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Special Issue "Applications of Manufacturing and Measurement Sensors"

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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: 31 August 2023 | Viewed by 5196

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Special Issue Editors

Prof. Dr. Shichang Du

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Guest Editor

Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: intelligent manufacturing; measurement application; quality control; manufacturing system; machine vision

Dr. Yiping Shao

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Guest Editor

College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Interests: measurement and metrology; intelligent manufacturing; quality big data; visual inspection; monitoring and diagnosis; intelligent logistics equipment

Dr. Delin Huang

E-Mail Website
Guest Editor

College of Mechanical Engineering, Donghua University, Shanghai 201620, China
Interests: surface monitoring; defect detection; point cloud data processing; machine vision; intelligent manufacturing

Special Issue Information

Dear Colleagues,

Measurement sensors are an essential technology in advanced manufacturing, with substantial significance in the context of the engineering field. With tighter tolerance and higher performance standards, there is a need to develop fast and efficient applications of manufacturing and measurement sensors to satisfy the specifications of various products’ functional attributes.

As one of the experts in the field, I would like to cordially invite you to submit an original paper to this Special Issue, which we wish to publish to spread your brilliant ideas throughout communication media.

The aims and scope of this Special Issue include, but are not limited to:

Advanced measurement sensors;
Measurement and sensing applications;
Manufacturing system;
Applications of sensors in intelligent logistics equipment;
Quality monitoring and control;
Machine vision and applications;
Methods of detection.

Prof. Dr. Shichang Du
Dr. Yiping Shao
Dr. Delin Huang
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 2400 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.

Published Papers (5 papers)

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Research

Open AccessArticle

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

and

Sensors 2023, 23(7), 3676; https://doi.org/10.3390/s23073676 - 01 Apr 2023

Viewed by 1064

Abstract

High−precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch–Tung–Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF−RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF−RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF−RTS algorithm is more robust and provides more accurate localization. Full article

(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors)

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Open AccessArticle

Sensitivity Analysis of RV Reducer Rotation Error Based on Deep Gaussian Processes

and

Sensors 2023, 23(7), 3579; https://doi.org/10.3390/s23073579 - 29 Mar 2023

Viewed by 773

Abstract

The rotation error is the most important quality characteristic index of a rotate vector (RV) reducer, and it is difficult to accurately optimize the design of a RV reducer, such as the Taguchi type, due to the many factors affecting the rotation error and the serious coupling effect among the factors. This paper analyzes the RV reducer rotation error and each factor based on the deep Gaussian processes (DeepGP) model and Sobol sensitivity analysis(SA) method. Firstly, using the optimal Latin hypercube sampling (OLHS) approach and the DeepGP model, a high-precision regression prediction model of the rotation error and each affecting factor was created. On the basis of the prediction model, the Sobol method was used to conduct a global SA of the factors influencing the rotation error and to compare the coupling relationship between the factors. The results show that the OLHS method and the DeepGP model are suitable for predicting the rotation error in this paper, and the accuracy of the prediction model constructed based on both of them is as high as 95%. The rotation error mainly depends on the influencing factors in the second stage cycloidal pinwheel drive part. The primary involute planetary part and planetary output carrier’s rotation error factors have little effect. The coupling effects between the matching clearance between the pin gear and needle gear hole (

δ J

) and the circular position error of the needle gear hole (

δ t

) is noticeably stronger. Full article

(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors)

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Open AccessArticle

A Point Cloud Data-Driven Pallet Pose Estimation Method Using an Active Binocular Vision Sensor

and

Sensors 2023, 23(3), 1217; https://doi.org/10.3390/s23031217 - 20 Jan 2023

Viewed by 839

Abstract

Pallet pose estimation is one of the key technologies for automated fork pickup of driverless industrial trucks. Due to the complex working environment and the enormous amount of data, the existing pose estimation approaches cannot meet the working requirements of intelligent logistics equipment in terms of high accuracy and real time. A point cloud data-driven pallet pose estimation method using an active binocular vision sensor is proposed, which consists of point cloud preprocessing, Adaptive Gaussian Weight-based Fast Point Feature Histogram extraction and point cloud registration. The proposed method overcomes the shortcomings of traditional pose estimation methods, such as poor robustness, time consumption and low accuracy, and realizes the efficient and accurate estimation of pallet pose for driverless industrial trucks. Compared with traditional Fast Point Feature Histogram and Signature of Histogram of Orientation, the experimental results show that the proposed approach is superior to the above two methods, improving the accuracy by over 35% and reducing the feature extraction time by over 30%, thereby verifying the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors)

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Open AccessArticle

High-Precision Tribometer for Studies of Adhesive Contacts

and

Sensors 2023, 23(1), 456; https://doi.org/10.3390/s23010456 - 01 Jan 2023

Cited by 2 | Viewed by 1035

Abstract

Herein, we describe the design of a laboratory setup operating as a high-precision tribometer. The whole design procedure is presented, starting with a concept, followed by the creation of an exact 3D model and final assembly of all functional parts. The functional idea of the setup is based on a previously designed device that was used to perform more simple tasks. A series of experiments revealed certain disadvantages of the initial setup, for which pertinent solutions were found and implemented. Processing and correction of the data obtained from the device are demonstrated with an example involving backlash and signal drift errors. Correction of both linear and non-linear signal drift errors is considered. We also show that, depending on the research interests, the developed equipment can be further modified by alternating its peripheral parts without changing the main frame of the device. Full article

(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors)

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Open AccessArticle

3D Metrology Using One Camera with Rotating Anamorphic Lenses

Xiaobo Chen

Jinkai Zhang

and

Juntong Xi

Sensors 2022, 22(21), 8407; https://doi.org/10.3390/s22218407 - 01 Nov 2022

Viewed by 956

Abstract

In this paper, a novel 3D metrology method using one camera with rotating anamorphic lenses is presented based on the characteristics of double optical centers for anamorphic imaging. When the anamorphic lens rotates −90° around its optical axis, the 3D data of the measured object can be reconstructed from the two anamorphic images captured before and after the anamorphic rotation. The anamorphic lens imaging model and a polynomial anamorphic distortion model are firstly proposed. Then, a 3D reconstruction model using one camera with rotating anamorphic lenses is presented. Experiments were carried out to validate the proposed method and evaluate its measurement accuracy. Compared with stereo vision, the main advantage of the proposed 3D metrology approach is the simplicity of point matching, which makes it suitable for developing compact sensors for fast 3D measurements, such as car navigation applications. Full article

(This article belongs to the Special Issue Applications of Manufacturing and Measurement Sensors)

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