윤희택 교수

Often, manufacturing equipment is utilized without a planned maintenance approach. Such a strategy frequently results in unplanned downtime, owing to unexpected failures. Scheduled maintenance replaces components frequently to avoid unexpected equipment stoppages, but increases the time associated with machine non-operation and maintenance cost. The emergence of Industry 4.0 and smart systems is leading to increasing attention to predictive maintenance (PdM) strategies that can decrease the cost

Abstract Feature recognition and manufacturability analysis from computer-aided design (CAD) models are indispensable technologies for better decision making in manufacturing processes. It is important to transform the knowledge embedded within a CAD model to manufacturing instructions for companies to remain competitive as experienced baby-boomer experts are going to retire. Automatic feature recognition and computer-aided process planning have a long history in research, and recent development

The single-pattern Fourier transform profilometry (FTP) and double-pattern modified FTP methods have great value in high-speed three-dimensional shape measurement, yet it is difficult to retrieve absolute phase pixel by pixel. This paper presents a method that can recover absolute phase pixel by pixel for the modified FTP method. The proposed method uses two images with different frequencies, and the recovered low-frequency phase is used to temporally unwrap the high-frequency phase pixel by pix

With the development of Internet of Things (IoT), predictive maintenance (PdM) for smart manufacturing receives attentions recently. For the monitoring of machines with rotary components, sound and vibration emitted from the machines have been utilized as the meaningful information. However, sound sensors are susceptible to external noise and the costs for signal conditioning should be considered. In this paper, a stethoscope is utilized as an internal sound sensor which are capable of noise red

A process for femtosecond laser-based manufacturing was proposed to fabricate a long-period fiber grating (LPFG) with a screw shape. The screw-shaped LPFG was continuously inscribed by single laser scanning, which results in the improvement of fabrication time. For the single laser scanning, an optical fiber was traveled along fiber axis and rotated about the fiber axis. The 44.65-mm-long LPFG with a screw shape and the period of 450 μm was fabricated about 17 min, and its sensitivity was 48 ~ 5

Machine sound monitoring is widely used in various applications of operational state and diagnostic monitoring as machine-emitted sound contains the operational and process information. In the metal cutting industry, it is not surprising that operators are easily able to recognize whether cutting is engaging by listening to the operational sounds based on their experiences even if the cutting parameters are changed. Inspired by the ability of recognizing human sound, we propose a real-time sound

In this study, a high-performance triboelectric nanogenerator (TENG) is developed based on cold spray (CS) deposition of composite material layers. Composite layers were fabricated by cold spraying of micron-scale tin (Sn) particles on aluminum (Al) and polytetrafluoroethylene (PTFE) films, which led to improved TENG performance owing to functionalized composite layers as friction layers and electrodes, respectively. As-sprayed tin composite layers not only enhanced the flow of charges by strong

Abstract Vision-based robots have been utilized for pick-and-place operations by their ability to find object poses. As they progress into handling a variety of objects with cluttered state, more flexible and lightweight operations have been presented. In this paper, an autonomous robotic bin-picking platform is proposed. It combines human demonstration with a collaborative robot for the flexibility of the objects and YOLOv5 neural network model for faster object localization without prior compu