Engineering applications of artificial intelligence

Nexus March 02, 2024 0

 

Engineering is one of the many industries where artificial intelligence (AI) can completely change things. AI for engineering refers to applying computer science and artificial intelligence to assist engineers in analyzing and optimizing complicated systems. 

Machine learning is a new and exciting area of artificial intelligence. In the past ten years (2013–2023), Deep Learning algorithms have demonstrated their worth in machine learning models. Now that they are no longer restricted to academia, they are piquing the interest of global giants in the automotive and aerospace industries and those in the biological sciences and civil and naval engineering.

Companies like Bosch and Airbus, to mention a couple, are actively implementing Deep Learning. This post focuses on the engineering applications of artificial intelligence.

Why is AI engineering receiving so much attention these days, and what can AI engineers do to fuel this growing interest?

The tasks that an artificial intelligence (AI) engineer could work on are as follows:

●     They create algorithms for machine learning.

●     An AI engineer can design and implement machine learning algorithms to allow a system to learn from data and make predictions or judgments without explicit programming.

●     They construct systems for natural language processing. A machine intelligence (AI) engineer can develop chatbots or language translation software, among other systems that can comprehend and process human language.

●     Systems that analyze and interpret visual input from a variety of sources, such as cameras and recorded 3D industrial shapes (CAD), can be created by an AI engineer.

Artificial Intelligence Applications in Engineering

1.   Assist with Design Optimization

You can utilize AI to optimize system design as a design engineer or AI engineer. Determining the ideal form for a mechanical component is one example. Mechanical engineers look for lightweight parts with the least amount of mechanical stress.

2.   Assist in Increasing Product Quality

An artificial intelligence (AI) engineer can use data patterns and trends to find product issues before release. This helps manufacturing and product design departments find and address problems with their products.

3.   Assist in Automating and Reducing Standard Engineering Tasks.

This will be the primary subject of the article's second section. Data analysis, modeling, and simulation are routine jobs that can be costly and time-consuming.

Aerodynamics simulations for cars may need hundreds of processors and hours of human labour.

 

Therefore, automation can free up skilled engineers to work on more difficult and imaginative projects while enabling less experienced engineers to tackle modelling and simulation confidently.

4.   Industry 4.0 and Smart Manufacturing Using AI Techniques

Industry 4.0 is all about smart factories, which use connected equipment to maximize efficiency through improved automation.

This system, which includes data analytics in security measures, automatically configures systems, and plans control actions while monitoring performance predictions or troubleshooting potential difficulties, heavily relies on artificial intelligence. It also provides quality control for dependable production procedures.

AI is essential for automated production, cost reduction, increased safety, and output enhancement when robotics are incorporated into the process! Industry 4.0's supply chain integration and efficient optimization are made possible by AI solutions.

5.   The Engineering of Aerospace

Artificial intelligence engineer’s knowledge  can be applied to aerospace engineering to improve the performance and design of spacecraft and airplanes.

AI can be used by engineers to evaluate data from tests and simulations to enhance the aerodynamics and structural integrity of airplanes.

AI is also capable of piloting and controlling autonomous aircraft, like as drones, which are useful for a variety of applications including delivery, surveillance, and search and rescue.

6.   Small Factories

Industry 4.0, often known as the concept of a "smart factory," uses cutting-edge technology to elevate and revolutionize manufacturing processes in the current industrial period.

To improve automation capacity, a variety of systems, including robots, sensors, embedded software, and data analysis, work together to convey information across machines.

7.   Robotics Applications of Artificial Intelligence

Another area where applications of artificial intelligence are frequently applied is robotics. Artificial intelligence (AI)-powered robots employ real-time updates to detect obstructions in their path and instantaneously plan their route.

It is applicable to:

●     Transporting supplies in factories, hospitals, and warehouses

●     Sanitizing big machinery and offices

●     Inventory control

Jobs in Artificial Intelligence Engineering

The professional profiles of design and machine learning engineers will now be examined. The following are some job titles for an artificial intelligence engineer.

1.   Design Engineer

An industrial product's conceptualization or improvement is the responsibility of a design engineer. Their job is to boost the company's profitability while making it more competitive in the market and sustainable in terms of price and performance.

Engineers employ various tools and materials, including computer-aided design (CAD) and computer-aided engineering simulation (CAE) software, to produce prototypes and finished goods.

Design engineers may work on various projects, such as machinery buildings, consumer goods, medical gadgets, and vehicles.

2.   Machine Learning Engineer

Professionals who use machine learning techniques to design, develop, and implement machine learning models and systems are known as machine learning engineers.

Machine learning engineers work on various projects, such as creating recommendation engines for e-commerce websites and machine learning algorithms for self-driving automobiles.

Machine learning experts are passionate about developing algorithms that help people by giving them software that performs as well as or better than people for particular activities.

Strong computer science and mathematics backgrounds are typical for machine learning engineers, as is familiarity with machine learning frameworks like TensorFlow and programming languages like Python.

The following are a few examples of the regular tasks and fundamental technical abilities of machine learning engineers:

●     Carry out statistical analysis.

●     Apply machine learning models with a solid grasp of machine learning methods and an awareness of the business procedures of the end-users

●     A capacity for teamwork in data science

As a result, a machine learning engineer should be well-versed in computer science and artificial intelligence ideas and methods and able to apply them to practical issues while working with other teams like design engineers.