How Related Are Mechanical Engineering and Artificial Intelligence?

Artificial Intelligence (AI) is no longer a futuristic concept but a present-day reality, impacting various fields including Mechanical Engineering. This article delves into the integration of AI, particularly neural networks, in solving complex problems related to fluid mechanics in the context of mechanical engineering.

Understanding the Role of Neural Networks in Mechanical Engineering

In my mechanical engineering course conclusion work at the Federal University of Rio de Janeiro (UFRJ), we explored the application of neural networks to model physical phenomena, such as calculating the necessary energy to raise water from one reservoir to another. This task involves intricate fluid mechanics and substantial mathematical calculations. The use of neural networks not only simplifies these calculations but also enhances their accuracy and applicability in industry.

Case Study: Modelling Energy for Water Lift

The project aimed to determine the energy required to lift water to a height of 30 meters at a specified flow rate. This calculation necessitates a deep understanding of fluid mechanics, including criteria such as pipe diameters, lengths, and roughness, internal pressures, and energy losses due to friction and localized obstacles.

Training Neural Networks for Energy Calculation

To achieve this, the neural network was trained using a vast dataset generated in MATLAB. MATLAB was chosen for its robust capabilities in managing tables and matrices. The network was trained to compute the energy based on numerous input variables, leading to highly accurate predictions after a significant amount of training.

Generalization and Accuracy of Neural Networks

The trained neural network demonstrated remarkable accuracy, capable of predicting energy requirements even for inputs that were not part of the training dataset. This ability to generalize from the training data is a critical feature of well-trained neural networks. The graphs below illustrate the close agreement between the neural network's predictions (blue line) and traditional mathematical calculations (red line).

Graphical Representation of Neural Network Predictions

Benefits and Implications of Using AI in Mechanical Engineering

The application of neural networks in mechanical engineering offers several benefits:

Enhanced Accuracy: Neural networks can handle complex, multi-dimensional problems more efficiently than traditional methods. Efficiency: Automation through AI can significantly reduce the time and resources required for calculations. Generalization: Trained neural networks can adapt to new scenarios, making them valuable in dynamic environments.

Integration of Computational Mechanics with AI

Projects like the one at UFRJ showcase how AI and computational mechanics can complement each other. While traditional methods rely heavily on precise mathematical formulations, AI can help bridge the gap between theory and practical application.

Case Studies from Europe and the USA

European and US universities have a strong tradition of developing and utilizing specialized software in Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD). For instance, the French methodology 'CODEASTER', 'TFEL', 'Mfront', and 'MoFEM' demonstrate advanced computational techniques. India, on the other hand, is increasingly adopting open-source software to enhance its engineering capabilities.

Worldwide Training on Formulas in Product Design

To address the gap between theoretical knowledge and practical application, organizations such as DeepJyoti are providing worldwide training on the application of formulas in product design. Their initiatives aim to empower engineers by bridging the knowledge gap and reducing the fear of applying formulas in real-world scenarios.

Conclusion

The integration of AI, particularly neural networks, in mechanical engineering is transforming how we approach complex problems. By leveraging the strengths of both AI and traditional methods, engineers can achieve higher levels of accuracy and efficiency. Universities and industry must continue to promote these technologies to ensure that the next generation of engineers is well-equipped to harness the power of AI in their work.