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Smart Sensing Control Scheme for Manufacturing and Automation Application

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 17528

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Special Issue Editor

Prof. Dr. Cheng-Chi Wang

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Guest Editor

Ph.D. Program, Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, No.57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
Interests: nonlinear dynamic analysis; smart machine; chaos; optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Smart Technologies in the research field of innovation in advanced system design, sensing control, modeling, and optimization have made great progress in recent years, and intelligent manufacturing and automation systems have now become a popular term in the field of electrical/mechanic engineering and development of Industry 4.0. Many researchers in smart system design, metrology, and optimization have made great efforts to develop innovative methodologies for engineering, physical, biological, etc., and these research results have had a great influence in the greater field of system simulation and control.

With the advancement of computer hardware providing more powerful computing environments, intelligent automation system modeling, numerical simulation, and optimization, researchers have been able to challenge solving larger and more complex problems. Driven by such motivation, the innovative methodologies of smart system design, control, metrology, and optimization technologies are proposed not only in the area of engineering but also in new paradigms in computer science. In addition, advanced system modeling, numerical simulation, and optimization researchers have applied the developed innovative methods to various real world problems such as intelligent robotic sensing systems. This Special Issue includes the mathematical and physical theories of nonlinear dynamics analysis and optimization in physical, engineering, biological studies, and their various applications. Prospective authors are invited to submit original papers to this Special issue.

Prof. Dr. Cheng-Chi Wang
Guest Editor

Manuscript Submission Information

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Keywords

Smart technologies
Sensing control
Intelligent manufacturing
Intelligent automation
System modeling, numerical simulation, and optimization

Published Papers (3 papers)

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Research

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21 pages, 14734 KiB

Open AccessArticle

Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

by Hsing-Cheng Chang, Yu-Liang Hsu, San-Shan Hung, Guan-Ru Ou, Jia-Ron Wu and Chuan Hsu

Sensors 2021, 21(4), 1102; https://0-doi-org.brum.beds.ac.uk/10.3390/s21041102 - 05 Feb 2021

Cited by 36 | Viewed by 9230

Abstract

Water is one of the most precious resources. However, industrial development has made water pollution a critical problem today and thus water quality monitoring and surface cleaning are essential for water resource protection. In this study, we have used the sensor fusion technology as a basis to develop a multi-function unmanned surface vehicle (MF-USV) for obstacle avoidance, water-quality monitoring, and water surface cleaning. The MF-USV comprises a USV control unit, a locomotion module, a positioning module, an obstacle avoidance module, a water quality monitoring system, a water surface cleaning system, a communication module, a power module, and a remote human–machine interface. We equip the MF-USV with the following functions: (1) autonomous obstacle detection, avoidance, and navigation positioning, (2) water quality monitoring, sampling, and positioning, (3) water surface detection and cleaning, and (4) remote navigation control and real-time information display. The experimental results verified that when the floating garbage located in the visual angle ranged from −30° to 30° on the front of the MF-USV and the distances between the floating garbage and the MF-USV were 40 and 70 cm, the success rates of floating garbage detection are all 100%. When the distance between the floating garbage and the MF-USV was 130 cm and the floating garbage was located on the left side (15°~30°), left front side (0°~15°), front side (0°), right front side (0°~15°), and the right side (15°~30°), the success rates of the floating garbage collection were 70%, 92%, 95%, 95%, and 75%, respectively. Finally, the experimental results also verified that the applications of the MF-USV and relevant algorithms to obstacle avoidance, water quality monitoring, and water surface cleaning were effective. Full article

(This article belongs to the Special Issue Smart Sensing Control Scheme for Manufacturing and Automation Application)

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21 pages, 9242 KiB

Open AccessArticle

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

by Chih-Yung Huang and Zaky Dzulfikri

Sensors 2021, 21(1), 262; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010262 - 02 Jan 2021

Cited by 14 | Viewed by 3941

Abstract

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models. Full article

(This article belongs to the Special Issue Smart Sensing Control Scheme for Manufacturing and Automation Application)

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Other

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14 pages, 3481 KiB

Open AccessLetter

Development of an Automated Optical Inspection System for Rapidly and Precisely Measuring Dimensions of Embedded Microchannel Structures in Transparent Bonded Chips

by Pin-Chuan Chen, Ya-Ting Lin, Chi-Minh Truong, Pai-Shan Chen and Huihua-Kenny Chiang

Sensors 2021, 21(3), 698; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030698 - 20 Jan 2021

Cited by 3 | Viewed by 3575

Abstract

This study aimed to develop an automated optical inspection (AOI) system that can rapidly and precisely measure the dimensions of microchannels embedded inside a transparent polymeric substrate, and can eventually be used on the production line of a factory. The AOI system is constructed based on Snell’s law. The concept holds that, when light travels through two transparent media (air and the microfluidic chip transparent material), by capturing the parallel refracted light from a light source that went through the microchannel using a camera with a telecentric lens, the image can be analyzed using formulas derived from Snell’s law to measure the dimensions of the microchannel cross-section. Through the NI LabVIEW 2018 SP1 programming interface, we programmed this system to automatically analyze the captured image and acquire all the needed data. The system then processes these data using custom-developed formulas to calculate the height and width measurements of the microchannel cross-sections and presents the results on the human–machine interface (HMI). In this study, a single and straight microchannel with a cross-sectional area of 300 μm × 300 μm and length of 44 mm was micromachined and sealed with another polymeric substrate by a solvent bonding method for experimentations. With this system, 45 cross-sectional areas along the straight microchannel were measured within 20 s, and experiment results showed that the average measured error was less than 2%. Full article

(This article belongs to the Special Issue Smart Sensing Control Scheme for Manufacturing and Automation Application)

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