How Is AI Used in Mechanical Engineering?

Artificial Intelligence (AI) has attracted interest in recent years due to AI's technological advancements and its increased ability to process large amounts of data.

Mechanical engineering faces several challenges today, such as designing systems and components for our future, respectful of sustainability and energy efficiency.

Incorporating advanced technologies into their designs can help mechanical engineering. An example of such a technology is AI and data analytics.

There has been tremendous progress in AI in areas such as autonomous vehicles. Focusing more specifically on upfront mechanical design, we will see the application of a machine learning algorithm by Neural Concept in a collaborative scenario; taken not from the future, but from the present.

The collaboration scenario between engineers we envisage is the following.

• AI turnkey solutions could originate from data analysis by data scientists and other expert engineers with expertise in programming languages and AI algorithms.
• On the other hand, most mechanical engineers would wish to exploit AI solutions while focusing on their expertise. Examples of mechanical engineering skills are factory automation, material synthesis, and product design, including CAD.

We will introduce the topics of AI and Data Science for mechanical engineers and show the up-and-coming applications of Deep Learning to 3D simulation with three short use cases.

Today's Mechanical Engineering Challenges

Modern mechanical engineering faces pressure from multiple angles. On one side, the demands of creating innovative products and processes. On the other side, ensuring that designs are sustainable and cost-effective.

More in details, some of the mechanical engineering challenges are:

• Sustainability: design and create environmentally friendly products and processes. Engineers are also responsible for reducing waste and increasing efficiency.

• Energy-efficient systems: mechanical engineering should deliver energy-efficient products and processes that use renewable energy sources.
• Competition between companies and profitability for shareholders: engineers are also under pressure to reduce costs and increase efficiency and productivity. Meanwhile, they should maintain or improve the product's quality.
• Finally, engineers must keep updated with new technologies and incorporate them into their designs.

Artificial Intelligence - Introduction

If you are in mechanical engineering, you may feel AI is inapplicable to your field. Let us show its practical applications in the product design office, after a short introduction.

AI simulates human intelligence in devices programmed to think and learn like humans.

AI is a branch of computer engineering that focuses on making machines perform specialized tasks that usually need human intelligence such as recognizing speech, understanding natural language (NLP) and, in general, learning from experience.

The most common process to get a usable AI is to use algorithms and statistical models to let a machine learn from data.

Are AI Devices Hardware or Software?

What is a AI machine? You can build AI with software, hardware, or a combination.

• Chatbots, virtual assistants, and image recognition programs are all types of AI software.
• Hardware-based AI includes cars that can drive themselves, drones, and robots.

A Short History of AI

AI has been present since the 50s when researchers began thinking about making machines that can think and learn like humans.

In the following decades, research focused on knowledge-based "expert systems", but these early AI still needed too much help from humans with explicit programming.

In the 80s and 90s, AI research evolved into Machine Learning, letting machines learn from data, instead of being explicitly programmed. This led to a more advanced artificial intelligence that could recognize images and voices.

AI has gained more attention in the past few years because technology has improved and more data is available.

The Role of Data Science

Data science is a field that involves using various techniques and tools to extract insights and knowledge from data. It is a multidisciplinary field that combines computer science, statistics, and domain-specific learning.

Data science plays a crucial role in AI by providing the data and insights needed to train and improve AI models.

For someone in mechanical engineering, it is essential to understand that data science provides a way to understand the underlying principles of how AI works and how it makes its predictions.

By understanding the data that an AI system is trained on, a mechanical engineer can better understand the limitations and potential biases of the system and make informed decisions on how to use it.

Artificial Intelligence vs Machine Learning vs Deep Learning — Definitions

Artificial intelligence is a vast field that includes several sub-disciplines.

Machine learning and deep learning are two of the many branches that fall under the general definition of AI.

Machine Learning (ML)

In ML, algorithms help computers learn from new data and past performance without human intervention for the development of their functions. ML applications include image recognition.

Deep Learning (DL)

DL is based on Artificial Neural Networks, i.e. mathematical models that take inspiration from the human brain in their structure and function, including training. The leading programming language is Python, one of the basic skills of data scientists and anyone in software product development.

Mechanical Component Design and Algorithms

The design process for mechanical components involves analyzing a component's function and performance requirements, determining its materials and manufacturing methods, and then creating detailed drawings and specifications that can be used to manufacture the component.

• Conceptual Design. The mechanical component design process typically starts with conceptual design, where the basic requirements and constraints of the component are defined.
• Detailed Design. Conceptual design is followed by detailed design, where the component is designed to meet the requirements and constraints, considering factors such as materials, manufacturing methods, and cost.
• Analysis and Optimization (CAE). The design then goes through an analysis and optimization stage. Analysis and optimization rely on computer-aided design (CAD) and 3D mechanical engineering simulation (CAE) tools to ensure they will function as intended and meet performance requirements.
• Multi-Objective Optimization. Optimizing an industrial product means considering several objectives (sometimes contrasting) and constraints coming from targets on weight, cost, and size. This is called multi-objective optimization.

We will show how can AI can tackle multi-objective optimization.

AI Use Cases in Mechanical Engineering

Let's take a quick tour of the AI applications produced by Neural Concept's Deep Learning algorithm. In all the shown use cases, AI-driven simulation replaces numerical approximations with neural network predictions. This is possible thanks to the training capability of AI.

Therefore, AI-driven simulation starts from data coming from previous numerical analyses. AI predictions, however, deliver results with a timescale of thousands or even millions of times faster than previous numerical analyses.

Mechanical Engineering Use Cases

Mechanical Engineering Use Case #1: Turbomachinery Explorer

Turbomachines are complex assemblies that need to operate efficiently under a wide range of operating conditions. Simulation-driven design is now an essential tool in this industry.

However, major bottlenecks remain; such as the speed of getting a single simulation result, not to mention a full performance map! This limits the practical implementation. How can a mechanical engineer overcome these limitations and radically change the design process of turbomachines in his/her company? With AI performing quasi-real-time performance maps and designing space exploration, after training with quality data.

The user can navigate on the performance map, evaluating the behaviour of the design on specific operating conditions for different values and views (pressure field, velocity field).

Mechanical Engineering Use Case #2: Heat Exchanger

In this second example, PhysicsX, a UK-based team of scientists and engineers, collaborated with Neural Concept to build an AI predictive model.

The model could predict in real-time the performance of various heat exchanger designs with different topologies. Furthermore, AI's impact extended to optimising the heat exchanger shape.

Other Applications

This last example shows how AI can be applied to challenging situations like boat races. The previous methods were extended to the optimization of a 3D shape.

The project reached two concrete goals:

• coming up with a new foil design idea for the students lightboat
• also, in general, being able to apply this powerful methodology to other hydrodynamics problems

Mechanical Engineering & Artificial Intelligence: Wrap-Up

There are so many potential solutions from the combination of mechanical engineering and artificial intelligence. The most efficient way is to get in touch with our experts and explore your use case(s).

New products are waiting to be designed by you with the help of AI!

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