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. INDEX TERMS Grinding wheel wear, intelligent system, machining sound, audio signals, deep learning. I. INTRODUCTION CHENG-HSIUNG LEE received the B.I.M. and M.I.M. degrees in information management from the Chaoyang University of Technology, in 2002 and 2004, respectively, and the Ph.D. degree in computer science and engineering from the

With an estimation of 220 million people playing badminton on a regular basis, it was particularly popular in Asia but has growing popularity in different regions of the world. The demands of the relevant products, such as shuttlecocks and rackets, are also increasing in the sports industry. Synthetic shuttlecock, produced to offer similar experience and feel as feather shuttlecocks to players, is a more economical alternative to feather shuttlecocks. In addition to maintaining high throughput production for synthetic shuttlecocks with cost reduction, a more substantial improvement in quality control is desired as well. Since the defect detection of synthetic shuttlecocks is a challenging task, it heavily relies on human visual inspection at present. The existing manual quality-inspection process is not only error-prone but also considerably less efficient. In this paper, we propose an intelligent system to overcome these difficulties and bridge the gap between research and practice. Two cylinder grippers are designed to automatically deliver the shuttlecocks, a camera is used for capturing images and an end-to-end objection detection approach based on the Transformer model is investigated to recognize defects. Empirical results show that the proposed system obtains encouraging performance with AP 50 value of 87.5% and outperforms other methods. Ablation studies demonstrate that our approach can considerably boost the detection performance of synthetic shuttlecocks. Moreover, the processing speed is much faster than human operators and suitable for industrial applications. INDEX TERMSSynthetic shuttlecocks, defect detection, intelligent system, transformer model, cylinder gripper. He was a Postdoctoral Research Fellow with Tunghai University, Taiwan, in 2018, where he is currently an Assistant Professor with the Master Program of Digital Innovation. His research interests include natural language processing, artificial intelligence, and machine learning. HAN-YI HSIEH received the B.E. and M.S. degrees in computer science from Tunghai University, Taichung, Taiwan, in 2013 and 2017, respectively. He is currently pursuing the Ph.D. degree in computer science with the

The production of composite material components is a high priority in the aerospace and defense industry. It is important to introduce Industry 4.0 related technologies into this field for the purpose of innovating the factory system. Among the processes of the composite materials production, autoclave curing is the key to yielding durable and sustainable structures. However, the settings of the autoclave curing process still heavily rely on the experienced operators in the current practices. In this paper, we adopt the concept of Data Twin Service to allow the interaction between human intelligence and artificial intelligence. The developed Data Twin Service for the autoclave curing process enables the operators to simulate the placement of molds and learns to predict the curing time. Our service is a GUI application which employs a human-in-the-loop design approach and helps users perceive the simulation process intuitively. We propose a two-stage machine learning model to learn the curing time according to the parts and placement. The empirical study is conducted on the one-year historical data and has proven the practical feasibility of the proposed approach. Moreover, the service is currently being used in the manufacturing site and obtains satisfactory performance.

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