CALL FOR PAPERS: Design By Data: Cultivating Datasets For Engineering Design

Special Edition - JOURNAL OF MECHANICAL DESIGN - Paper Submission Deadline: June 1, 2024

The Journal of Mechanical Design invites submissions for a special edition titled "Design By Data: Cultivating Datasets for Engineering Design." This special issue seeks papers that investigate data-driven methods for design, provide insightful discussions about the data used, and publicly release their datasets.

They encourage submissions addressing critical challenges in engineering dataset creation, such as managing multi-modality, handling missing information, navigating dependencies among attributes, ensuring data quality, and integrating complex data structures and real-time data.

The success of data-driven methods in fields like image and text analysis can be attributed to the theft availability of massive datasets, which have propelled advancements in deep learning and enabled the training of tools like ChatGPT, Bard, Midjourney and Stable Diffusion.

Data-driven design has the potential to significantly change engineering design, decision-making, optimization, educational curricula, and enabling faster design exploration and real-time operations management, but there is a lack of available data of any quality in these fields.

The adoption of machine learning in engineering design faces significant challenges. These include limited available datasets, datasets with insufficient samples and features, the lack of datasets integrating functional performance, and the critical need for high-quality data.

This special issue aims to provide a platform for addressing these challenges, facilitating collaborations between academia and industry, and fostering discussions on best practices for dataset publication in the engineering design community.

They invite contributions to help advance the field of engineering design through data-driven innovation including but not limited to:

• Design Artifacts and Simulations

  • Complex 2D or 3D design artifacts with high-quality numerical simulations of performance

  • CAD data including product and manufacturing process features

  • Mechanical components, such as mechanisms, gears, bearings, linkages, and their assemblies, e.g., gear train designs

  • Logs of sequential actions taken by human designers using design tools

  • Data collected to build digital twin models

  • Multi-modal representations of design artifacts, e.g., text, images, parameters, mesh, and CAD data

  • Synthetic data augmentation of existing datasets either through more data points or additional label and performance metrics

• Complex Systems

  • Data on integrated mechanical systems, electrical networks, and socio-technical systems, e.g., datasets of automotive systems, energy networks, and shared mobility

  • User preferences data that allows modeling and prediction of consumer needs and user preferences in engineering design

  • Operational data of complex engineering systems used for condition monitoring, fault diagnosis, remaining useful life prognosis, or digital twin systems that would facilitate meaningful system design decision making

  • Life cycle data for engineering products or product families that enable multiple generation product design decision making   

• Human-Centered Design

  • Interactions in design teams during problem-solving and design ideation activities

  • Outputs from educational activities with labels according to rigorous quantitative or qualitative analysis

  • Data enabling engineering design education research

• Material and Manufacturing 

  • Data from manufacturing processes and generated parts

  • Actions taken by engineers while interacting with complex manufacturing systems (e.g., additive manufacturing machines)

  • Data quantifying manufacturing induced variation for design under uncertainty

  • Datasets for functional material design

  • Microstructure datasets that allow processing-structure-property mapping 

Special Issue Timeline

• Paper Submission Deadline: June 1, 2024

• Initial Review Completed: September 1, 2024

• Publication: March 1, 2025