Assessing student learning of design and project management skills in a project-based course

The design process followed by mechanical engineers is undergoing significant changes with the development of rapid prototyping techniques [1] and of artificial intelligence (A.I.) supported tools [2]. The job market is highly affected by these changes. However, Deininger et al. [3] show that novice designers do only rarely take advantage of the new tools and if they do, their use is unintentional. There is a clear need for mechanical engineers to be trained to use A.I.-based tools. Created in 2016-2017, the ME-403 course in “Applied mechanical design” seeks to help students develop both design and project management skills though a highly applied project-based format. In 2018-2019, the course has been modified to include the use of A.I.-based tools: students first experience the traditional design process and then the A.I.-based process with the same set of requirements. This contribution presents the results of a study based on the data collected during normal teaching and learning activities of this course. Indeed, students submit several group reports, which include design process information, design data (chosen components and dimensions) as well as project management related information. In addition, students answer a “Project Management Skills” questionnaire at the beginning and at the end of the course to get feedback on their skills in five project-management domains (planning, risk-assessment, ethical sensitivity, communication and interprofessional competence). Because the course is currently running, we cannot summarize the findings yet, but we intend to report on: • The evolution of students’ project-management skills, using the pre-post responses to the questionnaire and project-management information extracted from students reports; • The evolution of students’ design process and performance between the traditional and A.I.-based parts of the project, using design information extracted from students reports. Finally, we will describe how these results will inform the teaching of the course for the next occurrence.

Automated Design: A Journey Across Modelling, Optimization, and Education

Cyril Picard

Machine intelligence greatly impacts almost all domains of our societies. It is profoundly changing the field of mechanical engineering with new technical possibilities and processes. The education of future engineers also needs to adapt in terms of techniques and even skills. Using the design of electro-mechanical actuators as a common thread, this work explores the many-facets of automated design: modeling, optimization, and education, and looks for the prerequisites essential to its successful application. The journey starts by building a modular and integrated model. It focuses on the prediction of system-level specifications that yield high added-value for decision-makers and shorten the path from the model to the final product. Combined with multi-objective evolutionary algorithms (MOEAs) and visualization tools, the model forms an automated design tool that helps engineers and decision-makers to rapidly get important insights into their design task. Its potential and benefits are validated through two specific applications. The results, however, also highlight a gap between the reported performance of optimizers on common benchmark problems and the actual performance on these problems. To further develop optimizers, appropriate and realistic benchmark problems are needed. A subset of the integrated design model is used to formulate a new test suite called MODAct, composed of 20 constrained multi-objective optimization problems (CMOPs) with variable levels of complexity. In addition, numerical approaches to evaluate the constraint landscape of CMOPs are introduced and applied to identify the differences in features of MODAct against 45 benchmark problems from literature. Further, the convergence performance of three algorithms on the same problems highlights the key role of constraints and, in particular, the number of simultaneously violated constraints in MODAct problems. In a next step, existing constraint handling strategies suitable for MOEAs along with a newly proposed technique for many-constraint problems are evaluated. Their parameters are tuned for different problems. The performance of the various configurations further highlights the difference between MODAct and other benchmark problems and show the highly competitive results of the proposed constraint handling technique on realistic design problems. As the technical limits are removed, the impact of automated design on the work of future engineers should be considered. On the one hand, the development of professional skills by students working on team project in different settings has been evaluated thanks to 205 students from three classes. Explicitly addressing these skills within the project seems key to support stronger and broader learning, suggesting changes that do not require a full curriculum redesign. On the other hand, nine groups (33 students) have been asked to design an actuator using a conventional approach followed by an automated design approach. The actuators suggested by students using the automated tool outperform the designs obtained through the traditional approach. Six groups even suggest solutions cheaper, three of which are also smaller, than the product of experienced industry engineers. Students proved thus capable of leveraging the tool within a short time. The analysis of their mistakes suggests possible improvements for future tools. As these students leave university, they carry the hope to see such methods spread in industry.

EPFL2021

Automated Design: A Journey Across Modelling, Optimization, and Education

Cyril Picard

EPFL2021