An approach to in-process surface texture condition monitoring |
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Affiliation: | 1. Department of Mechanical Engineering, University of Zanjan, Zanjan, Iran;2. Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran;1. Department of Electrical Engineering, Faculty of Natural Sciences and Engineering, Gaziosmanpaşa University, Tokat, Turkey;2. Department of Mechatronics Engineering, Faculty of Natural Sciences and Engineering, Gaziosmanpaşa University, Tokat, Turkey;1. Faculty of Engineering, Department of Mechatronics Engineering, Federal University Oye Ekiti, Ekiti State, Nigeria;2. School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom |
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Abstract: | Surface textures formed in the machining process have a great influence on parts’ mechanical behaviours. Normally, the surface textures are inspected by using the images of the machined and cleaned parts. In this paper, an in-process surface texture condition monitoring approach is proposed. Based on the grey-level co-occurrence matrices, some surface texture image features are extracted to describe the texture characteristics. On the basis of the empirical model decomposition, some sensitive features are also extracted from the vibration signal. The mapping relationship from texture characteristics to texture image features and vibration signal features is found. A back propagation neural network model is built when the signal features and the texture conditions are respectively inputs and outputs. The particle swarm optimization is used to optimise the weights and thresholds of the neural network. Experimental study verifies the approach's effectiveness in monitoring the surface texture conditions during the machining process. The approach's accuracy and robustness are also verified. Then, the surface texture condition can be monitored efficiently during the machining process. |
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Keywords: | Surface texture In-process monitoring Correlation analysis |
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