Engineering applications of artificial intelligence

March 2, 2024

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.

Leave a Reply

Your email address will not be published. Required fields are marked *