Mechanical Engineering is known to be one of the oldest as well as fundamental branches of engineering is includes the principles of thermodynamics, material science, mechanics as well as manufacturing. With the rise of digital technologies, the boundaries of this branch have been expanded to a greater extent. The design, manufacturing and maintenance areas are becoming more easy and reliable with the help of AI and machine learning. This involvement of AI and machine learning has enabled the innovation across various industries.
Artificial intelligence refers to the simulation of human intelligence in machines. These systems are capable of performing tasks such as decision making, problem solving, learning and adapting based on data. Machine learning, a subset of AI, involves algorithms that allow computers to learn from data and make predictions or take decisions without being explicitly programmed
The complex process of thermodynamics, optimized designs and system failures enhances the overall productivity with the help of AI and machine learning. Machine learning helps in identifying unnecessary material from a part while maintaining structural integrity, leading to lightweight and cost-effective designs. The maintenance part is very critical but AI enhances it with the utilization of sensors, IoT devices which includes sounds, vibration, temperature etc. and analyze these data to predict component failures. Considering temperature as first factor, In manufacturing plants sensors are installed on machines and send real time data to cloud based ML model by which technicians are alert about heating of motor or it is wear out Similarly for vibrational system, AI driven simulation optimize the design parts like suspension systems and turbine blades for minimum weight and maximum strength.
As far as quality is concerned ML algorithms can detect the defects in manufactured parts in real time. Deep learning is used to measure dimensions and ensure tolerances. Smart manufacturing systems use AI to monitor the temperatures and pressures which are responsible for tool wear. ML suggests corrective action to maintain product quality and reduce waste. Mechanical systems, especially in HVAC and power plants, benefit from AI for efficient energy usage. AI models optimize heating, cooling and power distribution based on demand patterns. ML helps in forecasting energy loads and minimizing losses, contributing to sustainable engineering. AI-powered inventory systems can suggest when to reorder raw material to prevent both shortages and over stockings.
With significant benefits, there are several challenges in implementing AI and ML in the field of mechanical engineering. AI models require large amount of data which is not readily available. Traditional mechanical engineers may not be trained in programming or data science. It also includes high initial cost. The predictions sometimes are as per pattern rather than based on actual situations. Also due to cyber-attack, AI systems that are connected to networks are under threat which can suddenly stops the whole process.
As industries are moving towards industry 4.0, the demand for intelligent systems will continue to rise. In additive manufacturing also known as 3D printing technologies AI optimizes printing parameters and predict defects. AI can give material specification as per its required design and applications.
Some real world examples include Generic electric which uses AI for predictive maintenance in their jet engines and power plants. Tesla utilizes AI in the design and testing in its electric vehicles, using simulations to improve battery performance and structural integrity.
The role of AI and machine learning is no longer limited to only academic research but it is reshaping the industries by improving design accuracy, increasing operational efficiency and enabling predictive capabilities.
Abhimanyu Gadhavi
Assistant Professor, Faculty of enginering and technology
Madhav University, Sirohi (Rajasthan)