Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Advanced Manufacturing Technologies and Their Applications
share announcement

Advanced Manufacturing Technologies and Their Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (15 April 2021) | Viewed by 34790

Special Issue Editor

Prof. Dr. Richard (Chunhui) Yang

E-Mail Website
Guest Editor
School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
Interests: additive manufacturing; advanced manufacturing; multiscale modelling and simulations of advanced engineering materials and structures; engineering numerical methods and their applications; digital material representation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The manufacturing industry is currently experiencing a worldwide transformation from traditional manufacturing to advanced manufacturing under the 4th Industrial revolution (Industry 4.0), which features Internet-of-Things (IoT) technologies, Big Data Analytics, Cloud Computing, Cyber Security, System Integration, Smart Robotics, Augmented Reality, and Additive Manufacturing and Simulations. This Special Issue is aiming to become a collection of high-quality articles contributed by both practitioners and researchers in the relevant fields of research on advanced manufacturing, from theory to applications. Topics of interest for publication include but are not limited to:

  • Additive manufacturing (3D printing) of metals, polymers, ceramics and composites
  • Fabrication and evaluation of novel engineering materials
  • Advanced sensing technologies for process monitoring and real-time control
  • Artificial intelligence, machine learning, and intelligent production systems
  • Augmented and virtual reality for manufacturing
  • Internet of things (IoT) and big data for manufacturing
  • Smart robotics, control, and automation

We would like to sincerely invite you to submit your manuscripts to this Special Issue of Applied Sciences on “Advanced Manufacturing Technologies and Their Applications”, which fall within its scope and could be experimental and/or theoretical papers, technical notes, review papers, etc.

Thank you very much for your attention. I look forward to receiving your submissions.

Yours Sincerely,

Prof. Richard (Chunhui) Yang
Guest Editor

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. Applied Sciences 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.

Keywords

  • Additive manufacturing (3D printing)
  • Advanced materials
  • Advanced sensor tecnologies
  • Artificial intelligence, machine learning, and intelligent production systems
  • Augmented and virtual reality
  • Internet of things (IoT) and big data for manufacturing
  • Nanotechnologies
  • Biotechnologies
  • Smart robotics, control, and automation

Published Papers (7 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

14 pages, 3091 KiB  
Article
Nozzle Thermal Estimation for Fused Filament Fabricating 3D Printer Using Temporal Convolutional Neural Networks
by Danielle Jaye S. Agron, Jae-Min Lee and Dong-Seong Kim
Appl. Sci. 2021, 11(14), 6424; https://doi.org/10.3390/app11146424 - 12 Jul 2021
Cited by 11 | Viewed by 2258
Abstract
A preventive maintenance embedded for the fused deposition modeling (FDM) printing technique is proposed. A monitoring and control integrated system is developed to reduce the risk of having thermal degradation on the fabricated products and prevent printing failure; nozzle clogging. As for the [...] Read more.
A preventive maintenance embedded for the fused deposition modeling (FDM) printing technique is proposed. A monitoring and control integrated system is developed to reduce the risk of having thermal degradation on the fabricated products and prevent printing failure; nozzle clogging. As for the monitoring program, the proposed temporal neural network with a two-stage sliding window strategy (TCN-TS-SW) is utilized to accurately provide the predicted thermal values of the nozzle tip. These estimated thermal values are utilized to be the stimulus of the control system that performs countermeasures to prevent the anomaly that is bound to happen. The performance of the proposed TCN-TS-SW is presented in three case studies. The first scenario is when the proposed system outperforms the other existing machine learning algorithms namely multi-look back LSTM, GRU, LSTM, and the generic TCN architecture in terms of obtaining the highest training accuracy and lowest training loss. TCN-TS-SW also outperformed the mentioned algorithms in terms of prediction accuracy measured by the performance metrics like RMSE, MAE, and R2 scores. In the second case, the effect of varying the window length and the changing length of the forecasting horizon. This experiment reveals the optimized parameters for the network to produce an accurate nozzle thermal estimation. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications)
►▼ Show Figures

Figure 1

18 pages, 4968 KiB  
Article
Design and Control of a Open-Source, Low Cost, 3D Printed Dynamic Quadruped Robot
by Joonyoung Kim, Taewoong Kang, Dongwoon Song and Seung-Joon Yi
Appl. Sci. 2021, 11(9), 3762; https://doi.org/10.3390/app11093762 - 22 Apr 2021
Cited by 9 | Viewed by 9266
Abstract
In this paper, we present a new open source dynamic quadruped robot, PADWQ (pronounced pa-dook), which features 12 torque controlled quasi direct drive joints with high control bandwidth, as well as onboard depth sensor and GPU-equipped computer that allows for a highly dynamic [...] Read more.
In this paper, we present a new open source dynamic quadruped robot, PADWQ (pronounced pa-dook), which features 12 torque controlled quasi direct drive joints with high control bandwidth, as well as onboard depth sensor and GPU-equipped computer that allows for a highly dynamic locomotion over uncertain terrains. In contrast to other dynamic quadruped robots based on custom actuator and machined metal structural parts, the PADWQ is entirely built from off the shelf components and standard 3D printed plastic structural parts, which allows for a rapid distribution and duplication without the need for advanced machining process. To make sure that the plastic structural parts can withstand the stress of dynamic locomotion, we performed finite element analysis (FEA) on leg structural parts as well as a continuous walking test using the physical robot, both of which the robot has passed successfully. We hope this work to help a wide range of researchers and engineers that need an affordable, highly capable and easily customizable quadruped robot. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications)
►▼ Show Figures

Figure 1

15 pages, 4433 KiB  
Article
Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints
by Tian-Yau Wu and Chi-Chen Lin
Appl. Sci. 2021, 11(5), 2137; https://doi.org/10.3390/app11052137 - 28 Feb 2021
Cited by 16 | Viewed by 2366
Abstract
The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to [...] Read more.
The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications)
►▼ Show Figures

Figure 1

20 pages, 3758 KiB  
Article
Modeling and Simulation of a Flexible Manufacturing System—A Basic Component of Industry 4.0
by Adriana Florescu and Sorin Adrian Barabas
Appl. Sci. 2020, 10(22), 8300; https://doi.org/10.3390/app10228300 - 23 Nov 2020
Cited by 55 | Viewed by 9031
Abstract
The field of Flexible Manufacturing Systems (FMS) has seen in recent years a dynamic development trend and can now be considered an integral part of intelligent manufacturing systems and a basis for digital manufacturing. Developing the factory of the future in an increasingly [...] Read more.
The field of Flexible Manufacturing Systems (FMS) has seen in recent years a dynamic development trend and can now be considered an integral part of intelligent manufacturing systems and a basis for digital manufacturing. Developing the factory of the future in an increasingly competitive industrial environment involves the study and analysis of some FMS key elements and managerial, technical, and innovative efforts. Using a new approach, thus paper presents a material flow design methodology for flexible manufacturing systems in order to establish the optimal architecture of the analyzed system. The research offers a solution for modeling and optimizing material flows in advanced manufacturing systems. By using a dedicated analysis and simulation software, the structure of the system can be established and specific technical and economic parameters can be determined for each processing and transport capacity. Different processing scenarios will be evaluated through virtual modeling and simulations in order to increase the performance and efficiency of the system. Thus, an interactive tool useful in the design and management of flexible manufacturing lines will be developed for companies operating in the industrial sector. The application of this paper is mainly in the field of development of intelligent manufacturing systems, where the control system will make and use simulations in order to analyze current parameters and to predict the future. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications)
►▼ Show Figures

Figure 1

16 pages, 1564 KiB  
Article
A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
by Gangping Tan, Qingshuang Chen, Changyin Li and Richard (Chunhui) Yang
Appl. Sci. 2020, 10(22), 8092; https://doi.org/10.3390/app10228092 - 15 Nov 2020
Cited by 3 | Viewed by 1991
Abstract
It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations [...] Read more.
It is vital to greatly reduce traffic noises emitted by motor vehicles during accelerating through determining limit values of noises and further improve technical specifications and comforts of these automobiles for automotive manufacturers. The United Nations Economic Commission for Europe (UNECE) R51 regulations define the noise limits for all vehicle categories, which are kept updating, and these noise limits are implemented by governments all over the world; however, the automobile manufactures need to estimate future values of noise limits for developing their next-generation vehicles. In this study, a machine learning model using the back-propagation neural network (BPNN) approach is developed to determine noise limits of a vehicle during accelerating by using historic data and predict its noise limits for future revisions of the UNECE R51 regulations. The proposed prediction model adopts the Levenberg-Marquardt algorithm which can automatically adapt its learning rate to train the model with input data, and at the same time randomly select the validation data and test data to verify the correlation and determine the accuracy of the prediction results. To showcase the proposed prediction model, acceleration noise limits from six historic data are used for training the model, and the noise limits at the seventh version can be predicted and validated. As the results achieve a required accuracy, vehicle noise limits in the next revision as the future eighth version can be predicted based on these data. It can be found that the obtained prediction results are much close to those noise limits defined in current regulations and negative error ratios are reduced significantly, compared to those values obtained using a quadratic regression model. As a result, the proposed BPNN model can predict future noise limits for the next revision of the UNECE R51automotive noise limit regulations. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications)
►▼ Show Figures

Figure 1

11 pages, 11970 KiB  
Article
Utilization of CD and DVD Pick-Up Heads for Scratch Inspection of Magnetic Disk in Dynamic State Using Microcontroller
by Achinee Polsawat, Warunee Tipcharoen and Apirat Siritaratiwat
Appl. Sci. 2020, 10(19), 6897; https://doi.org/10.3390/app10196897 - 01 Oct 2020
Cited by 1 | Viewed by 4129
Abstract
A non-destructive technique to inspect a scratch on all magnetic disks in the beginning process of hard disk drive (HDD) manufacturing by using CD and DVD pick-up heads as the detector is proposed. It requires a 100% disk inspection of micrometer-sized scratches in [...] Read more.
A non-destructive technique to inspect a scratch on all magnetic disks in the beginning process of hard disk drive (HDD) manufacturing by using CD and DVD pick-up heads as the detector is proposed. It requires a 100% disk inspection of micrometer-sized scratches in a quick measurement with low cost inventing. Most of the previous studies were in static state but this is the first time to be done in dynamic study using the microcontroller in order to promptly serve for industrial utilization. The size, position, and shape characteristic of scratches are examined using light reflection technique. The results show that, when the laser beam is targeted on a magnetic disk in a position, either scratch or non-scratch, the reflected light intensity differs. The DVD pick-up head can detect the width and the surface characteristic of the scratches, which is similar to the results from scanning electron microscope (SEM) for all scratches sizes less than 100 µm. It is also found that using a DVD pick-up head provides a better resolution of shape characteristic and roughness of scratches surface than a CD pick-up head. Hence, the scratch size of 10s µm scale on the magnetic disk can be accurately characterized by this proposed technique, which can be further utilized for magnetic disk inspection in the hard disk drive manufacturing process. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications)
►▼ Show Figures

Figure 1

20 pages, 6179 KiB  
Article
Effects of Tension–Compression Asymmetry on Bending of Steels
by Hamed Mehrabi, Richard (Chunhui) Yang and Baolin Wang
Appl. Sci. 2020, 10(9), 3339; https://doi.org/10.3390/app10093339 - 11 May 2020
Cited by 10 | Viewed by 4200
Abstract
Stainless steels (SUS) and dual-phase (DP) steels have tension-compression asymmetry (TCA) in mechanical responses to full loading cycles. This phenomenon can significantly influence sheet metal forming of such metals, however, it is difficult to describe this behaviour analytically. In this research, a novel [...] Read more.
Stainless steels (SUS) and dual-phase (DP) steels have tension-compression asymmetry (TCA) in mechanical responses to full loading cycles. This phenomenon can significantly influence sheet metal forming of such metals, however, it is difficult to describe this behaviour analytically. In this research, a novel analytical method for asymmetric elastic-plastic pure bending using the Cazacu–Barlat 2004 asymmetric yield function is proposed. It only uses material parameters in tension along with an asymmetry coefficient related to the yield function. Bending operations of SUS304 and DP980 are investigated as two case studies. In the pure bending for both SUS304 and DP980, moment–curvature diagrams are analytically obtained. Furthermore, linear and nonlinear springback behaviours of SUS304 are analytically investigated. Moreover, using the analytical model as a user-defined material, a numerical model is developed for both steels under pure bending. In the V-bending case of SUS304 with and without TCA effects, the springback behaviours of the material are investigated numerically. In addition, considering friction effects, the analytical method is further modified for predicting springback behaviours in the V-bending of 16 types of SUS304 with various strengths are determined. All the analytical and numerical results have good agreement with those experimental results from literature for validation. Full article
(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop