Industry 4.0: Design and Improvement of Additive Manufacturing

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Additive Manufacturing".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1262

Special Issue Editor

School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford CM1 1SQ, UK
Interests: 3D/4D printing; additive/hybrid manufacturing; composite & hybrid materials; graphene processing; industry 4.0; metal & polymer prototyping; product design & development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Additive manufacturing (AM) is an umbrella term that encompasses seven categories, i.e., VAT photopolymerization, material jetting, binder jetting, material extrusion, powder bed fusion, sheet lamination and directed energy deposition. AM has been around for decades, and has demonstrated its significance through design freedom, time and cost-effectiveness, as well as integration with artificial intelligence which has driven generative design to develop bespoke products. These benefits have evolved AM into a notable pillar of Industry 4.0; the other pillars include augmented reality, simulation, autonomous robots, Industrial Internet of Things, big data analytics, cloud computing, cyber security as well as horizontal and vertical integration. The fourth industrial revolution is characterized by interconnectivity, automation, machine learning and real-time data analytics, leading to the cyber-physical transformation of manufacturing. These are exciting times for AM, as with the rapid growth in consumer demands and need for the customization of products, it can meet these stringent requirements with the help of other Industry 4.0 pillars able to support the design, analysis and improvement of AM methods and products. There exists an interconnectedness between the Industry 4.0 pillars and AM, where they are either used directly (for AM) or indirectly (with AM) to manufacture products and improve processes. This Special Issue focuses on such interactions, resulting in the development of digital twins, cyber-physical systems and operation management approaches through the incorporation of Industry 4.0 in driving the optimisation of AM methods and products.

Innovative research highlighting the development of a continuous digital thread for AM, both for product design and process management, starting from the raw material stage to customer feedback is crucial to leverage the benefits of AM for increased efficiency and productivity. Therefore, we welcome articles demonstrating the impact of Industry 4.0 on AM via principles such as interoperability, virtualisation, decentralisation, real-time capability, service orientation and modularity.

Dr. Javaid Butt
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at 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. Metals 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.


  • additive manufacturing
  • industry 4.0
  • digital twin
  • cyber-physical system
  • digital manufacturing
  • artificial intelligence
  • machine learning

Published Papers (1 paper)

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22 pages, 3406 KiB  
A Grey-Box Model of Laser Powder Directed Energy Deposition for Complex Scanning Strategy
Metals 2023, 13(10), 1763; - 17 Oct 2023
Viewed by 766
Directed Energy Deposition using a laser based system (DED-LB) is a technology that enables the repair of components, cutting costs and saving resources when it comes to valuable and expensive components. Furthermore, this method can be used in the production of multi-material components. [...] Read more.
Directed Energy Deposition using a laser based system (DED-LB) is a technology that enables the repair of components, cutting costs and saving resources when it comes to valuable and expensive components. Furthermore, this method can be used in the production of multi-material components. Despite its benefits, DED-LB process has limitations as well, particularly in terms of resolution, surface quality, and dimensional accuracy. Optimisation of scanning parameters and strategies, as well as the use of new materials, appears to be advantageous in this regard. Simultaneously, the use of methods such as numerical simulation expedites the process of becoming familiar with the technology, thereby improving optimization tasks. DED-LB process starts with one track; the research and optimisation of its properties are crucial, as they affect the outcome of the DED-LB component. In this research article, a novel grey-box model that exhibits the ability to precisely predict the temperature distribution and track dimensions was introduced. The proposed model adopts a numerical–analytical methodology, yielding outcomes at a comparatively reduced computational expense while upholding precision in the obtained results. The proposed modelling approach is based on the solution of the heat equation coupled with an iterative feedback loop to quantify the power losses and ensure energy and mass balance at the melt pool. The model is used to forecast the temperature field and track characteristics for a collection of linear tracks while varying the main process parameters in order to study their effect on track characteristics. In addition, this model can be used to predict the course of more complex trajectories; to illustrate this, an application in which both circular and square tracks are made was presented. Full article
(This article belongs to the Special Issue Industry 4.0: Design and Improvement of Additive Manufacturing)
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