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Selected Papers about Sensor Application from 16th International Conference on Automation Technology

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

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 23161

Special Issue Editors

Prof. Dr. Chien-Hung Liu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, National Chung Hsing University, 250 Kuo Kuang Rd., Taichung 402, Taiwan
Interests: high precision instrument design; laser engineering; smart sensors and actuators; optical device; optical measurement; metrology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chung-Hsien Kuo

E-Mail Website
Guest Editor
Department of Mechanical Engineering, National Taiwan University, Taipei 106319, Taiwan
Interests: intelligent robots; intelligent automation; intelligent welfare technologies; autonomous driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue will select extended papers about sensor application from the 16th International Conference on Automation Technology (Automation 2019). Automation 2019 is an annual conference of the Chinese Institute of Automation Engineers (CIAE). It provides an international platform for the smart automation research community to explore the state-of-the-art of sciences and technologies within academic and industrial applications. The extended papers will be sensor studies on topics related to artificial intelligence and machine learning, sensor networks, signal and image processing, machine vision, internet of things, automatic optical inspection, robotics, automatic measurement, intelligent manufacturing systems, robots and systems, opto-mechatronics, industry4.0, micro/nano systems, micro/precision/ultraprecision manufacturing systems, MEMS, nanotechnology and sensor networks, biotechnology and biomedical engineering, smart grids, and power systems and renewable energy.

http://automation2019.ntust.edu.tw/

Prof. Dr. Chien-Hung Liu
Prof. Dr. Chung-Hsien Kuo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensors in smart automation
  • Sensors development in artificial intelligence
  • Sensors with robotics
  • Automatic measurement
  • Sensors in Industry 4.0
  • Sensors in mechatronics and opto-mechatronics
  • Sensors in micro/precision/ultraprecision manufacturing systems
  • Sensors in MEMS, nanotechnology, and sensor networks

Published Papers (6 papers)

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Research

27 pages, 5149 KiB  
Article
Equipment Anomaly Detection for Semiconductor Manufacturing by Exploiting Unsupervised Learning from Sensory Data
by Chieh-Yu Chen, Shi-Chung Chang and Da-Yin Liao
Sensors 2020, 20(19), 5650; https://doi.org/10.3390/s20195650 - 02 Oct 2020
Cited by 10 | Viewed by 4513
Abstract
In-line anomaly detection (AD) not only identifies the needs for semiconductor equipment maintenance but also indicates potential line yield problems. Prompt AD based on available equipment sensory data (ESD) facilitates proactive yield and operations management. However, ESD items are highly diversified and drastically [...] Read more.
In-line anomaly detection (AD) not only identifies the needs for semiconductor equipment maintenance but also indicates potential line yield problems. Prompt AD based on available equipment sensory data (ESD) facilitates proactive yield and operations management. However, ESD items are highly diversified and drastically scale up along with the increased use of sensors. Even veteran engineers lack knowledge about ESD items for automated AD. This paper presents a novel Spectral and Time Autoencoder Learning for Anomaly Detection (STALAD) framework. The design consists of four innovations: (1) identification of cycle series and spectral transformation (CSST) from ESD, (2) unsupervised learning from CSST of ESD by exploiting Stacked AutoEncoders, (3) hypothesis test for AD based on the difference between the learned normal data and the tested sample data, (4) dynamic procedure control enabling periodic and parallel learning and testing. Applications to ESD of an HDP-CVD tool demonstrate that STALAD learns normality without engineers’ prior knowledge, is tolerant to some abnormal data in training input, performs correct AD, and is efficient and adaptive for fab applications. Complementary to the current practice of using control wafer monitoring for AD, STALAD may facilitate early detection of equipment anomaly and assessment of impacts to process quality. Full article
(This article belongs to the Special Issue Selected Papers about Sensor Application from 16th International Conference on Automation Technology)
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18 pages, 8944 KiB  
Article
Extending and Matching a High Dynamic Range Image from a Single Image
by Van Luan Tran and Huei-Yung Lin
Sensors 2020, 20(14), 3950; https://doi.org/10.3390/s20143950 - 16 Jul 2020
Cited by 5 | Viewed by 3181
Abstract
Extending the dynamic range can present much richer contrasts and physical information from the traditional low dynamic range (LDR) images. To tackle this, we propose a method to generate a high dynamic range image from a single LDR image. In addition, a technique [...] Read more.
Extending the dynamic range can present much richer contrasts and physical information from the traditional low dynamic range (LDR) images. To tackle this, we propose a method to generate a high dynamic range image from a single LDR image. In addition, a technique for the matching between the histogram of a high dynamic range (HDR) image and the original image is introduced. To evaluate the results, we utilize the dynamic range for independent image quality assessment. It recognizes the difference in subtle brightness, which is a significant role in the assessment of novel lighting, rendering, and imaging algorithms. The results show that the picture quality is improved, and the contrast is adjusted. The performance comparison with other methods is carried out using the predicted visibility (HDR-VDP-2). Compared to the results obtained from other techniques, our extended HDR images can present a wider dynamic range with a large difference between light and dark areas. Full article
(This article belongs to the Special Issue Selected Papers about Sensor Application from 16th International Conference on Automation Technology)
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13 pages, 5729 KiB  
Article
Inline Inspection with an Industrial Robot (IIIR) for Mass-Customization Production Line
by Zai-Gen Wu, Chao-Yi Lin, Hao-Wei Chang and Po Ting Lin
Sensors 2020, 20(11), 3008; https://doi.org/10.3390/s20113008 - 26 May 2020
Cited by 14 | Viewed by 3176
Abstract
Robots are essential for the rapid development of Industry 4.0. In order to truly achieve autonomous robot control in customizable production lines, robots need to be accurate enough and capable of recognizing the geometry and orientation of an arbitrarily shaped object. This paper [...] Read more.
Robots are essential for the rapid development of Industry 4.0. In order to truly achieve autonomous robot control in customizable production lines, robots need to be accurate enough and capable of recognizing the geometry and orientation of an arbitrarily shaped object. This paper presents a method of inline inspection with an industrial robot (IIIR) for mass-customization production lines. A 3D scanner was used to capture the geometry and orientation of the object to be inspected. As the object entered the working range of the robot, the end effector moved along with the object and the camera installed at the end effector performed the requested optical inspections. The detailed information about the developed methodology was introduced in this paper. The experiments showed there was a relative movement between the moving object and the following camera and the speed was around 0.34 mm per second (worst case was around 0.94 mm per second). For a camera of 60 frames per second, the relative moving speed between the object and the camera was around 6 micron (around 16 micron for the worst case), which was stable enough for most industrial production inspections. Full article
(This article belongs to the Special Issue Selected Papers about Sensor Application from 16th International Conference on Automation Technology)
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26 pages, 7174 KiB  
Article
A PCB Alignment System Using RST Template Matching with CUDA on Embedded GPU Board
by Minh-Tri Le, Ching-Ting Tu, Shu-Mei Guo and Jenn-Jier James Lien
Sensors 2020, 20(9), 2736; https://doi.org/10.3390/s20092736 - 11 May 2020
Cited by 5 | Viewed by 5034
Abstract
The fiducial-marks-based alignment process is one of the most critical steps in printed circuit board (PCB) manufacturing. In the alignment process, a machine vision technique is used to detect the fiducial marks and then adjust the position of the vision system in such [...] Read more.
The fiducial-marks-based alignment process is one of the most critical steps in printed circuit board (PCB) manufacturing. In the alignment process, a machine vision technique is used to detect the fiducial marks and then adjust the position of the vision system in such a way that it is aligned with the PCB. The present study proposed an embedded PCB alignment system, in which a rotation, scale and translation (RST) template-matching algorithm was employed to locate the marks on the PCB surface. The coordinates and angles of the detected marks were then compared with the reference values which were set by users, and the difference between them was used to adjust the position of the vision system accordingly. To improve the positioning accuracy, the angle and location matching process was performed in refinement processes. To overcome the matching time, in the present study we accelerated the rotation matching by eliminating the weak features in the scanning process and converting the normalized cross correlation (NCC) formula to a sum of products. Moreover, the scanning time was reduced by implementing the entire RST process in parallel on threads of a graphics processing unit (GPU) by applying hash functions to find refined positions in the refinement matching process. The experimental results showed that the resulting matching time was around 32× faster than that achieved on a conventional central processing unit (CPU) for a test image size of 1280 × 960 pixels. Furthermore, the precision of the alignment process achieved a considerable result with a tolerance of 36.4 μm. Full article
(This article belongs to the Special Issue Selected Papers about Sensor Application from 16th International Conference on Automation Technology)
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13 pages, 3444 KiB  
Article
Linear Displacement Calibration System Integrated with a Novel Auto-Alignment Module for Optical Axes
by Yi-Chieh Shih, Pi-Cheng Tung, Yung-Cheng Wang, Lih-Horng Shyu and Eberhard Manske
Sensors 2020, 20(9), 2462; https://doi.org/10.3390/s20092462 - 26 Apr 2020
Cited by 3 | Viewed by 2644
Abstract
The quality of processed workpieces is affected directly by the precision of the linear stage. Therefore, the linear displacement calibration of machine tools must be implemented before delivery and after employment for a period of time. How to perform a precise calibration with [...] Read more.
The quality of processed workpieces is affected directly by the precision of the linear stage. Therefore, the linear displacement calibration of machine tools must be implemented before delivery and after employment for a period of time. How to perform a precise calibration with high inspection efficiency is a critical issue in the precision mechanical engineering industry. In this study, the self-developed system integrated by the measurement module based on the common path Fabry–Pérot interferometer for linear displacement and the auto-alignment module for optical axes was proposed to realize the automatic linear displacement calibration of the linear stages. The measurement performance of the developed structure was verified experimentally. With the auto-alignment module, the cosine error was reduced to 0.36 nm and the entire procedure accomplished within 75 s without the limitation of the perceived resolution of the human eye, operational experience, and the risk of misalignment and broken cable. According to the comparison of experimental results for the linear displacement, the repeatability of the proposed measurement module was less than 0.171 μm. After the compensation procedure according to the linear displacement calibration, the systematic positional deviation, repeatability, and accuracy of the linear axis could be improved to 4 μm, 1 μm, and 5 μm respectively. Hence, the calibration efficiency can be improved by 80% with the proposed compact system, which is beneficial for the linear displacement calibration of machine tools in the precision mechanical engineering industry. Full article
(This article belongs to the Special Issue Selected Papers about Sensor Application from 16th International Conference on Automation Technology)
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17 pages, 8601 KiB  
Article
In Situ Diagnosis of Industrial Motors by Using Vision-Based Smart Sensing Technology
by Ching-Yuan Chang, En-Chieh Chang and Chi-Wen Huang
Sensors 2019, 19(24), 5340; https://doi.org/10.3390/s19245340 - 04 Dec 2019
Cited by 10 | Viewed by 3928
Abstract
This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm [...] Read more.
This study uses machine vision, feature extraction, and support vector machine (SVM) to compose a vibration monitoring system (VMS) for an in situ evaluation of the performance of industrial motors. The vision-based system respectively offers a spatial and temporal resolution of 1.4 µm and 16.6 ms after the image calibration and the benchmark of a laser displacement sensor (LDS). The embedded program of machine vision has used zero-mean normalized correlation (ZNCC) and peak finding (PF) for tracking the registered characteristics on the object surface. The calibrated VMS provides time–displacement curves related to both horizontal and vertical directions, promising remote inspections of selected points without attaching additional markers or sensors. The experimental setup of the VMS is cost-effective and uncomplicated, supporting universal combinations between the imaging system and computational devices. The procedures of the proposed scheme are (1) setting up a digital camera, (2) calibrating the imaging system, (3) retrieving the data of image streaming, (4) executing the ZNCC criteria, and providing the time–displacement results of selected points. The experiment setup of the proposed VMS is straightforward and can cooperate with surveillances in industrial environments. The embedded program upgrades the functionality of the camera system from the events monitoring to remote measurement without the additional cost of attaching sensors on motors or targets. Edge nodes equipped with the image-tracking program serve as the physical layer and upload the extracted features to a cloud server via the wireless sensor network (WSN). The VMS can provide customized services under the architecture of the cyber–physical system (CPS), and this research offers an early warning alarm of the mechanical system before unexpected downtime. Based on the smart sensing technology, the in situ diagnosis of industrial motors given from the VMS enables preventative maintenance and contributes to the precision measurement of intelligent automation. Full article
(This article belongs to the Special Issue Selected Papers about Sensor Application from 16th International Conference on Automation Technology)
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