Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.4
Journals
Micromachines
Special Issues
Intelligent Precision Machining
share announcement

Intelligent Precision Machining

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "D：Materials and Processing".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 4683

Special Issue Editors

Prof. Dr. Jiang Guo

E-Mail Website
Guest Editor
School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China
Interests: intelligent precision machining; surface integrity; precision measurement
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yebing Tian

E-Mail Website
Guest Editor
Institute of Advanced Manufacturing, Shandong University of Technology, Zibo 255049, China
Interests: alloy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Caixu Yue

E-Mail Website
Guest Editor
School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
Interests: design of cutting tool, cutting technology; intelligent optimization of cutting process; fem simulation of cutting process
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The intelligent precision manufacturing concepts of Industry 4.0 shed some light and provide essential means for manufacturing those products with the rapid growth in the demand for advanced products and high-precision components for assorted applications in fields such as aerospace, biomedical, advanced optics, photonics and telecommunication, etc. The combination of artificial intelligence and manufacturing processes is indispensable for achieving high precision and ultra-smooth surface, which forms the backbone and support of today’s innovative technology industries. Accordingly, this Special Issue seeks to showcase regular research papers, reviews and communications that focus on the frontiers of intelligent precision machining. It will serve as a platform for the communication of the latest developments and innovations in intelligent precision machining technologies.

We look forward to receiving your contributions!

Prof. Dr. Jiang Guo
Prof. Dr. Yebing Tian
Prof. Dr. Caixu Yue
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. Micromachines is an international peer-reviewed open access monthly 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

  • manufacturing intelligence and informatics
  • Intelligent manufacturing process
  • precision manufacturing
  • multi-objective optimization
  • digital twin for process
  • real-time monitoring and decision making
  • machine Learning
  • manufacturing optimization and simulation
  • adaptive manufacturing

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 15434 KiB  
Article
A Method for Optimizing the Dwell Time of Optical Components in Magnetorheological Finishing Based on Particle Swarm Optimization
by Bo Gao, Bin Fan, Jia Wang, Xiang Wu and Qiang Xin
Micromachines 2024, 15(1), 18; https://doi.org/10.3390/mi15010018 - 21 Dec 2023
Cited by 1 | Viewed by 616
Abstract
In this paper, a dwell time optimization method based on the particle swarm optimization algorithm is proposed according to the pulse iteration principle in order to achieve high-precision magnetorheological finishing of optical components. The dwell time optimization method explores the optimal solution in [...] Read more.
In this paper, a dwell time optimization method based on the particle swarm optimization algorithm is proposed according to the pulse iteration principle in order to achieve high-precision magnetorheological finishing of optical components. The dwell time optimization method explores the optimal solution in the solution space by comparing the accuracy value of the final surface with the set value. In this way, the dwell time optimization method was able to achieve global optimization of the overall dwell times and each dwell time point, ultimately realizing the high-precision processing of a surface. Through the simulation of two Φ156 mm asphaltic mirrors (1# and 2#), the root-mean-square (RMS) and peak–valley (PV) values of 1# converged from the initial values of 169.164 nm and 1161.69 nm to 24.79 nm and 911.53 nm. Similarly, the RMS and PV values of 2# converged from the initial values of 187.27 nm and 1694.05 nm to 31.76 nm and 1045.61 nm. The simulation results showed that compared with the general pulse iteration method, the proposed algorithm could obtain a more accurate dwell time distribution of each point under the condition of almost the same processing time, subsequently acquiring a better convergence surface and reducing mid-spatial error. Finally, the accuracy of the optimization algorithm was verified through experiments. The experimental results demonstrated that the optimized algorithm could be used to perform high-precision surface machining. Overall, this optimization method provides a solution for dwell time calculation in the process of the magnetorheological finishing of optical components. Full article
(This article belongs to the Special Issue Intelligent Precision Machining)
►▼ Show Figures

Figure 1

13 pages, 3845 KiB  
Article
Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data Fusion
by Suiyan Shang, Chunjin Wang, Xiaoliang Liang, Chi Fai Cheung and Pai Zheng
Micromachines 2023, 14(11), 2016; https://doi.org/10.3390/mi14112016 - 29 Oct 2023
Cited by 1 | Viewed by 1174
Abstract
This paper pioneers the use of the extreme learning machine (ELM) approach for surface roughness prediction in ultra-precision milling, leveraging the excellent fitting ability with small datasets and the fast learning speed of the extreme learning machine method. By providing abundant machining information, [...] Read more.
This paper pioneers the use of the extreme learning machine (ELM) approach for surface roughness prediction in ultra-precision milling, leveraging the excellent fitting ability with small datasets and the fast learning speed of the extreme learning machine method. By providing abundant machining information, the machining parameters and force signal data are fused on the feature level to further improve ELM prediction accuracy. An ultra-precision milling experiment was designed and conducted to verify our proposed data-fusion-based ELM method. The results show that the ELM with data fusion outperforms other state-of-art methods in surface roughness prediction. It achieves an impressively low mean absolute percentage error of 1.6% while requiring a mere 18 s for model training. Full article
(This article belongs to the Special Issue Intelligent Precision Machining)
►▼ Show Figures

Figure 1

15 pages, 3718 KiB  
Article
Energy Prediction Models and Distributed Analysis of the Grinding Process of Sustainable Manufacturing
by Yebing Tian, Jinling Wang, Xintao Hu, Xiaomei Song, Jinguo Han and Jinhui Wang
Micromachines 2023, 14(8), 1603; https://doi.org/10.3390/mi14081603 - 14 Aug 2023
Cited by 1 | Viewed by 934
Abstract
Grinding is a critical surface-finishing process in the manufacturing industry. One of the challenging problems is that the specific grinding energy is greater than in ordinary procedures, while energy efficiency is lower. However, an integrated energy model and analysis of energy distribution during [...] Read more.
Grinding is a critical surface-finishing process in the manufacturing industry. One of the challenging problems is that the specific grinding energy is greater than in ordinary procedures, while energy efficiency is lower. However, an integrated energy model and analysis of energy distribution during grinding is still lacking. To bridge this gap, the grinding time history is first built to describe the cyclic movement during air-cuttings, feedings, and cuttings. Steady and transient power features during high-speed rotations along the spindle and repeated intermittent feeding movements along the x-, y-, and z-axes are also analysed. Energy prediction models, which include specific movement stages such as cutting-in, stable cutting, and cutting-out along the spindle, as well as infeed and turning along the three infeed axes, are then established. To investigate model parameters, 10 experimental groups were analysed using the Gauss-Newton gradient method. Four testing trials demonstrate that the accuracy of the suggested model is acceptable, with errors of 5%. Energy efficiency and energy distributions for various components and motion stages are also analysed. Low-power chip design, lightweight worktable utilization, and minimal lubricant quantities are advised. Furthermore, it is an excellent choice for optimizing grinding parameters in current equipment. Full article
(This article belongs to the Special Issue Intelligent Precision Machining)
►▼ Show Figures

Figure 1

18 pages, 4370 KiB  
Article
Automated Industrial Composite Fiber Orientation Inspection Using Attention-Based Normalized Deep Hough Network
by Yuanye Xu, Yinlong Zhang and Wei Liang
Micromachines 2023, 14(4), 879; https://doi.org/10.3390/mi14040879 - 19 Apr 2023
Viewed by 1392
Abstract
Fiber-reinforced composites (FRC) are widely used in various fields due to their excellent mechanical properties. The mechanical properties of FRC are significantly governed by the orientation of fibers in the composite. Automated visual inspection is the most promising method in measuring fiber orientation, [...] Read more.
Fiber-reinforced composites (FRC) are widely used in various fields due to their excellent mechanical properties. The mechanical properties of FRC are significantly governed by the orientation of fibers in the composite. Automated visual inspection is the most promising method in measuring fiber orientation, which utilizes image processing algorithms to analyze the texture images of FRC. The deep Hough Transform (DHT) is a powerful image processing method for automated visual inspection, as the “line-like” structures of the fiber texture in FRC can be efficiently detected. However, the DHT still suffers from sensitivity to background anomalies and longline segments anomalies, which leads to degraded performance of fiber orientation measurement. To reduce the sensitivity to background anomalies and longline segments anomalies, we introduce the deep Hough normalization. It normalizes the accumulated votes in the deep Hough space by the length of the corresponding line segment, making it easier for DHT to detect short, true “line-like” structures. To reduce the sensitivity to background anomalies, we design an attention-based deep Hough network (DHN) that integrates attention network and Hough network. The network effectively eliminates background anomalies, identifies important fiber regions, and detects their orientations in FRC images. To better investigate the fiber orientation measurement methods of FRC in real-world scenarios with various types of anomalies, three datasets have been established and our proposed method has been evaluated extensively on them. The experimental results and analysis prove that the proposed methods achieve the competitive performance against the state-of-the-art in F-measure, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE). Full article
(This article belongs to the Special Issue Intelligent Precision Machining)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop