Engineering design informatics is the application, development, and creation of advanced computing, information, and communication technologies for engineering design. It enables the study of specific aspects of design in handling large sets of data and information rich digital objects. Engineering design informatics includes attempts to develop a deeper understanding of the design process using informatics. Different from other fields of informatics, such as bioinformatics, engineering design informatics seeks contributions beyond static understanding of engineering design. It seeks to create theory, method, and science use data, digital objects, information, and knowledge to help engineers better generate, develop, test, and realize designed products, systems, and services.

For example, engineering design informatics may identify information that may help a designer during the design process by using programs to assist in retrieving, storing, manipulating, and sharing data. The information of interest may describe previous designs, design needs and issues, correlations between variables, or may contain sources of inspiration for novel, innovative solutions. Design informatics programs allow users to explore more of the existing knowledge space, which can help stimulate ideas and the creation of new or improved design solutions.

We invite contributions for this Special Issue regarding the development of design informatics systems and tools as well as their application. Examples include but are not limited to the following:

• • Data mining to find and generate concepts to solve design problems

• • Informatics-based methods to improve engineering decision making

• • Informatic analysis of engineering processes and design processes

• • New methods to support automatic information intensive activities in engineering design

• • Information processing as applied to risk and failure reduction and avoidance

• • Information modeling approaches to design evaluation

• • Informatics-based approaches to strategic product planning and realization

• • Machine learning methods applied to large data sets to inform and guide designers on customer needs

• • Methods for the systematic organization and access and long-term storage and interoperability of engineering design data

• • New methods for capture of design rationale and for automatic documentation of design processes

All submissions will be anonymously reviewed by at least three reviewers. The selection for publication will be made on the basis of these reviews. High quality papers not selected for this Special Issue may be considered for standard publication in AI EDAM.

Information about the format and style required for AI EDAM papers can be found at http://aiedam.usc.edu/index.php/Authors/ForAuthors

Note that all inquiries and submissions for Special Issues go to the Guest Editors, not to the Editor in Chief.

Important Dates

• Intend to submit (Abstract & Title): As soon as possible

• Submission deadline for full papers: October 1, 2015

• Reviews due: December 15, 2015

• Notification and reviews due to authors: January 15, 2016

• Revised papers due from authors: March 1, 2016

• Final version due: May 1, 2016

Guest Editors

• Daniel McAdams

• Department of Mechanical Engineering

• Texas A&M University

• 3123 TAMU

• College Station, TX 77843-3123, USA

• E-mail: dmcadams@tamu.edu

• Chris McMahon

• Department of Mechanical Engineering

• University of Bristol

• Queen's Building, University Walk

• Clifton BS8 1TR, United Kingdom

• E-mail: chris.mcmahon@bristol.ac.uk

• Ying Liu

• Institute of Mechanical and Manufacturing Engineering

• School of Engineering

• Cardiff University

• Cardiff CF24 3AA, United Kingdom

• E-mail: LiuY81@cardiff.ac.uk