Virtual Project-Based Learning involving Computer-Aided Engineering & Data Analytics
This webinar is from the IFEES-GEDC library. In this session, Clive will share his perspectives and experiences based on over two decades as a thought-leader and student-mentor for university-level Project-Based Learning.
In particular, he will spotlight innovative work he has done with his students, coaching them on how to effectively apply CAE on several special projects – to complement theory and first principles.
Many of these projects had design & automotive outputs focused on efficiency, especially the reduction of material & mass while maintaining structural integrity. One of his students, Charl Rossouw, will end by sharing a bit about the latest work that he & Clive are doing with Data Analytics, using it complement their work with CAE in order to make better design decisions, faster.
Clive Hands is a seasoned Mechanical Engineering Lecturer with a 26-year history of working in the higher education industry, specializing in areas such as Strength (Mechanics) of Materials with a focus on simulation & analysis, optimization & light-weighting.
He has developed multiple online eLearning projects and outputs aimed at students, schools & industry utilizing browser-based platforms to facilitate enhanced teaching & learning capabilities in the Engineering sector.
He is a member of SAIMechE and an East Cape committee member. Charl Rossouw (Mechatronics Engineering B.Eng (Hons) & current M.Eng student) – has 5 years experience developing various online eLearning modules.
For his Final Year Project, he designed and built an additively manufactured and topology optimized cross-disciplinary SCARA robot with vision and web based control and monitoring, using Altair Embed for the controller logic and Altair Inspire and Altair SimSolid for structural analysis and optimization. Currently completing his masters degree in Mechatronics, which is centered around cloud-based IoT systems using live sensor data from IoT sensors and machine learning to aid predictive maintenance, utilizing Altair Knowledge Studio to aid in the design of the machine learning algorithms and Altair PanOpticon to aid in the real-time data processing and visualization.