Special Issue on Design by Data: Cultivating Datasets for Engineering Design

Journal of Mechanical Design
Journal of Mechanical Design icon
In today's rapidly evolving world, data plays a pivotal role across various sectors including engineering. The success of data-driven methods in image and text analysis is largely due to adequately large datasets, which have enabled tools like ChatGPT and Stable Diffusion. Data-driven design is revolutionizing engineering design, enhancing design theory, decision-making, optimization, and educational curricula. However, challenges in adopting machine learning for data-driven design include limited high-quality publicly available datasets, datasets with insufficient number of samples and features, and a lack of real-world datasets. 

This special issue provides a platform for papers that investigate data-driven methods for design, provide insightful discussions about the data used, and publicly release their dataset. It also encourages submissions to address critical challenges in engineering dataset creation, such as multi-modality, handling missing information, managing diverse data types, navigating dependencies among attributes, ensuring data quality, and integrating complex data structures and real-time data. In particular, we seek papers that release datasets in, but not limited to, the following areas:

Topic Areas


  • Complex 2D or 3D design artifacts (including CAD) with high-quality numerical simulations of performance
  • Mechanical components, such as mechanisms, gears, bearings, linkages, and their assemblies, e.g., gear train designs
  • Multi-modal representations of design artifacts
  • Synthetic data augmentation of datasets through additional data points or label and performance metrics
  • Data on integrated mechanical systems, electrical networks, and socio-technical systems
  • User preferences data that allows modeling and prediction of consumer needs 
  • Operational data for monitoring, diagnosing, predicting system life, and supporting digital twins 
  • Interactions in design teams during problem-solving and design ideation activities
  • Data from manufacturing processes and generated parts

Special Issue Publication Dates

Paper submission deadline: June 1, 2024
Initial review completed: August 1, 2024
Publication date: March 2025

Submission Instructions

Detailed submission instructions for this special issue can be found on the Journal of Mechanical Design’s companion website

Papers should be submitted electronically to the journal through the ASME Journal ToolIf you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here
Once at the Paper Submittal page, select the Journal of Mechanical Design, and then select the Special Issue on Design by Data: Cultivating Datasets for Engineering Design.

Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.

Guest Editors

Dr. Faez Ahmed, Massachusetts Institute of Technology, USA, faez@mit.edu

Dr. Cyril Picard, Massachusetts Institute of Technology, USA, cyrilp@mit.edu

Dr. Michael Kokkolaras, McGill University, Canada, michael.kokkolaras@mcgill.ca

Dr. Wei Chen, Northwestern University, USA, weichen@northwestern.edu

Dr. Christopher McComb, Carnegie Mellon University, USA, ccm@cmu.edu

Dr. Pingfeng Wang, University of Illinois Urbana-Champaign, USA, pingfeng@illinois.edu

Dr. Ikjin Lee, Korea Advanced Institute of Science & Technology, Republic of Korea, ikjin.lee@kaist.ac.kr

Dr. Tino Stankovic, ETH Zurich, Switzerland, tinos@ethz.ch

Dr. Douglas Allaire, Texas A&M University, USA, dallaire@tamu.edu

Dr. Stefan Menzel, Honda Research Institute Europe, Germany, stefan.menzel@honda-ri.de

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