Submit to Micromachines Review for Micromachines Propose a Special Issue

Journal Menu

Journal Browser

Recent Advances in Precision Manufacturing: Materials, Methods, and Potential Applications

Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 10588

Share This Special Issue

Special Issue Editor

Dr. Uzair Sajjad

E-Mail Website
Guest Editor

Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: phase change heat transfer; boiling; evaporation; evaporative cooling; desiccant air-conditioning; dehumidification; heat transfer; artificial intelligence; deep learning; optimization; explainable artificial intelligence; additive manufacturing; surface engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the main problems in today's world is the creation of advanced compact systems. Among other things, the manufacture of smart materials is a field worth exploring. The use of contemporary methods to create composite materials with improved qualities is a constant research focus. These manufacturing methods can be categorized into additive (coated and 3D-printed), subtractive (or machined), and compound (a combination of more than one additive or subtractive method) fabrication techniques. In particular, additive manufacturing methods provide the freedom to fabricate complex parts, thus overcoming the constraints posed by traditional fabrication techniques. These methods are also beneficial in creating micro- and nanoscale coatings. In line with this, the focus of this Special Issue is to highlight recent developments in the above-mentioned manufacturing methods and their potential applications, especially in mechanical, energy, agriculture, structural, and thermal systems. In addition to review and experimental works, this Special Issue takes into consideration cutting-edge research based on modeling, artificial intelligence, and machine learning.

Dr. Uzair Sajjad
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. 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.

Dr. Uzair Sajjad
Guest Editor

Manuscript Submission Information

Keywords

additive manufacturing
subtractive manufacturing
compound manufacturing
micro- and nanoscale coatings
engineering applications
artificial intelligence
explainable artificial intelligence
deep learning
machine learning
physics informed neural networks
Bayesian optimization
genetic algorithm
energy systems
thermal systems
agricultural systems

Published Papers (6 papers)

Download All Papers

Research

Jump to: Review

14 pages, 3937 KiB

Open AccessArticle

A Hybrid Data-Driven Metaheuristic Framework to Optimize Strain of Lattice Structures Proceeded by Additive Manufacturing

by Tao Zhang, Uzair Sajjad, Akash Sengupta, Mubasher Ali, Muhammad Sultan and Khalid Hamid

Micromachines 2023, 14(10), 1924; https://doi.org/10.3390/mi14101924 - 13 Oct 2023

Viewed by 1007

Abstract

This research is centered on optimizing the mechanical properties of additively manufactured (AM) lattice structures via strain optimization by controlling different design and process parameters such as stress, unit cell size, total height, width, and relative density. In this regard, numerous topologies, including sea urchin (open cell) structure, honeycomb, and Kelvin structures simple, round, and crossbar (2 × 2), were considered that were fabricated using different materials such as plastics (PLA, PA12), metal (316L stainless steel), and polymer (thiol-ene) via numerous AM technologies, including stereolithography (SLA), multijet fusion (MJF), fused deposition modeling (FDM), direct metal laser sintering (DMLS), and selective laser melting (SLM). The developed deep-learning-driven genetic metaheuristic algorithm was able to achieve a particular strain value for a considered topology of the lattice structure by controlling the considered input parameters. For instance, in order to achieve a strain value of 2.8 × 10⁻⁶ mm/mm for the sea urchin structure, the developed model suggests the optimal stress (11.9 MPa), unit cell size (11.4 mm), total height (42.5 mm), breadth (8.7 mm), width (17.29 mm), and relative density (6.67%). Similarly, these parameters were controlled to optimize the strain for other investigated lattice structures. This framework can be helpful in designing various AM lattice structures of desired mechanical qualities. Full article

(This article belongs to the Special Issue Recent Advances in Precision Manufacturing: Materials, Methods, and Potential Applications)

► Show Figures

Figure 1

14 pages, 6907 KiB

Open AccessArticle

Edge-Enhanced Object-Space Model Optimization of Tomographic Reconstructions for Additive Manufacturing

by Yanchao Zhang, Minzhe Liu, Hua Liu, Ce Gao, Zhongqing Jia and Ruizhan Zhai

Micromachines 2023, 14(7), 1362; https://doi.org/10.3390/mi14071362 - 30 Jun 2023

Cited by 1 | Viewed by 985

Abstract

Object-space model optimization (OSMO) has been proven to be a simple and high-accuracy approach for additive manufacturing of tomographic reconstructions compared with other approaches. In this paper, an improved OSMO algorithm is proposed in the context of OSMO. In addition to the two model optimization steps in each iteration of OSMO, another two steps are introduced: one step enhances the target regions’ in-part edges of the intermediate model, and the other step weakens the target regions’ out-of-part edges of the intermediate model to further improve the reconstruction accuracy of the target boundary. Accordingly, a new quality metric for volumetric printing, named ‘Edge Error’, is defined. Finally, reconstructions on diverse exemplary geometries show that all the quality metrics, such as VER, PW, IPDR, and Edge Error, of the new algorithm are significantly improved; thus, this improved OSMO approach achieves better performance in convergence and accuracy compared with OSMO. Full article

(This article belongs to the Special Issue Recent Advances in Precision Manufacturing: Materials, Methods, and Potential Applications)

► Show Figures

Figure 1

14 pages, 40947 KiB

Open AccessArticle

Effects of Grinding Parameters on the Processing Temperature, Crack Propagation and Residual Stress in Silicon Nitride Ceramics

by Haipeng Yan, Fei Deng, Zhiying Qin, Jinda Zhu, Hongjie Chang and Huli Niu

Micromachines 2023, 14(3), 666; https://doi.org/10.3390/mi14030666 - 16 Mar 2023

Cited by 4 | Viewed by 1824

Abstract

The surface/subsurface damage of engineering ceramics after machining has a great influence on the service performance of parts. In order to obtain a high grinding surface quality of engineering ceramics, and take silicon nitride ceramic as a research object, a series of grinding experiments were carried out. The effects of grinding parameters on longitudinal crack propagation depth and the surface residual stress of silicon nitride ceramics were analyzed by grinding experiments, and the residual stress at the location of crack propagation was obtained. The variation in the grinding temperature under different grinding parameters was explored. The influences of the grinding temperature on crack propagation depth and surface residual stress were clarified, the distribution of residual stress along the depth direction was discussed, and the relationship between the residual stress and crack propagation was revealed. The results show that the residual compressive stress on the surface of silicon nitride ceramics decreases with the increase in the depth of crack propagation and the degree of surface brittle spalling. The residual stress at the location of the crack propagation was residual tensile stress. The crack propagation depth increased with the increase in the residual tensile stress. The research provides a reference for the realization of high-quality surfaces in the grinding of silicon nitride ceramics. Full article

(This article belongs to the Special Issue Recent Advances in Precision Manufacturing: Materials, Methods, and Potential Applications)

► Show Figures

Graphical abstract

12 pages, 2017 KiB

Open AccessArticle

Research on Intelligent Identification and Grading of Nonmetallic Inclusions in Steels Based on Deep Learning

by Xiaolin Zhu, Wenhai Wan, Ling Qian, Yu Cai, Xiang Chen, Pingze Zhang, Guanxi Huang, Bo Liu, Qiang Yao, Shaoyuan Li and Zhengjun Yao

Micromachines 2023, 14(2), 482; https://doi.org/10.3390/mi14020482 - 19 Feb 2023

Cited by 2 | Viewed by 1491

Abstract

Non-metallic inclusions are unavoidable defects in steel, and their type, quantity, size, and distribution have a great impact on the quality of steel. At present, non-metallic inclusions are mainly detected manually, which features high work intensity, low efficiency, proneness to misjudgment, and low consistency of results. In this paper, based on deep neural network algorithm, a small number of manually labeled, low-resolution metallographic images collected by optical microscopes are used as the dataset for intelligent boundary extraction, classification, and rating of non-metallic inclusions. The training datasets are cropped into those containing only a single non-metallic inclusion to reduce the interference of background information and improve the accuracy. To deal with the unbalanced distribution of each category of inclusions, the reweighting cross entropy loss and focal loss are respectively used as the category prediction loss and boundary prediction loss of the DeepLabv3+ semantic segmentation model. Finally, the length and width of the minimum enclosing rectangle of the segmented inclusions are measured to calculate the grade of inclusions. The resulting accuracy is 90.34% in segmentation and 90.35% in classification. As is verified, the model-based rating results are consistent with those of manual labeling. For a single sample, the detection time is reduced from 30 min to 15 s, significantly improving the detection efficiency. Full article

(This article belongs to the Special Issue Recent Advances in Precision Manufacturing: Materials, Methods, and Potential Applications)

► Show Figures

Figure 1

23 pages, 22786 KiB

Open AccessArticle

Evaluating the Stress-Strain Relationship of the Additively Manufactured Lattice Structures

by Long Zhang, Farzana Bibi, Imtiyaz Hussain, Muhammad Sultan, Adeel Arshad, Saqib Hasnain, Ibrahim M. Alarifi, Mohammed A. Alamir and Uzair Sajjad

Micromachines 2023, 14(1), 75; https://doi.org/10.3390/mi14010075 - 27 Dec 2022

Cited by 6 | Viewed by 2645

Abstract

Extensive amount of research on additively manufactured (AM) lattice structures has been made to develop a generalized model that can interpret how strongly operational variables affect mechanical properties. However, the currently used techniques such as physics models and multi-physics simulations provide a specific interpretation of those qualities, and are not general enough to assess the mechanical properties of AM lattice structures of different topologies produced on different materials via several fabrication methods. To tackle this problem, this study develops an optimal deep learning (DL) model based on more than 4000 data points, which has been optimized by analyzing three different hyper-parameters optimization schemes including gradient boost regression trees (GBRT), gaussian process (GP), and random forest (RF) with different data distribution schemes such as normal distribution, nth root transformation, and robust scaler. With the robust scaler and nth root transformation, the accuracy of the model increases from R² = 0.85 (for simple distribution) to R² = 0.94 and R² = 0.88, respectively. After feature engineering and data correlation, the stress, unit cell size, total height, width, and relative density are chosen to be the input parameters to model the strain. The optimal DL model is able to predict the strain of different topologies of lattices (such as circular, octagonal, Gyroid, truncated cube, Truncated cuboctahedron, Rhombic do-decahedron, and many others) with decent accuracy (R² = 0.936, MAE = 0.05, and MSE = 0.025). The parametric sensitivity analysis and explainable artificial intelligence (by using DeepSHAP library) based insights confirm that stress is the most sensitive input to the strain followed by the relative density from the modeling perspective of the AM lattices. The findings of this study would be helpful for the industry and the researchers to design AM lattice structures of different topologies for various engineering applications. Full article

(This article belongs to the Special Issue Recent Advances in Precision Manufacturing: Materials, Methods, and Potential Applications)

► Show Figures

Figure 1

Review

Jump to: Research

26 pages, 12885 KiB

Open AccessReview

Research Status of Manufacturing Technology of Tungsten Alloy Wire

by Jun Cao, Yongzhen Sun, Baoan Wu, Huiyi Tang, Yong Ding, Kexing Song and Chengqiang Cui

Micromachines 2023, 14(5), 1030; https://doi.org/10.3390/mi14051030 - 11 May 2023

Cited by 2 | Viewed by 1939

Abstract

In light of the fact that tungsten wire is gradually replacing high-carbon steel wire as a diamond cutting line, it is particularly important to study tungsten alloy wire with better strength and performance. According to this paper, in addition to various technological factors (powder preparation, press forming, sintering, rolling, rotary forging, annealing, wire drawing, etc.), the main factors affecting the properties of the tungsten alloy wire are the composition of the tungsten alloy, the shape and size of the powder, etc. Combined with the research results in recent years, this paper summarizes the effects of changing the composition of tungsten materials and improving the processing technology on the microstructure and mechanical properties of tungsten and its alloys and points out the development direction and trend of tungsten and its alloy wires in the future. Full article

(This article belongs to the Special Issue Recent Advances in Precision Manufacturing: Materials, Methods, and Potential Applications)

► Show Figures