Development of Surface Roughness Prediction and Monitoring System in Milling Process

Authors

  • Yu-Sheng Lai Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Republic of China
  • Wei-Zhu Lin Department of Mechanical Engineering, National Chin-Yi University of Technology, Republic of China
  • Yung-Chih Lin Intelligent Machinery Technology Center, Industrial Technology Research Institute, Republic of China
  • Jui-Pin Hung Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Republic of China
Volume: 14 | Issue: 1 | Pages: 12797-12805 | February 2024 | https://doi.org/10.48084/etasr.6664

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 roughness

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How to Cite

[1]
Y.-S. Lai, W.-Z. Lin, Y.-C. Lin, and J.-P. Hung, “Development of Surface Roughness Prediction and Monitoring System in Milling Process ”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 1, pp. 12797–12805, Feb. 2024.

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