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Smart Manufacturing in the Era of Industry 4.0
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Smart Manufacturing in the Era of Industry 4.0

A special issue of Journal of Manufacturing and Materials Processing (ISSN 2504-4494).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 6948

Special Issue Editor

Dr. Enkhsaikhan Boldsaikhan

E-Mail Website
Guest Editor
Industrial, Systems & Manufacturing Engineering Department, Wichita State University, Wichita, KS 67260, USA
Interests: smart manufacturing; industrial robotics; automation; sensor fusion; manufacturing processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is my immense pleasure to invite you to submit your research findings to this Special Issue, entitled “Smart Manufacturing in the Era of Industry 4.0,” of the Journal of Manufacturing and Materials Processing, published by MDPI. In the era of Industry 4.0, a wave of new scientific and technological breakthroughs, such as artificial intelligence, cyber-physical systems, robotics, automation, digital transformation, digital twinning, additive manufacturing, the internet of things (IoT), and sensor fusion, has pushed the boundaries of manufacturing realms and enabled the inception of smart manufacturing.

The aim of this Special Issue is to compile recent advancements and innovations in the research domains that enable smart manufacturing and the processing of materials. High-quality contributions that demonstrate substantial advancements and applications, with emphases on smart manufacturing and materials processing, will be considered for publication in this Special Issue. The desired topics of contributions include, but are not limited to, the following:

  • Artificial intelligence in manufacturing and materials processing;
  • Cyber-physical systems;
  • Industrial robotics and automation;
  • Digital transformation;
  • Digital twinning;
  • Additive manufacturing;
  • The internet of things (IoT);
  • Sensor fusion.

Dr. Enkhsaikhan Boldsaikhan
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. Journal of Manufacturing and Materials Processing 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 1800 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

  • smart manufacturing
  • robotics
  • automation
  • digital twin
  • additive manufacturing
  • Industry 4.0

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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23 pages, 1064 KiB  
Article
A Universal Framework for Skill-Based Cyber-Physical Production Systems
by Max Hossfeld and Andreas Wortmann
J. Manuf. Mater. Process. 2024, 8(5), 221; https://doi.org/10.3390/jmmp8050221 - 2 Oct 2024
Viewed by 638
Abstract
In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and [...] Read more.
In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework’s benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework’s application, demonstrating its current capabilities and potential for modern manufacturing. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)
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25 pages, 5085 KiB  
Article
Development and Application of Digital Twin Control in Flexible Manufacturing Systems
by Asif Ullah and Muhammad Younas
J. Manuf. Mater. Process. 2024, 8(5), 214; https://doi.org/10.3390/jmmp8050214 - 28 Sep 2024
Viewed by 911
Abstract
Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, [...] Read more.
Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)
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19 pages, 4551 KiB  
Article
Development of a Method and a Smart System for Tool Critical Life Real-Time Monitoring
by Shih-Ming Wang, Wan-Shing Tsou, Jian-Wei Huang, Shao-En Chen and Chia-Che Wu
J. Manuf. Mater. Process. 2024, 8(5), 194; https://doi.org/10.3390/jmmp8050194 - 5 Sep 2024
Viewed by 716
Abstract
Tool wear management and real-time machining quality monitoring are pivotal components of realizing smart manufacturing objectives, as they directly influence machining precision and productivity. Traditionally, measuring and analyzing cutting force fluctuations in the time domain has been employed to diagnose tool wear effects. [...] Read more.
Tool wear management and real-time machining quality monitoring are pivotal components of realizing smart manufacturing objectives, as they directly influence machining precision and productivity. Traditionally, measuring and analyzing cutting force fluctuations in the time domain has been employed to diagnose tool wear effects. This study introduces a novel, indirect approach that leverages spindle-load current variations as a proxy for cutting force analysis. Compared to conventional methods relying on machining vibration or direct cutting force measurement, this technique provides a safer, simpler, and more cost-effective means of data aquisition, with reduced computational demands. Consequently, it is ideally suited for real-time monitoring and long-term analyses such as tool-life prediction and surface-roughness evolution induced by tool wear. An intelligent tool wear monitoring system was developed based on spindle-load current data. The system employs extensive cutting experiments to identify and analyze the correlation between tool wear and spindle-load current signal patterns. By establishing a tool wear near-end-of-life threshold, the system enables intelligent monitoring using C#. Experimental validation under both roughing and finishing conditions demonstrated the system’s exceptional diagnostic accuracy and reliability. The results demonstrate that the current ratio threshold value has good universality in different materials, indicating that monitoring the machining current ratio to estimate the degree of tool wear is a feasible research direction, and that the average error between the experimental surface-roughness measurement value and the predicted value was 10%. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)
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Review

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23 pages, 2096 KiB  
Review
Soft Robot Design, Manufacturing, and Operation Challenges: A Review
by Getachew Ambaye, Enkhsaikhan Boldsaikhan and Krishna Krishnan
J. Manuf. Mater. Process. 2024, 8(2), 79; https://doi.org/10.3390/jmmp8020079 - 16 Apr 2024
Cited by 2 | Viewed by 3678
Abstract
Advancements in smart manufacturing have embraced the adoption of soft robots for improved productivity, flexibility, and automation as well as safety in smart factories. Hence, soft robotics is seeing a significant surge in popularity by garnering considerable attention from researchers and practitioners. Bionic [...] Read more.
Advancements in smart manufacturing have embraced the adoption of soft robots for improved productivity, flexibility, and automation as well as safety in smart factories. Hence, soft robotics is seeing a significant surge in popularity by garnering considerable attention from researchers and practitioners. Bionic soft robots, which are composed of compliant materials like silicones, offer compelling solutions to manipulating delicate objects, operating in unstructured environments, and facilitating safe human–robot interactions. However, despite their numerous advantages, there are some fundamental challenges to overcome, which particularly concern motion precision and stiffness compliance in performing physical tasks that involve external forces. In this regard, enhancing the operation performance of soft robots necessitates intricate, complex structural designs, compliant multifunctional materials, and proper manufacturing methods. The objective of this literature review is to chronicle a comprehensive overview of soft robot design, manufacturing, and operation challenges in conjunction with recent advancements and future research directions for addressing these technical challenges. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0)
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