Challenges, Opportunities and Future Directions in Smart Manufacturing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 20 July 2026 | Viewed by 28
Special Issue Editor
2. Projects and Sustainable Engineering Group, C/Gonzalo Gutiérrez Quirós s/n 33600 Mieres, University of Oviedo, Oviedo, Spain
Interests: data science and engineering; smart manufacturing; process modeling and optimization; project management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Although the term smart manufacturing was coined in the early 2000s, recent advances in machine learning (ML) and artificial intelligence (AI), especially generative artificial intelligence (GenAI), are profoundly reshaping this field across several dimensions. The integration of GenAI into smart manufacturing introduces significant strategic shifts, particularly in enabling human-centric automation, broadening access to advanced tools, and enhancing system adaptability. In this context, Transformer architectures are proving to be of crucial importance. However, generative AI is not the only significant factor in this field. Concurrent advances in other ML algorithms, such us recent variants of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), are also transforming smart manufacturing.
Notwithstanding its transformative potential, the adoption of AI and ML in smart manufacturing faces significant challenges. A central issue concerns the dependence of these models on large volumes of high-quality data, which are often fragmented, inconsistent, or not available in real time. Additionally, manufacturing environments typically consist of heterogeneous legacy systems, sensors, and platforms that are not inherently interoperable, posing further barriers to effective data integration and the deployment of AI-driven solutions. To address these limitations, ongoing efforts are focused on the development of standardized data architectures, the implementation of industrial Internet of Things (IIoT) frameworks, and the use of data lakes and middleware platforms that facilitate interoperability. Moreover, techniques such as data augmentation, transfer learning, and federated learning are increasingly being employed to mitigate data scarcity and enhance model robustness in complex manufacturing contexts.
This Special Issue encourages submissions on the latest developments in generative AI, data-centric challenges, and the integration of next-generation AI techniques within heterogeneous manufacturing ecosystems. For this Special Issue, original research articles focused on practical applications are welcome.
I look forward to receiving your contributions.
Dr. Vicente Rodríguez Montequín
Guest Editor
Manuscript Submission Information
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Keywords
- GenAI
- deep learning
- recurrent neural networks
- graph neural networks
- natural language AI
- heterogeneous manufacturing ecosystems
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