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Shaping the Future of Industry: Innovations in Digital and Smart...
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Shaping the Future of Industry: Innovations in Digital and Smart Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3297

Special Issue Editor

Dr. Shohin Aheleroff

E-Mail Website
Guest Editor
SUEZ Smart Solutions Limited, Auckland 1010, New Zealand
Interests: Industry 4.0; digital twin; IoT and, more specifically, mass personalization production
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industrial Revolution, a cornerstone of human progress, is undergoing a profound transformation, driven by the convergence of cutting-edge technologies. This evolution, known as Digital Manufacturing, is characterized by a significant enhancement in production efficiency, sustainability, and resilience.

This Special Issue delves into the forefront of this revolution, showcasing pioneering research and groundbreaking innovations that are reshaping the industrial landscape. By exploring advanced methodologies, cutting-edge technologies, and transformative applications, this issue aims to provide critical insights into how these innovations are shaping the future of manufacturing and impacting society.

Key areas of focus include:

  • Advanced Manufacturing Technologies: This encompasses a wide range of technologies, such as additive manufacturing (3D printing), robotics, automation, and artificial intelligence (AI). These technologies revolutionize traditional manufacturing processes by enabling greater flexibility, customization, and efficiency.
  • Digital Transformation: This refers to the integration of digital technologies into manufacturing processes, including data analytics, cloud computing, and the Internet of Things (IoT). These technologies are enabling real-time monitoring, predictive maintenance, and improved supply chain management.
  • Industry 4.0: This refers to the fourth industrial revolution, characterized by the convergence of physical and digital technologies. Digital Manufacturing plays a crucial role in enabling Industry 4.0 by creating interconnected and intelligent manufacturing systems.

We invite researchers to submit their latest findings and perspectives to contribute to this dynamic and rapidly evolving field. By sharing their knowledge and expertise, they can help shape the future of manufacturing and drive innovation in this exciting era.

Dr. Shohin Aheleroff
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. Machines 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 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

  • Industry 4.0
  • Industry 5.0
  • smart manufacturing
  • intelligent manufacturing
  • digital manufacturing
  • advanced manufacturing
  • sustainable manufacturing
  • resilient production systems
  • next-generation automation
  • future-driven technologies
  • transformative industrial applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

16 pages, 4778 KiB  
Article
Automating Quality Control on a Shoestring, a Case Study
by Hang Sun, Wei-Ting Teo, Kenji Wong, Botao Dong, Jan Polzer and Xun Xu
Machines 2024, 12(12), 904; https://doi.org/10.3390/machines12120904 - 10 Dec 2024
Cited by 1 | Viewed by 1200
Abstract
Dependence on manual inspections for quality control often results in errors, especially after prolonged periods of work that heighten the risk of missed defects. There is no shortage of expensive commercial inspection systems that can carry out the quality control work satisfactorily. However, [...] Read more.
Dependence on manual inspections for quality control often results in errors, especially after prolonged periods of work that heighten the risk of missed defects. There is no shortage of expensive commercial inspection systems that can carry out the quality control work satisfactorily. However, small to medium-sized enterprises (SMEs) often face challenges in adopting these new systems for their production workflows because of the associated integration risks, high cost, and skill complexity. To address these issues, a portable, cost-effective, and automated quality inspection system was developed as an introductory tool for SMEs. Leveraging computer vision, 3D-printed mechanical parts, and accessible components, this system offers a 360-degree inspection of production line products, enabling SMEs to explore automation with minimal investment. It features a brief training phase using a few defect-free parts to reduce the skill barrier, thus helping SMEs to transition towards smart manufacturing. These help to address the main technology adoption barriers of cost, risk, and complexity. The system’s performance was validated through repeated testing on a large sheet metal chassis installed in uninterruptible power supplies (UPS), confirming its effectiveness as a steppingstone toward more advanced smart manufacturing solutions. Full article
(This article belongs to the Special Issue Shaping the Future of Industry: Innovations in Digital and Smart Manufacturing)
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25 pages, 4369 KiB  
Article
Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm
by Ali Akbar ForouzeshNejad, Farzad Arabikhan and Shohin Aheleroff
Machines 2024, 12(12), 867; https://doi.org/10.3390/machines12120867 - 29 Nov 2024
Cited by 2 | Viewed by 1812
Abstract
Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting [...] Read more.
Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries. Full article
(This article belongs to the Special Issue Shaping the Future of Industry: Innovations in Digital and Smart Manufacturing)
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