December 2022, Volume 22 – Issue 6
The digitalization of manufacturing and the technologies associated with Industry 4.0 has led to an explosion in unstructured data across the entire product lifecycle, including engineering design and manufacturing activities, which are embodied in the emerging “digital thread” and corresponding “digital twin” of the product. These technologies expose rich information that can be used to achieve data-driven (re)design of products and engineering, support continuous improvement of manufacturing operations, and enhance product development practices. However, challenges persist across the entire product lifecycle due to the massive scale at which this data is generated and shared (e.g., some researchers have reportedly resorted to the inelegant solution of mailing hard drives). Significant challenges also arise due to the format, variety, and content of the data as well, limiting its broader use in engineering design and manufacturing research.
This special issue aims to capture contemporary perspectives on both the challenges and opportunities regarding the generation, collection, curation, storage, transmission, and transformation of engineering design and manufacturing data in digital databases and repositories. Topics of interest include, but are not limited to:
- Methods for data storage, management, and curation of product lifecycle data
- Repository-based exploration of design and manufacturing data
- Translation and transmission techniques for facilitating scalable data-driven pipelines
- Automated data/model generation for engineering workflows (e.g., virtual scenes and data-driven decision-making)
- Opportunities of standards development for data management in engineering
- Data representations and data schemas to enable the digital thread
Papers Published
Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput
Patent Data for Engineering Design: A Critical Review and Future Directions
Design for Artificial Intelligence: Proposing a Conceptual Framework Grounded in Data Wrangling
STEP-NC Process Planning for Powder Bed Fusion Additive Manufacturing
Ontology Network-Based In-Situ Sensor Selection for Quality Management in Metal Additive Manufacturing
My Facts Are not Your Facts: Data Wrangling as a Socially Negotiated Process, A Case Study in a Multisite Manufacturing Company
Guest Editors
- Christopher McComb, Carnegie Mellon University, ccm@cmu.edu
- William Bernstein, Air Force Research Laboratory, william.bernstein@us.af.mil
- Vincenzo Ferrero, National Institute of Standards and Technology, vincenzo.ferrero@nist.gov
- Timothy W. Simpson, The Pennsylvania State University, tws8@psu.edu
- Nicholas A. Meisel, The Pennsylvania State University, nam20@psu.edu
- Binil Starly, North Carolina State University, bstarly@ncsu.ed
