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Engineering Design Informatics is the application, development, and creation of advanced computing, information and communication technologies to Engineering Design. Engineering Design Informatics creates the ability to study design specific aspects of 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. Differently than other fields of informatics, such as Bioinformatics, Engineering Design Informatics seeks contributions beyond static understanding of Engineering Design. Engineering Design Informatics 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.

For this special “Engineering Design Informatics” issue of Artificial Intelligence in Engineering Design and Manufacturing we invite contributions regarding the development of design informatics systems and tools as well as their application. Examples include but are not limited to:

• 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 organisation 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 AIEDAM.

Note that all enquiries and submissions for special issues go to the Guest Editors, and not to the Editor-in-Chief.

Important dates:

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