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Micromachines
Special Issues
Intelligent Additive/Subtractive Manufacturing
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Intelligent Additive/Subtractive Manufacturing

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 14907

Special Issue Editors

Dr. Muhommad Azizur Rahman

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Guest Editor
Mechanical & Production Engineering, Ahsanullah University of Science & Technology (AUST), Dhaka 1208, Bangladesh
Interests: advanced manufacturing; metal cutting; micro-nano fabrication; ultra-precision machining; non-traditional machining; additive manufacturing; Industry 4.0; manufacturing process improvement
Dr. Tanveer Saleh

E-Mail Website
Guest Editor
Department of Mechatronics Engineering, International Islamic University Malaysia, Jalan Gombak, 53100 Kuala Lumpur, Malaysia
Interests: automation; smart material; MEMS; carbon nanotube forest; rehabilitation robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Pervej Jahan

E-Mail Website
Guest Editor
Department of Mechanical and Manufacturing Engineering, Miami University, Oxford, OH 45056, USA
Interests: micromachining; micro-electro-discharge machining (micro-EDM); hybrid micromachining; nanomachining; non-conventional machining; additive manufacturing (AM); post-processing of AM parts; manufacturing processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As industrialization advances global connections, manufacturing processes are required to be more efficient than before. Thus, factory operations and fabrication tasks are becoming increasingly complex, dynamic, automated, connected, and remotely operated. This has created ample challenges and opportunities for Industry 4.0 (I4.0) readiness. Additive manufacturing, an enabler of Industry 4.0, recently opened limitless opportunities in various sectors covering personal, industrial, medical, aviation, and even extra-terrestrial applications. However, limitations of industrial-scale additively manufactured (AMed) parts arise due to the issues of machines, processes, and materials. Moreover, inadequate cyber-physical systems (CPS), lack of collaboration, and intelligence in the entire additive manufacturing platform hinder machine-to-machine (M2M) data distributions and machine-to-human (M2H) interactions for rapid prototyping and industrial-scale manufacturing requirement. This is also true for conventional subtractive manufacturing processes, which can augment AMed part quality by finishing and post-processing. More importantly, the intelligent and hybrid additive/subtractive processes can remove barriers to AMed parts becoming fully functional products used for a wide range of Macro-to-Micro-to-Nano scale applications. Thus, this Special Issue will publish research and review articles covering smart manufacturing systems emphasizing additive, subtractive, and/or hybrid processes in a collaborative, predictive, decisive, and intelligent environment.

Dr. Muhommad Azizur Rahman
Dr. Tanveer Saleh
Dr. Muhammad Pervej Jahan
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

  • Industry 4.0
  • Intelligence in manufacturing
  • Additive manufacturing (3D Printing)
  • Subtractive manufacturing (machining)
  • Macro–micro–nano applications
  • Structural integrity and surface quality
  • Cyber-physical system (CPS) and collaboration
  • Machine-to-machine (M2M) communication
  • Advanced and bio-medical applications
  • Precision and aerospace applications

Published Papers (5 papers)

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Research

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21 pages, 11581 KiB  
Article
Computational Analysis of Machining Induced Stress Distribution during Dry and Cryogenic Orthogonal Cutting of 7075 Aluminium Closed Cell Syntactic Foams
by Kevin K. Thomas, Sathish Kannan, Salman Pervaiz, Mohammad Nazzal and Ramanujam Karthikeyan
Micromachines 2023, 14(1), 174; https://0-doi-org.brum.beds.ac.uk/10.3390/mi14010174 - 10 Jan 2023
Viewed by 1127
Abstract
The addition of hollow aluminium oxide bubbles to the 7075 aluminium matrix results in a lightweight syntactic foam with a reduced density and an increased peak compression strength. The presence of ceramic bubbles also aids in a reduced coefficient of thermal expansion and [...] Read more.
The addition of hollow aluminium oxide bubbles to the 7075 aluminium matrix results in a lightweight syntactic foam with a reduced density and an increased peak compression strength. The presence of ceramic bubbles also aids in a reduced coefficient of thermal expansion and thermal conductivity in comparison to aluminium alloys. In spite of their enhanced material properties, the inclusion of hollow aluminium oxide bubbles presents the challenge of poor machinability. In order to elucidate the problem of poor surface machinability, an attempt has been made to develop a thermo-mechanical finite element machining model using AdvantEdgeTM software with which surface quality and machined syntactic foam material can be analyzed. If the novel model developed is combined with virtual reality technology, CNC technicians can observe the machining results to evaluate and optimize the machining program. The main novelty behind this software is that the material foam is assumed as a homogeneous material model for simplifying the material model as a complex heterogeneous material system. The input parameters used in this study are cutting speed, feed, average size and volume fraction of hollow aluminium oxide bubbles, and coolant. For the output parameters, the numerical analysis showed a 6.24% increase in peak tensile machining induced stress as well as a 51.49% increase in peak cutting temperature as cutting speed (25 m/min to 100 m/min) and uncut chip thickness (0.07 mm to 0.2 mm) were increased. The average size and volume fraction of hollow aluminium oxide bubbles showed a significant impact on the magnitude of cutting forces and the depth of tensile induced stress distribution. It was observed on the machined surface that, as the average size of hollow aluminium oxide bubbles became coarser, the peak machining induced tensile stress on the cut surface reduced by 4.47%. It was also noted that an increase in the volume fraction of hollow aluminium oxide bubbles led to an increase in both the peak machining induced tensile stress and the peak cutting temperature by 29.36% and 20.11%, respectively. This study also showed the influence of the ceramic hollow bubbles on plastic deformation behavior in 7075 aluminium matrix; the machining conditions for obtaining a favorable stress distribution in the machined surface and sub-surface of 7075 closed cell syntactic foam are also presented. Full article
(This article belongs to the Special Issue Intelligent Additive/Subtractive Manufacturing)
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16 pages, 7459 KiB  
Article
Quality Prediction and Control in Wire Arc Additive Manufacturing via Novel Machine Learning Framework
by Xinyi Xiao, Clarke Waddell, Carter Hamilton and Hanbin Xiao
Micromachines 2022, 13(1), 137; https://0-doi-org.brum.beds.ac.uk/10.3390/mi13010137 - 15 Jan 2022
Cited by 43 | Viewed by 3676
Abstract
Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and [...] Read more.
Wire arc additive manufacturing (WAAM) is capable of rapidly depositing metal materials thus facilitating the fabrication of large-shape metal components. However, due to the multi-process-variability in the WAAM process, the deposited shape (bead width, height, depth of penetration) is difficult to predict and control within the desired level. Ultimately, the overall build will not achieve a near-net shape and will further hinder the part from performing its functionality without post-processing. Previous research primarily utilizes data analytical models (e.g., regression model, artificial neural network (ANN)) to forwardly predict the deposition width and height variation based on single or cross-linked process variables. However, these methods cannot effectively determine the optimal printable zone based on the desired deposition shape due to the inability to inversely deduce from these data analytical models. Additionally, the process variables are intercorrelated, and the bead width, height, and depth of penetration are highly codependent. Therefore, existing analysis cannot grant a reliable prediction model that allows the deposition (bead width, height, and penetration height) to remain within the desired level. This paper presents a novel machine learning framework for quantitatively analyzing the correlated relationship between the process parameters and deposition shape, thus providing an optimal process parameter selection to control the final deposition geometry. The proposed machine learning framework can systematically and quantitatively predict the deposition shape rather than just qualitatively as with other existing machine learning methods. The prediction model can also present the complex process-quality relations, and the determination of the deposition quality can guide the WAAM to be more prognostic and reliable. The correctness and effectiveness of the proposed quantitative process-quality analysis will be validated through experiments. Full article
(This article belongs to the Special Issue Intelligent Additive/Subtractive Manufacturing)
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21 pages, 36330 KiB  
Article
Intelligent Design Optimization System for Additively Manufactured Flow Channels Based on Fluid–Structure Interaction
by Haonan Ji, Bin Zou, Yongsheng Ma, Carlos F. Lange, Jikai Liu and Lei Li
Micromachines 2022, 13(1), 100; https://0-doi-org.brum.beds.ac.uk/10.3390/mi13010100 - 08 Jan 2022
Cited by 2 | Viewed by 1967
Abstract
Based on expert system theory and fluid–structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design [...] Read more.
Based on expert system theory and fluid–structure interaction (FSI), this paper suggests an intelligent design optimization system to derive the optimal shape of both the fluid and solid domain of flow channels. A parametric modeling scheme of flow channels is developed by design for additive manufacturing (DfAM). By changing design parameters, a series of flow channel models can be obtained. According to the design characteristics, the system can intelligently allocate suitable computational models to compute the flow field of a specific model. The pressure-based normal stress is abstracted from the results and transmitted to the solid region by the fluid–structure (FS) interface to analyze the strength of the structure. The design space is obtained by investigating the simulation results with the metamodeling method, which is further applied for pursuing design objectives under constraints. Finally, the improved design is derived by gradient-based optimization. This system can improve the accuracy of the FSI simulation and the efficiency of the optimization process. The design optimization of a flow channel in a simplified hydraulic manifold is applied as the case study to validate the feasibility of the proposed system. Full article
(This article belongs to the Special Issue Intelligent Additive/Subtractive Manufacturing)
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20 pages, 6121 KiB  
Article
Predicting Cutting Force and Primary Shear Behavior in Micro-Textured Tools Assisted Machining of AISI 630: Numerical Modeling and Taguchi Analysis
by Shafahat Ali, Said Abdallah and Salman Pervaiz
Micromachines 2022, 13(1), 91; https://0-doi-org.brum.beds.ac.uk/10.3390/mi13010091 - 07 Jan 2022
Cited by 5 | Viewed by 1362
Abstract
The cutting tool heats up during the cutting of high-performance super alloys and it negatively affects the life of the cutting tool. Improved tool life can enhance both the machinability and sustainability of the cutting process. To improve the tool life preferably cutting [...] Read more.
The cutting tool heats up during the cutting of high-performance super alloys and it negatively affects the life of the cutting tool. Improved tool life can enhance both the machinability and sustainability of the cutting process. To improve the tool life preferably cutting fluids are utilized. However, the majority of cutting fluids are non-biodegradable in nature and pose harmful threats to the environment. It has been established in the metal cutting literature that introducing microgrooves at the cutting tool rake face can significantly reduce the coefficient of friction (COF). Reduction in the COF promotes anti-adhesive behavior that improves the tool life. The current study numerically investigates the orthogonal cutting process of AISI 630 Stainless Steel using different micro grooved cutting tools. Results of the numerical simulations point to the positive influence of micro grooves on tool life. The results of the main effects found that the cutting temperature was decreased by approximately 10% and 7% with rectangular and triangular micro grooved tools, respectively. Over machining performance indicated that rectangular micro groove tools provided comparatively better performance. Full article
(This article belongs to the Special Issue Intelligent Additive/Subtractive Manufacturing)
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Review

Jump to: Research

53 pages, 12839 KiB  
Review
Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects
by M. Azizur Rahman, Tanveer Saleh, Muhammad Pervej Jahan, Conor McGarry, Akshay Chaudhari, Rui Huang, M. Tauhiduzzaman, Afzaal Ahmed, Abdullah Al Mahmud, Md. Shahnewaz Bhuiyan, Md Faysal Khan, Md. Shafiul Alam and Md Shihab Shakur
Micromachines 2023, 14(3), 508; https://0-doi-org.brum.beds.ac.uk/10.3390/mi14030508 - 22 Feb 2023
Cited by 16 | Viewed by 5155
Abstract
Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of [...] Read more.
Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes. Full article
(This article belongs to the Special Issue Intelligent Additive/Subtractive Manufacturing)
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