Recent advances in artificial intelligence (AI) have shed light on the potential uses and applications of AI tools in engineering design. However, the aspiration of a fully automated engineering design process still seems out of reach of AI’s current capabilities, and therefore, the need for human expertise and cognitive skills persists. Nonetheless, a collaborative design process that emphasizes and uses the strengths of both AI and human engineers is an appealing direction for AI in design. Touncover the current applications of AI, the authors review literature pertaining to AI applications in design research and engineering practice. This highlights the importance of integrating AI education into engineering design curricula in post-secondary institutions. Next, a pilot studyassessment of undergraduate mechanical engineering course descriptions at the University of Waterloo and University of Toronto reveals that only one out of a total of 153 courses provides both AI and design-related knowledge together in a course. This result identifies possible gaps in Canadian engineering curricula and potential deficiencies in the skills of graduating Canadianengineers.
The present article provides a compilation of microstructures and respective strain fields expressed by them during elastic loading. These microstructures were synthesized in Abaqus Standard software and their strain fields were modelled using Abaqus based static implicit analysis. The Python Development Environment (PDE) in Abaqus was used. These microstructures were subjected to uniform displacement boundary condition to obtain strain fields in the plane-strain mode. The purpose of the generating this data was to test the efficacy of convolutional neural networks (CNNs) in predicting strain fields. This raw data consisting of microstructure and their strain fields was converted to images using MATLAB as two dimensional arrays with each pixel denoting value to be used as input for training the CNN. This processed data in the form of images can be potentially used in deep learning or data science methodologies to perform finite element simulations.
Automation and artificial intelligence (AI) are increasingly seen as appealing tools to perform design tasks traditionally accomplished by human designers. In today's digital economy, industries aim to adopt these tools to improve the efficiency of their complex design processes. But how does one decide what parts of their existing design process should be automated and which automation/AI tool to implement? With these questions in mind, we present a case study highlighting a company's decision-making process in converting its existing designer-dependent design process to one supported by automation. In this case study, we observed the company's decisions in selecting and rejecting certain automation and AI methods before finalizing a heuristics-based automation method that proved highly efficient compared to the company's traditional human-driven design program. In addition, we present three key discussion points observed in this case study: (1) the importance of implementing the designer's heuristics in the automation framework, (2) the importance of a uniform and modular design automation framework, and (3) the challenges of implementing AI methods.
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