Special Journal Issue on Data Wrangling to Support Research on Engineering Design and Manufacturing
Special Journal Issue on Data Wrangling to Support Research on Engineering Design and Manufacturing
The ASME Journal of Computing and Information Science in Engineering is currently accepting manuscripts for a special issue focusing on the topic “Data Wrangling to Support Research on Engineering Design and Manufacturing.” Authors who are interested in having their manuscripts included in the special issue, to be published in December 2022, should submit their manuscripts by March 2022.
The digitalization of manufacturing and the technologies associated with Industry 4.0 has led to an explosion of 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, limiting its broader use in engineering design and manufacturing research.
The 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.
Manuscripts to be included in the special issue should focus on the listed topics, 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; and data representations and data schemas to enable the digital thread.
Manuscripts should be submitted electronically to the journal by March 2022, via Journals Connect at journaltool.asme.org. Authors who have an account should log in and select “Submit Paper” at the bottom of the page. Authors without an account should select “Submissions” and follow the steps. At the Paper Submittal page, authors should select “ASME Journal of Computing and Information Science in Engineering” and then select the special issue “Data Wrangling to Support Research on Engineering Design and Manufacturing.” Papers received after the deadline or papers not selected for inclusion in the special issue may be accepted for publication in a regular issue. Early submission is highly encouraged. Please also email the editor-in-chief, Professor Satyandra K. Gupta, at guptask@usc.edu, to alert him that your paper is intended for this special issue.
Authors may submit either tech briefs (~5,000 words) or research articles (~10,000 words). The focus of this special issue is on the generation, collection, curation, storage, transmission, and transformation of the data itself. Research and development contributions should explicitly relate to the domain-specific challenges of mechanical design, product development, manufacturing, and/or similar areas. Submissions that propose novel machine learning or other algorithms, for instance, will be considered out-of-scope. To the extent possible, authors are encouraged to make accessible the data and software used in their submissions.
The guest editors for the special issue are Christopher McComb, Carnegie Mellon University, USA, ccm@cmu.edu; William Bernstein, Air Force Research Laboratory, USA, william.bernstein@us.af.mil; Vincenzo Ferrero, National Institute of Standards and Technology, USA, vincenzo.ferrero@nist.gov; Timothy W. Simpson, The Pennsylvania State University, USA, tws8@psu.edu; Nicholas A. Meisel, The Pennsylvania State University, USA, nam20@psu.edu; and Binil Starly, North Carolina State University, USA, bstarly@ncsu.edu.
For more information on the ASME Journal of Computing and Information Science in Engineering, visit https://asmedigitalcollection.asme.org/computingengineering. To learn more about the ASME Journal Program, visit www.asme.org/publications-submissions/journals/information-for-authors.
The digitalization of manufacturing and the technologies associated with Industry 4.0 has led to an explosion of 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, limiting its broader use in engineering design and manufacturing research.
The 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.
Manuscripts to be included in the special issue should focus on the listed topics, 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; and data representations and data schemas to enable the digital thread.
Manuscripts should be submitted electronically to the journal by March 2022, via Journals Connect at journaltool.asme.org. Authors who have an account should log in and select “Submit Paper” at the bottom of the page. Authors without an account should select “Submissions” and follow the steps. At the Paper Submittal page, authors should select “ASME Journal of Computing and Information Science in Engineering” and then select the special issue “Data Wrangling to Support Research on Engineering Design and Manufacturing.” Papers received after the deadline or papers not selected for inclusion in the special issue may be accepted for publication in a regular issue. Early submission is highly encouraged. Please also email the editor-in-chief, Professor Satyandra K. Gupta, at guptask@usc.edu, to alert him that your paper is intended for this special issue.
Authors may submit either tech briefs (~5,000 words) or research articles (~10,000 words). The focus of this special issue is on the generation, collection, curation, storage, transmission, and transformation of the data itself. Research and development contributions should explicitly relate to the domain-specific challenges of mechanical design, product development, manufacturing, and/or similar areas. Submissions that propose novel machine learning or other algorithms, for instance, will be considered out-of-scope. To the extent possible, authors are encouraged to make accessible the data and software used in their submissions.
The guest editors for the special issue are Christopher McComb, Carnegie Mellon University, USA, ccm@cmu.edu; William Bernstein, Air Force Research Laboratory, USA, william.bernstein@us.af.mil; Vincenzo Ferrero, National Institute of Standards and Technology, USA, vincenzo.ferrero@nist.gov; Timothy W. Simpson, The Pennsylvania State University, USA, tws8@psu.edu; Nicholas A. Meisel, The Pennsylvania State University, USA, nam20@psu.edu; and Binil Starly, North Carolina State University, USA, bstarly@ncsu.edu.
For more information on the ASME Journal of Computing and Information Science in Engineering, visit https://asmedigitalcollection.asme.org/computingengineering. To learn more about the ASME Journal Program, visit www.asme.org/publications-submissions/journals/information-for-authors.
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