Abstract
Immediate monitoring of the conditions of the grinding wheel during the grinding process is important because it directly affects the surface accuracy of the workpiece. Because the variation in machining sound during the grinding process is very important for the field operator to judge whether the grinding wheel is worn or not, this study applies artificial intelligence technology to attempt to learn the experiences of auditory recognition of experienced operators. Therefore, we propose an intelligent system based on machining sound and deep learning to recognize the grinding wheel condition. This study uses a microphone embedded in the grinding machine to collect audio signals during the grinding process, and extracts the most discriminated feature from spectrum analysis. The features will be input the designed CNNs architecture to create a training model based on deep learning for distinguishing different conditions of the grinding wheel. Experimental results show that the proposed system can achieve an accuracy of 97.44%, a precision of 98.26% and a recall of 96.59% from 820 testing samples.
Highlights
Since maintenance costs of manufacturing plants account for the majority of total operating costs, how to reduce maintenance costs has become a topic of concern in the manufacturing industry
Related sensors used to the monitoring of machining operations can be divided into direct methods, such as an optical device of scanning electron microscope (SEM) and indirect methods, such as cutting force, acoustic emission, spindle motor, The associate editor coordinating the review of this manuscript and approving it for publication was Zhigang Liu
Because deep convolutional neural networks (CNNs) are widely used in solving high dimensional and intricate nonlinear problems [12], and designed for variable and complex signals and have shown remarkable success in various applications in the past few years [25], we developed an intelligent system based on CNNs to recognize different conditions of grinding wheel during grinding process
Summary
Since maintenance costs of manufacturing plants account for the majority of total operating costs, how to reduce maintenance costs has become a topic of concern in the manufacturing industry. In the case of steel, pulp and paper, and other heavy industries, maintenance costs account for 60% of total production costs [1]. Numerous studies have been developed tool wear monitoring systems using available and suitable sensors [4]. Related sensors used to the monitoring of machining operations can be divided into direct methods, such as an optical device of scanning electron microscope (SEM) and indirect methods, such as cutting force, acoustic emission, spindle motor, The associate editor coordinating the review of this manuscript and approving it for publication was Zhigang Liu
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