Introduction
Artificial Intelligence, or AI, is essentially the ability of computers, or machines, to perform human-like intelligent tasks. The definition of AI encompasses such areas as learning from experience, pattern recognition, understanding natural language, problem-solving, and decision-making. Mechanical engineering, on the other hand, studies the behavior of objects and systems in motion. Engineers design, develop, and test the behavior of engineered systems in the context of sustainability and energy efficiency.
In mechanical engineering, there is expertise in such areas as factory automation, product design, and applications of computer-aided design (CAD). Mechanical engineers can build on these areas of knowledge and engineering practice by integrating AI into the knowledge of a real-world problem. Part of the process may have elements of data science and statistics, which will be discussed later. In mechanical engineering there are still many problems that need solving; for instance, concerns for sustainability where engineers must take responsibility for waste reduction while increasing efficiency, and in regard to energy-efficient systems where new, energy efficient products must utilize renewable energy sources. Competition presents a challenge because engineers must be trained to work under pressure and maintain the quality of the company’s product(s).
Data science is an evolving field globally. It requires understanding of code (mostly python, occasionally java) and statistics. There are a variety of ways to extract knowledge from the input in this field, like machine learning. Machine Learning is a method of statistical inference that predicts the dependent variable through the independent variables that increase the predictive power of policies acting on outcomes. This information will help a mechanical engineer understand the limitations and possible biases of the system and will help the engineer to make decisions on how to utilize it.
The Use of AI in Mechanical Engineering
Artificial Intelligence (AI) is quickly becoming a valuable resource in mechanical engineering because it increases the accuracy and efficiency for numerous applications. Many of these methods have been automated instead of requiring engineers to conduct manual work. This allows engineers to focus on solving difficult and complex problems. An example of an application of AI is predictive maintenance. In predictive maintenance, machine learning algorithms are utilized to analyze sensor data to predict when equipment will fail before it actually happens. Continued advancement of predictive maintenance will reduce downtime and increase the service life of machines, thereby resulting in enhancements to both cost and production efficiencies.
In design and simulation capabilities, AI-driven software provides engineers with vast potential for efficiency and optimization. Design optimization is achieved through engineers using software to run several simulations quickly, and then through AI driven simulation, engineers can test multiple configurations to identify the best solutions more efficiently than hardware simulations. AI systems can also recommend better design changes based on historical data, resulting in improved product performance and reduced waste during the production process.
Factory automation will continue to benefit greatly through the advancement of AI. A benefit from robots and other heavy machinery that will incorporate AI will allow flexibility to modify tasks being performed, while also in some cases learning to perform tasks from the environment it exists in. The inclusion of AI into robots will improve productivity in manufacturing plants, while also increasing production flexibility in production lines for manufacturers.
Conclusion
Integrating AI into mechanical engineering processes not only makes existing aspects of traditional processes easier, but it opens up potential for innovation, ultimately pushing mechanical engineering towards being adaptable enough to meet the needs of industry today.