Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
3.3
Journals
JMMP
Special Issues
Smart Manufacturing in the Era of Industry 4.0, 2nd Edition
share announcement

Smart Manufacturing in the Era of Industry 4.0, 2nd Edition

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 18448

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 pleasure to invite you to submit your research findings to this Special Issue, entitled “Smart Manufacturing in the Era of Industry 4.0, 2nd Edition”, 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 250 words) can be sent to the Editorial Office for assessment.

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 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 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

17 pages, 3062 KB  
Article
Enhancing Geometric Deviation Prediction in Laser Powder Bed Fusion with Varied Process Parameters Using Conditional Generative Adversarial Networks
by Subigyamani Bhandari, Himal Sapkota and Sangjin Jung
J. Manuf. Mater. Process. 2025, 9(12), 411; https://doi.org/10.3390/jmmp9120411 - 15 Dec 2025
Viewed by 511
Abstract
The progress in metal additive manufacturing (AM) technology has enabled the printing of parts with intricate geometries. Predicting and reducing geometrical deviations (i.e., the difference between the printed part and the design) in metal AM parts remains a challenge. This work explores how [...] Read more.
The progress in metal additive manufacturing (AM) technology has enabled the printing of parts with intricate geometries. Predicting and reducing geometrical deviations (i.e., the difference between the printed part and the design) in metal AM parts remains a challenge. This work explores how changes in laser speed, laser power, and hatch spacing affect geometrical deviations in parts made using laser powder bed fusion (L-PBF) and emphasizes predicting geometrical defects in AM parts. Sliced images obtained from CAD designs and printed parts are utilized to capture the effects of various L-PBF process parameters and to generate a comprehensive data set. Conditional Generative Adversarial Networks (cGANs) are trained to predict images that accurately reflect actual geometrical deviations. In this study, the influence of L-PBF process parameters on geometric deviation is quantified, and the prediction results demonstrate the effectiveness of the proposed cGAN-based method in improving the predictability of geometric deviations in parts fabricated via L-PBF. This approach is expected to facilitate early correction of geometrical deviations during the L-PBF process. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0, 2nd Edition)
►▼ Show Figures

Figure 1

26 pages, 4117 KB  
Article
Defect Detection via Through-Transmission Ultrasound Using Neural Networks and Domain-Specific Feature Extraction
by Gary LeMay and Enkhsaikhan Boldsaikhan
J. Manuf. Mater. Process. 2025, 9(8), 271; https://doi.org/10.3390/jmmp9080271 - 11 Aug 2025
Viewed by 1685
Abstract
Defect detection in acoustically matched media remains a significant challenge, particularly when defects, such as fiberglass and polyamide residues, exhibit properties that match those of fiber-reinforced composite laminates as the base material. Techniques, such as through-transmission ultrasound (TTU), often miss subtle residues as [...] Read more.
Defect detection in acoustically matched media remains a significant challenge, particularly when defects, such as fiberglass and polyamide residues, exhibit properties that match those of fiber-reinforced composite laminates as the base material. Techniques, such as through-transmission ultrasound (TTU), often miss subtle residues as defects with the use of conventional amplitude-based TTU detection alone. There is a noticeable research gap in properly identifying such subtle residues in composites using TTU inspection. This study investigated the use of neural networks (NNs) to identify subtle defects in composites based on domain-specific feature extraction from TTU signals. Each signal waveform of each spatial TTU inspection is used as a discrete sample to obtain a larger dataset for each specimen. Domain-specific features were extracted separately from the time, frequency, and wavelet domains, resulting in independent feature vectors to emphasize the signal characteristics. The NN classification used 70% of the overall dataset for training and 30% for testing. Results reveal the features of the time- and frequency domains perform well, achieving macro-F1 scores of 0.96 and 0.97, respectively, while wavelet domain features perform lower with a macro-F1 score of 0.62. Wavelet-domain features perhaps need machine learning methods like recurrent NNs to correctly recognize subtle time-dependent signal variations. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0, 2nd Edition)
►▼ Show Figures

Figure 1

21 pages, 1338 KB  
Article
Flexible Job Shop Scheduling with Job Precedence Constraints: A Deep Reinforcement Learning Approach
by Yishi Li and Chunlong Yu
J. Manuf. Mater. Process. 2025, 9(7), 216; https://doi.org/10.3390/jmmp9070216 - 26 Jun 2025
Viewed by 4585
Abstract
The flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often struggle with scalability and efficiency when solving large instances. We propose a deep [...] Read more.
The flexible job shop scheduling problem with job precedence constraints (FJSP-JPC) is highly relevant in industrial production scenarios involving assembly operations. Traditional methods, such as mathematical programming and meta-heuristics, often struggle with scalability and efficiency when solving large instances. We propose a deep reinforcement learning (DRL) approach to minimize makespan in FJSP-JPC. The proposed method employs a heterogeneous disjunctive graph to represent the system state and a multi-head graph attention network for feature extraction. An actor–critic framework, trained using proximal policy optimization (PPO), is adopted to make operation sequencing and machine assignment decisions. The effectiveness of the proposed method is validated through comparisons with several classic dispatching rules and a state-of-the-art DRL approach. Additionally, the contributions of key mechanisms, such as information diffusion, node features, and action space, are analyzed through a full factorial design of experiments. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0, 2nd Edition)
►▼ Show Figures

Figure 1

Review

Jump to: Research

27 pages, 2431 KB  
Review
A Review of Production Scheduling with Artificial Intelligence and Digital Twins
by Punit Singh, Krishna Krishnan and Enkhsaikhan Boldsaikhan
J. Manuf. Mater. Process. 2026, 10(1), 6; https://doi.org/10.3390/jmmp10010006 - 25 Dec 2025
Viewed by 953
Abstract
Digital twin and artificial intelligence (DT-AI) technologies present hitherto unheard-of possibilities for dynamic production scheduling in smart manufacturing. Nevertheless, a careful examination of several studies reveals significant gaps in the current state of the discipline. This paper attempts to review advancements, gaps, and [...] Read more.
Digital twin and artificial intelligence (DT-AI) technologies present hitherto unheard-of possibilities for dynamic production scheduling in smart manufacturing. Nevertheless, a careful examination of several studies reveals significant gaps in the current state of the discipline. This paper attempts to review advancements, gaps, and opportunities in the areas of DT-AI-based production scheduling. Articles chosen for this literature analysis were mostly published within the last eight years. Based on the literature, five enabling challenges that are consistently considered in the literature include Dynamic and Unforeseen Disruptions, High System Complexity, Real-Time Data Management, Integration and Interoperability, and Adaptability and Generalizability. This review not only identifies these enabling challenges but also provides tailored outlines of progress and future directions. The findings pave the way for resilient, scalable, and interpretable DT-AI systems for production scheduling that can handle uncertainty and optimize output in real time. DTs and AI can benefit manufacturing with data-driven intelligent planning and decision-making as well as model-based systems engineering principles. This review examines these advancements and trending research directions in production scheduling. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0, 2nd Edition)
►▼ Show Figures

Figure 1

38 pages, 4536 KB  
Review
Emerging Technologies in Augmented Reality (AR) and Virtual Reality (VR) for Manufacturing Applications: A Comprehensive Review
by Nitol Saha, Victor Gadow and Ramy Harik
J. Manuf. Mater. Process. 2025, 9(9), 297; https://doi.org/10.3390/jmmp9090297 - 1 Sep 2025
Cited by 1 | Viewed by 9893
Abstract
As manufacturing processes evolve towards greater automation and efficiency, the integration of augmented reality (AR) and virtual reality (VR) technologies has emerged as a transformative approach that offers innovative solutions to various challenges in manufacturing applications. This comprehensive review explores the recent technological [...] Read more.
As manufacturing processes evolve towards greater automation and efficiency, the integration of augmented reality (AR) and virtual reality (VR) technologies has emerged as a transformative approach that offers innovative solutions to various challenges in manufacturing applications. This comprehensive review explores the recent technological advancements and applications of AR and VR within the context of manufacturing. This review also encompasses the utilization of AR and VR technologies across different stages of the manufacturing process, including design, prototyping, assembly, training, maintenance, and quality control. Furthermore, this review highlights the recent developments in hardware and software components that have facilitated the adoption of AR and VR in manufacturing environments. This comprehensive literature review identifies the emerging technologies that are driving AR and VR technology toward technological maturity for implementation in manufacturing applications. Finally, this review discusses the major difficulties in implementing AR and VR technologies in the manufacturing sectors. Full article
(This article belongs to the Special Issue Smart Manufacturing in the Era of Industry 4.0, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop