent variables, including cutting parameters and machining vibration, in terms of nonlinear functions by regression analysis and the neural network approach, respectively. To be implemented in a CNC milling machine for online application, a predictive model was introduced in the Virtual Machine Extension (VMX) intelligent software development platform. This model can acquire the cutting parameters from the controller via the Open Platform Communications Unified Architecture (OPCUA) interface as well as the vibration features from the sensory module. The system can calculate the roughness based on these data and issue alert when the predicted value exceeds the preset threshold or abnormal vibration is detected.
Keywords:
artificial neural networks, cutting conditions, machining vibration, surface roughnessDownloads
References
W. Grzesik, "Prediction of the Functional Performance of Machined Components Based on Surface Topography: State of the Art," Journal of Materials Engineering and Performance, vol. 25, no. 10, pp. 4460–4468, Oct. 2016.
R. A. Al-Samarai, Haftirman, K. R. Ahmad, and Y. Al-Douri, "Evaluate the Effects of Various Surface Roughness on the Tribological Characteristics under Dry and Lubricated Conditions for Al-Si Alloy," Journal of Surface Engineered Materials and Advanced Technology, vol. 2, no. 3, Jul. 2012, Art. no. 21326.
A. Javidi, "Influence of Machining on the Surface Integrity and Fatigue Strength of 34crnimo6 Steel," Ph.D. dissertation, University of Leoben, Leoben, Austria, 2008.
K. Kadirgama, M. M. Noor, and M. M. Rahman, "Optimization of Surface Roughness in End Milling Using Potential Support Vector Machine," Arabian Journal for Science and Engineering, vol. 37, no. 8, pp. 2269–2275, Dec. 2012.
P. G. Benardos and G.-C. Vosniakos, "Predicting surface roughness in machining: a review," International Journal of Machine Tools and Manufacture, vol. 43, no. 8, pp. 833–844, Jun. 2003.
T. H. Le, V. B. Pham, and T. D. Hoang, "Surface Finish Comparison of Dry and Coolant Fluid High-Speed Milling of JIS SDK61 Mould Steel," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8023–8028, Feb. 2022.
V. C. Nguyen, T. D. Nguyen, and D. H. Tien, "Cutting Parameter Optimization in Finishing Milling of Ti-6Al-4V Titanium Alloy under MQL Condition using TOPSIS and ANOVA Analysis," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6775–6780, Feb. 2021.
M. Aruna and V. Dhanalaksmi, "Design Optimization of Cutting Parameters when Turning Inconel 718 with Cermet Inserts," World Academy of Science, Engineering and Technology, vol. 61, pp. 952–955, Jan. 2012.
G. C. Manjunath Patel, D. Lokare, G. R. Chate, M. B. Parappagoudar, R. Nikhil, and K. Gupta, "Analysis and optimization of surface quality while machining high strength aluminium alloy," Measurement, vol. 152, Feb. 2020, Art. no. 107337.
J. E. Ribeiro, M. B. César, and H. Lopes, "Optimization of machining parameters to improve the surface quality," Procedia Structural Integrity, vol. 5, pp. 355–362, Jan. 2017.
G. Zhou, C. Xu, Y. Ma, X.-H. Wang, P.-F. Feng, and M. Zhang, "Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks," Advances in Manufacturing, vol. 8, no. 4, pp. 486–507, Dec. 2020.
B. T. Danh and N. V. Cuong, "Surface Roughness Modeling of Hard Turning 080A67 Steel," Engineering, Technology & Applied Science Research, vol. 13, no. 3, pp. 10659–10663, Jun. 2023.
V. Q. Nguyen, H. T. Dung, V. T. Nguyen, V. D. Pham, and V. C. Nguyen, "Multiple Response Prediction and Optimization in Thin-Walled Milling of 6061 Aluminum Alloy," Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10447–10452, Apr. 2023.
N. V. Cuong and N. L. Khanh, "Parameter Selection to Ensure Multi-Criteria Optimization of the Taguchi Method Combined with the Data Envelopment Analysis-based Ranking Method when Milling SCM440 Steel," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7551–7557, Oct. 2021.
S. S. Bhogal, C. Sindhu, S. S. Dhami, and B. S. Pabla, "Minimization of Surface Roughness and Tool Vibration in CNC Milling Operation," Journal of Optimization, vol. 2015, Jan. 2015, Art. no. e19203030.
J. H. Shaik and J. Srinivas, "Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach," Mechanics of Advanced Materials and Modern Processes, vol. 3, no. 1, Feb. 2017, Art. no. 5.
Y.-C. Lin, Y.-C. Chen, K.-D. Wu, and J.-P. Hung, "Prediction of Surface Roughness based on the Machining Conditions with the Effect of Machining Stability," Advances in Science and Technology. Research Journal, vol. 14, no. 2, pp. 171–183, 2020.
D. E. Dimla and P. M. Lister, "On-line metal cutting tool condition monitoring.: I: force and vibration analyses," International Journal of Machine Tools and Manufacture, vol. 40, no. 5, pp. 739–768, Apr. 2000.
I. Yesilyurt and H. Ozturk, "Tool condition monitoring in milling using vibration analysis," International Journal of Production Research, vol. 45, no. 4, pp. 1013–1028, Feb. 2007.
A. J. Vallejo, R. Morales-Menendez, and J. R. Alique, "On-line cutting tool condition monitoring in machining processes using artificial intelligence," in Robotics, Automation and Control, London, UK: IntechOpen, 2008, pp. 143–168.
A. Khorasani and M. R. S. Yazdi, "Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation," The International Journal of Advanced Manufacturing Technology, vol. 93, no. 1, pp. 141–151, Oct. 2017.
C. David, D. Sagris, E. Stergianni, C. Tsiafis, and I. Tsiafis, "Experimental Analysis of the Effect of Vibration Phenomena on Workpiece Topomorphy Due to Cutter Runout in End-Milling Process," Machines, vol. 6, no. 3, Sep. 2018, Art. no. 27.
S. Zahoor, N. A. Mufti, M. Q. Saleem, M. P. Mughal, and M. A. M. Qureshi, "Effect of machine tool’s spindle forced vibrations on surface roughness, dimensional accuracy, and tool wear in vertical milling of AISI P20," The International Journal of Advanced Manufacturing Technology, vol. 89, no. 9, pp. 3671–3679, Apr. 2017.
Md. S. H. Bhuiyan and I. A. Choudhury, "Investigation of Tool Wear and Surface Finish by Analyzing Vibration Signals in Turning Assab-705 Steel," Machining Science and Technology, vol. 19, no. 2, pp. 236–261, Apr. 2015.
C. L. Kiew et al., "Analysis of the relation between fractal structures of machined surface and machine vibration signal in turning operation," Fractals, vol. 28, no. 1, Feb. 2020, Art. no. 2050019.
O. B. Abouelatta and J. Madl, "Surface roughness prediction based on cutting parameters and tool vibrations in turning operations," Journal of Materials Processing Technology, vol. 118, no. 1, pp. 269–277, Dec. 2001.
Y.-C. Lin, K.-D. Wu, W.-C. Shih, P.-K. Hsu, and J.-P. Hung, "Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network," Applied Sciences, vol. 10, no. 11, Jan. 2020, Art. no. 3941.
A. M. Zain, H. Haron, and S. Sharif, "Prediction of surface roughness in the end milling machining using Artificial Neural Network," Expert Systems with Applications, vol. 37, no. 2, pp. 1755–1768, Mar. 2010.
K. Szwajka, J. Zielinska-Szwajka, and T. Trzepiecinski, "The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process," Materials, vol. 16, no. 15, Jul. 2023, Art. no. 5292.
N. Fang, P. S. Pai, and N. Edwards, "Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys," Journal of Computer and Communications, vol. 4, no. 5, pp. 1–9, May 2016.
"Spotfire Data Science: Powering Enterprise-Level Analytics & Collaboration," Spotfire. https://www.spotfire.com/products/data-science.html.
Downloads
How to Cite
License
Copyright (c) 2023 Yu-Sheng Lai , Wei-Zhu Lin , Yung-Chih Lin, Jui-Pin Hung
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
