| Title of the event | AI Applications in Mechanical Engineering: Design, Simulation & Optimization |
| Category | Webinar |
| Mode of conduction | Online |
| Date and Time | 26-02-2025 and 05.30 p.m. to 7.00 p.m. |
| Venue/Meeting link | https://meet.google.com/vrn-cgbc-tcq |
| Content Delivered | Generative Design, Deep Learning in Mechanical Engineering, Supervised & Unsupervised Learning in Engineering, AI Training Using Google Colab. |
| Convenor | Dr. A. Mathivanan, Head of the Department |
| Coordinators | Dr. M. Veeramanikandan, AP / Mech Dr. R. Thirunavukkarasu, AP/Mech Dr. T. Elangovan, AP / Mech |
| Participants Count | 83 |
| Attachments | 1. Event invitation 2. Resource person’s details 3. Photos 4. Event write-up |
Summary of the Event
The Department of Mechanical Engineering, in collaboration with the Institutions Innovation Council, organized a workshop program under IEI club activity on 26th Feb 2025 from 12.20 PM to 1:30 PM. Convenor Dr. A. Mathivanan, Head of Department, Mechanical Engineering, delivered the welcome address Dr. V. Thanigaivelan, Asso. Prof/ Mech conducted the work shop followed by a quiz program. Second and Third-year mechanical engineering students actively participated in the workshop and Coordinators Dr. V. Vinodkumar, AP/Mech and M. Krishna, AP/Mech, efficiently managed the event’s logistics.
Objectives
To introduce participants to cutting-edge AI applications in mechanical engineering, focusing on design, simulation, and optimization processes. To provide hands-on experience with generative design techniques, demonstrating how AI can revolutionize traditional design approaches. To explore the fundamentals of deep learning and its applications in mechanical engineering, including predictive maintenance and performance optimization. To teach participants the differences between supervised and unsupervised learning, and their respective uses in engineering problems. To familiarize attendees with Google Colab as a platform for AI training and implementation in mechanical engineering projects.
Event Outcomes
Students will gain practical skills in using AI-powered design optimization tools, enabling them to create more efficient and innovative mechanical systems. Students will understand how to implement deep learning algorithms for analyzing sensor data and predicting mechanical system behavior. Students will be able to distinguish between supervised and unsupervised learning approaches and apply them appropriately to various engineering challenges. Students will acquire hands-on experience using Google Colab for AI model development and training in mechanical engineering applications. Students will develop a comprehensive understanding of how AI can enhance simulation accuracy and speed up the testing process for mechanical systems.
