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Frontiers in Manufacturing Technology is a peer-reviewed journal published by Frontiers, covering Digital Transformation in Industry, Manufacturing Process and Optimization, Additive Manufacturing and 3D Printing Technologies. Over its lifetime it has published 74 papers, which have received 839 citations.
| Publisher | Frontiers |
|---|---|
| Reference style | Author–year (Harvard) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Frontiers in Manufacturing Technology, 12(3), pp. 45–58.
Formats any DOI in Frontiers in Manufacturing Technology style. No sign-up. |
| Publishes research in | Digital Transformation in Industry Manufacturing Process and Optimization Additive Manufacturing and 3D Printing Technologies Sustainable Supply Chain Management Flexible and Reconfigurable Manufacturing Systems |
| ISSN | 2813-0359 |
| Citation impact (2-yr) | 5.5 |
| h-index | 15 |
| i10-index | 22 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | RWTH Aachen University |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
There is a recognized need for mass personalization for sustainability at scale. Mass personalization is becoming a leading research trend in the latest Industrial Revolution, whereas substantial research has been undertaken on the role of Industry 4.0 enabling technologies. The world is moving beyond mass customization, while manufacturing has led to mass personalization ahead of…
Digital Twins, as a technological pillar of Industry 4.0, correspond to the virtual representation and bi-fold a real-time communication of a digital counterpart of a process or a physical object. As the industrial and manufacturing landscape is shifting towards Industry 5.0, huge investments focusing on enhancing interactions between Operators and Cyber-Physical Systems (CPS) occur. Yet,…
Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary. Methods: This paper focuses on…
Electrical, metal, plastic, and food manufacturing are among the major energy-consuming industries in the U.S. Since 1981, the U.S. Department of Energy Industrial Assessments Centers (IACs) have conducted audits to track and analyze energy data across several industries and provided recommendations for improving energy efficiency. In this article, we used statistical and machine learning techniques…
Addressing the challenges of data scarcity and privacy, synthetic data generation offers an innovative solution that advances manufacturing assembly operations and data analytics. Serving as a viable alternative, it enables manufacturers to leverage a broader and more diverse range of machine learning models by incorporating the creation of artificial data points for training and evaluation.…
