In order to design competitive products that meet the challenges related to sustainability, future engineers need knowledge and experience in applying and integrating optimization theory in the engineering design process. The Chalmers course Engineering Design and Optimization addresses this need and was developed and first offered in the fall semester of 2013. The course is held jointly for the MSc programmes in Applied Mechanics, Automotive Engineering and Product Development at Chalmers University of Technology, Sweden. This paper aims to provide a detailed account of the course development experiences, teaching methods and course evaluations using CDIO Standards. It also includes a discussion of the learning objectives, required resources, instructional processes and student assessments. The course is analyzed to see to what extent the course aim is satisfied and highlight areas of improvement. The course aims at integrating engineering design methodologies with concepts of modern optimization theory. The students are expected to be able to create design solutions that are both creative and have better performance than solutions generated using traditional methods. The course structure is built of lectures, industry guest lectures, workshops, flipped classroom, software training, three CDIO-based project assignments, a mid-term exam and a final exam. The projects that are carried out in groups of two are: 1. The Cantilever Challenge: The design-build-test (DBT) approach is adopted in order to design the optimal cantilever beam. In the first iteration, the students design a beam using their prior engineering skills and evaluate the performance using FEM. After this, a topology optimization tool is introduced to the students through an in-class demonstration. The students continue the development cycle, now with the new method, in order to iterate towards a better-performing design. The final beam designs are 3D-printed and tested in a live experiment. 2. Redesign Using Material Selection and Optimization Methods: The objective is to apply reverse engineering methodology, optimization and material selection to redesign a failed product. The students perform a test-to-failure experiment and use functional modelling and FMEA to understand the problem before utilizing the gained knowledge to design, model and optimize a new version. 3. Multidisciplinary Design Optimization: This assignment targets learning objectives related to multidisciplinary problem solving and multi-objective optimization. The case is based on a real industrial problem concerning an engine encapsulation component with complex geometry and two layers of different materials. The component should both act as a heat insulator and a noise absorber but still have a minimum mass. The students learn how to decompose a complex problem using simple analytical models and iteratively increase the modelling and optimization complexity from 1D to advanced 3D modelling in multi-physics software. The iterative nature of the assignment allows for active learning and self-assessment by for instance discussing and comparing the accuracy and sufficiency of different modelling approaches. The assignments are assessed with respect to both engineering criteria as well as reporting and communication. The course is believed to be novel in the way optimization theory and tools are taught as an integrated learning experience with engineering design and physical prototyping. The interim and final outcome of the different design-implement experiences provide an opportunity for student self-assessment in terms of both the learning progress as well as results and modelling validity.
Proceedings of the 13th International CDIO Conference in Calgary, Canada, June 18-22 2017
Conceive
Design
Implement
Operate
