Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Browser

Advanced Manufacturing Technologies and Their Applications, Volume II

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Additive Manufacturing Technologies".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13846

Share This Special Issue

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.

Prof. Dr. 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 (10 papers)

Download All Papers

Research

Jump to: Other

27 pages, 15445 KiB

Open AccessArticle

Evaluation on Material Anisotropy of Acrylonitrile Butadiene Styrene Printed via Fused Deposition Modelling

by Nima Zohdi, Phan Quoc Khang Nguyen and Richard (Chunhui) Yang

Appl. Sci. 2024, 14(5), 1870; https://doi.org/10.3390/app14051870 - 24 Feb 2024

Viewed by 664

Abstract

Thermoplastic polymers are widely used in industry to generate parts with reasonable production costs, lightweight, chemical stability, sustainability, and recyclability compared to other materials such as metals, metalloids, or even thermoset polymers. The innovative additive manufacturing (AM) techniques, e.g., fused deposition modelling (FDM), can be used to fabricate thermoplastic products with complex geometries and specific properties. However, the mechanical integrity of those FDM-printed plastic parts can be greatly impacted by a phenomenon named material anisotropy. In this study, an experimental study on a popular 3D printing polymer material—acrylonitrile butadiene styrene (ABS)—is performed to determine how FDM process parameters affect the mechanical properties of the printed ABS parts. This study uniquely concentrates on investigating mechanical anisotropy in FDM-printed ABS, delving into a combination of key printing parameters for a comprehensive exploration. Meanwhile, a finite-element-based numerical analysis is also utilised to numerically evaluate the influences of infill percentage and build orientations on the mechanical properties of the 3D-printed ABS materials for comparison. It generates a better understanding of material anisotropy and helps to find the optimal FDM process parameters to print high-quality ABS parts and may attract industrial interests in transitioning from traditional ABS part production methods such as injection moulding or hot pressing to additive manufacturing. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

21 pages, 5869 KiB

Open AccessArticle

The Mechanical Properties of Functionally Graded Lattice Structures Derived Using Computer-Aided Design for Additive Manufacturing

by Neslihan Top, İsmail Şahin and Harun Gökçe

Appl. Sci. 2023, 13(21), 11667; https://doi.org/10.3390/app132111667 - 25 Oct 2023

Viewed by 1432

Abstract

This study aims to investigate the mechanical properties of Functionally Graded Lattice Structures (FGLSs) and to determine their industrial application possibilities through additive manufacturing. For this purpose, lattice structures with uniform and horizontal, vertical and radially graded configurations are designed using auxetic unit cells were fabricated with RGD720 photopolymer resin using Material Jetting. FGLSs are compared with uniform structures in regards with deformation behavior, structural strength and energy absorption. The results showed that the most significant deviation in the strut diameters of the uniform lattice structures was seen in the rotation lattice structure at 8.2%. The lowest deviation was seen in the chiral structure, which deviated by 5.4%. The lowest deviations (between 3.4% and 9%) in FGLSs were obtained in chiral structures. The highest relative density value (0.3049 g/cm³) among all configurations was observed in the vertically graded chiral structure. The lowest relative density value (0.1865 g/cm³) was obtained in uniform re-entrant structures. According to the compression test results, the highest compressive stress (2.61513 MPa) and elastic modulus (84.63192 MPa) were formed in the rotation structure. The maximum energy absorption capacity value (19.381 KJ) and the maximum specific energy absorption value (3649.905 KJ/kg) were obtained in the uniform chiral structure. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

15 pages, 16258 KiB

Open AccessArticle

An Intelligent Approach to the Unit Nesting Problem of Coil Material

by Dezhong Qi, Wenguang Yang, Lu Ding, Yunzhi Wu, Chen Tian, Lifeng Yuan, Yuanfang Wang and Zhigao Huang

Appl. Sci. 2023, 13(16), 9067; https://doi.org/10.3390/app13169067 - 08 Aug 2023

Viewed by 731

Abstract

With the popularization of small batch production, the main cutting method for sheet metal parts has changed. Laser cutting has become the main production method for coil material cutting. Developing an irregular part nesting method for the continuous cutting of coil material is urgent. Based on the coil material cutting process, this paper proposes an intelligent approach for the unit nesting problem of coil material. Firstly, a unit nesting model of coil material was constructed. Secondly, an intelligent approach using an improved marine predator algorithm was used to solve this model. In solving the model, the minimum nesting unit was continuously updated by changing the position, angle, and quantity of the nesting parts. Thirdly, the geometric characteristics of this minimum nesting unit were extracted. Finally, the nesting units for production were obtained using a single row or opposite row of the minimum nesting unit. The computational results and comparison prove that the presented approach is feasible and effective in improving material utilization, reducing production costs, and meeting the requirements of the production site. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

17 pages, 547 KiB

Open AccessArticle

Identical Parallel Machine Scheduling Considering Workload Smoothness Index

by Zhaojie Wang, Feifeng Zheng and Ming Liu

Appl. Sci. 2023, 13(15), 8720; https://doi.org/10.3390/app13158720 - 28 Jul 2023

Viewed by 565

Abstract

Workload balance is significant in the manufacturing industry. However, on the one hand, some existing specific criteria cannot achieve the minimization workload imbalance of parallel machines. On the other hand, there are few algorithms in existing studies that can effectively solve the parallel machine scheduling problem with the objective of minimizing workload imbalance. Inspired by this, we investigate an identical parallel machine scheduling problem with the objective of the minimum workload smoothness index. We first establish a mathematical model for the considered problem and then linearize its objective function. We prove the NP-hardness of the problem by reducing the PARTITION problem to it, and we provide both the upper bound and lower bound of the studied problem. An efficient genetic algorithm and an improved list scheduling algorithm are also proposed to efficiently address the considered problem. The numerical results demonstrate the effectiveness of the proposed methods. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

14 pages, 10289 KiB

Open AccessArticle

A Deep Learning Technique for Optical Inspection of Color Contact Lenses

by Tae-yun Kim, Dabin Park, Heewon Moon and Suk-seung Hwang

Appl. Sci. 2023, 13(10), 5966; https://doi.org/10.3390/app13105966 - 12 May 2023

Cited by 2 | Viewed by 1768

Abstract

Colored contact lenses have gained popularity in recent years. However, their production process is plagued by low efficiency, which is attributed to the complex nature of the lens color patterns. The manufacturing process involves multiple complex steps that can introduce defects or inconsistencies into the contact lenses. Moreover, manual inspection of a considerable number of contact lenses that are produced inefficiently in terms of consistency and quality by humans is prevalent. Alternatively, automatic optical inspection (AOI) systems have been developed to perform quality-control checks on colored contact lenses. However, their accuracy is limited due to the increasing complexity of the lens color patterns. To address these issues, convolutional neural networks have been used to detect and classify defects in colored contact lenses. This study aims to provide a comprehensive guide for AOI systems using artificial intelligence in the colored contact lens manufacturing process, including the benefits and challenges of using these systems. Further, future research directions to achieve a classification accuracy of >95%, which is the human recognition rate, are explored. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

17 pages, 20372 KiB

Open AccessArticle

Fabrication and Characterization of Gel-Forming Cr₂O₃ Abrasive Tools for Sapphire Substrate Polishing

by Liang Zhao, Kaiping Feng, Binghai Lyu, Tianchen Zhao and Zhaozhong Zhou

Appl. Sci. 2022, 12(24), 12949; https://doi.org/10.3390/app122412949 - 16 Dec 2022

Cited by 1 | Viewed by 1134

Abstract

This paper proposes a gel-formed abrasive tool to address the problem of abrasive agglomeration in a traditional hot-pressing abrasive tool. The effect of Polyimide resin content on the mechanical properties of the gel abrasive tools were tested, and a comparison of the mechanical properties of the gel abrasive tool and the hot-pressing tool was conducted. An orthogonal experiment was conducted to explore the best combination of machining parameters. A polishing experiment of sapphire was conducted to compare the processing effect of the gel abrasive tool and hot-pressing tool. The results from testing the mechanical properties showed that the tensile, flexural, and impact strength of the gel abrasive tool was better than that of the hot-pressing abrasive tool. The results of the orthogonal experiment showed that the best process parameters of the gel abrasive tool were a spindle speed of 900 rpm, a feed rate of 8 μm/min, and a grinding depth of 16 μm. The polishing experiment showed that the gel abrasive tool had a better processing effect on sapphire. The sapphire surface processed by the gel abrasive tool had no deep scratches, and an ultrasmooth surface could be obtained after chemical mechanical polishing (CMP). Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

18 pages, 6175 KiB

Open AccessArticle

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

by Meet Gor, Aashutosh Dobriyal, Vishal Wankhede, Pankaj Sahlot, Krzysztof Grzelak, Janusz Kluczyński and Jakub Łuszczek

Appl. Sci. 2022, 12(14), 7271; https://doi.org/10.3390/app12147271 - 19 Jul 2022

Cited by 14 | Viewed by 2266

Abstract

Machine learning (ML) is one of the artificial intelligence tools which uses past data to learn the relationship between input and output and helps to predict future trends. Powder bed fusion additive manufacturing (PBF-AM) is extensively used for a wide range of applications in the industry. The AM process establishment for new material is a crucial task with trial-and-error approaches. In this work, ML techniques have been applied for the prediction of the density of PBF-AM. Density is the most vital property in evaluating the overall quality of the AM building part. The ML techniques, namely, artificial neural network (ANN), K-nearest neighbor (KNN), support vector machine (SVM), and linear regression (LR), are used to develop a model for predicting the density of the stainless steel (SS) 316L build part. These four methods are validated using R-squared values and different error functions to compare the predicted result. The ANN and SVM model performed well with the R-square value of 0.95 and 0.923, respectively, for the density prediction. The ML models would be beneficial for the prediction of the process parameters. Further, the developed ML model would also be helpful for the future application of ML in additive manufacturing. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

23 pages, 5674 KiB

Open AccessArticle

Optimal Placement of Vibration Sensors for Industrial Robots Based on Bayesian Theory

by Qiao Hu, Yangkun Zhang, Xingju Xie, Wenbin Su, Yangyang Li, Liuhao Shan and Xiaojie Yu

Appl. Sci. 2022, 12(12), 6086; https://doi.org/10.3390/app12126086 - 15 Jun 2022

Cited by 3 | Viewed by 1330

Abstract

This paper presents an optimal sensor placement method for vibration signal acquisition in the field of industrial robot health monitoring and fault diagnosis. Based on the general formula of Bayes and relative entropy, the evaluation function of sensor placement is deduced, and the modal confidence matrix is used to express the redundancy of sensor placement. The optimal placement of vibration sensors is described as a discrete variable optimization problem, which is defined as whether the existing sensor layout can obtain joint state information efficiently. The initial layout of the sensor was obtained from the structural simulation results of the industrial robots, and the initial layout was optimized by the derived objective function. The efficiency of the optimized layout in capturing joint state information is proven by the validation experiment with a simulation model. The problem of popularizing the optimization method in engineering is solved by a verification experiment without a simulation model. The optimal sensor placement method provides a theoretical basis for industrial robots to acquire vibration data effectively. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

13 pages, 4996 KiB

Open AccessArticle

Robotic Magnetorheological Finishing Technology Based on Constant Polishing Force Control

by Lin Zhang, Chunlei Zhang and Wei Fan

Appl. Sci. 2022, 12(8), 3737; https://doi.org/10.3390/app12083737 - 07 Apr 2022

Cited by 8 | Viewed by 2074

Abstract

The normal positioning error hinders the use of magnetorheological finishing (MRF) in robotic polishing. In this paper, the influence of robotic normal positioning error on the MRF removal rate is revealed, and a force-controlled end-effector for the robotic MRF process is presented. The developed end-effector is integrated into a six-axis industrial robot, and the robot positions the end-effector while the end-effector realizes the constant force control. A fused silicon mirror is polished, and the result shows that the proposed device effectively compensates for robotic normal positioning error and simultaneously maintains the stability of the polishing process. After deterministic polishing, the PV (peak to valley) of the figure is reduced from

126.56 nm

56.95 nm

, and the RMS (root mean square) is reduced from

22.15 nm

7.59 nm

. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures

Figure 1

Other

Jump to: Research

15 pages, 23705 KiB

Open AccessTechnical Note

A Parameter Self-Tuning Decoupling Controller Based on an Improved ADRC for Tension Systems

by Guoli Ju, Shanhui Liu, Keliang Wei, Haodi Ding and Chaoyue Wang

Appl. Sci. 2023, 13(19), 11085; https://doi.org/10.3390/app131911085 - 09 Oct 2023

Cited by 1 | Viewed by 718

Abstract

Aiming at the problems of strong coupling and time-varying parameters in the tension systems of roll-to-roll coating machines, this paper outlines the design of a parameter self-tuning decoupling controller based on an improved active disturbance rejection controller (ADRC) for tension systems. First, we established a nonlinear coupled model for a global tension system based on the roll-to-roll coating machine tension constituent unit and working principle. Second, we introduced virtual quantities and actual control quantities; then, we applied an ADRC for decoupling control of the tension system based on a nonlinear coupling model, using a genetic algorithm (GA) to achieve parameters tuning in the ADRC. Finally, both the traditional proportional–integral–differential (PID) controller and the designed controller were simulated to evaluate their anti-interference and decoupling performance. Furthermore, the performances of several controllers were compared to the results obtained by other researchers. The result found that the parameter self-tuning decoupling controller based on an improved ADRC for tension systems exhibited better decoupling control performance and that the controller was able to achieve precise control of tension. Full article

(This article belongs to the Special Issue Advanced Manufacturing Technologies and Their Applications, Volume II)

► Show Figures