Yigitcan Comlek, Liwei Wang, Wei Chen J. Mech. Des. May 2024, 146(5): 051706 https://doi.org/10.1115/1.4064133 The flowchart
While advances have been made in studying engineering design learning in the classroom, to date, such advances have not addressed hands-on, real-world learning experiences in university makerspaces. This article explores how such spaces support women engineers as designers, learners, makers, and community members.
This review of ethics, equity, and justice in engineering design literature explores how we discuss design justice related concepts in our research.
This featured article introduces a self-learning design agent, an innovative approach that doesn’t rely on human data or problem-specific knowledge. Instead, it uses deep learning and tree search to find high-performing and generalizable engineering design strategies.
In our modern world, complex systems such as power grids, transportation networks, and communication systems can be disrupted by both internal uncertainties and external factors like natural disasters. To make these systems more resilient, researchers have been working on design strategies that improve both reliability and recovery capabilities.
It is often desirable for engineered systems to sustain their functional effectiveness in the event of unforeseen disruptions. In analogy, biological systems are often physiologically adapted to survive both internal and environmental disruptions.
A key challenge in lengthy and labor-intensive design projects is leveraging knowledge and expertise.
3/9/2017 Luis A. Vasconcelos; Carlos C. Cardoso; Maria Sääksjärvi; Chih-Chun Chen; Nathan CrillyJ. Mech. Des. 2017; 139(3):031101-031101-13doi: 10.1115/1.4035540External
Current design theory lacks a systematic method to identify what designers know that helps them to create innovative products.
10/23/2015 Julia M. O’Rourke and Carolyn C. SeepersadJ. Mech. Des 137(9), 091101; doi: 10.1115/1.4030877Energy- and materials-efficient designs
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