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Harnessing AI for Product Engineering and Materials Testing in the Drone UAV Industry

Kartik Srinivas Artificial Intelligence, fea abaqus ansys, material testing January 14, 2024January 14, 2024

The world of design engineering and materials testing is being reshaped by the advent of artificial intelligence (AI), particularly large language models (LLMs) like Google Bard or ChatGPT. These AI models are not just about generating text; they hold transformative potential for mechanical and materials engineers, especially in the drone and UAV industry.

AI in Design Engineering

LLMs can analyze, interpret, and even generate complex engineering documentation and instructions. This capability allows design and FEA engineers to automate repetitive tasks, freeing them to focus on innovative problem-solving.

These AI models use Natural Language Processing (NLP) techniques to understand codes, clauses, formulas, and standards. With proper fine-tuning, they can develop accurate relationships between engineering variables and requirements within their neural networks. This allows design engineers to create systems that can automatically perform calculations based on the relevant formulas and clauses required for drone design.

AI as a Design Assistant and FEA Engineer

AI can act as a design assistant, capable of making informed decisions and applying codified standards to repetitive types of work. This significantly enhances productivity and efficiency in design engineering workflows.

AI can rapidly generate numerous potential solutions given a problem statement or a design specification. This broadens the design space, uncovering innovative approaches.

AI can also automate many tedious engineering process tasks. For instance, creating CAD models for structure design and analysis can be time-consuming and require a high level of knowledge. However, design engineers can leverage LLMs to instantly generate a CAD model by inputting fundamental design parameters. The LLM can further refine the design based on additional feedback and input provided by the engineer, streamlining the iterative design process.

Material Testing with AI

AI can also play a significant role in material testing. The vast amounts of data that LLMs are trained on means that they can identify patterns and relationships that are not immediately apparent to human designers. Through this unique capability, AI could suggest a material or configuration that increases efficiency or has superior performance compared to more conventional human-led designs.

By leveraging AI’s ability to uncover hidden insights within complex data sets, design engineers can explore novel design possibilities that push the boundaries of conventional engineering practices. This leads to innovative solutions that may have otherwise been overlooked.

Conclusion

The integration of AI into design engineering and materials testing holds significant potential. From automating tedious tasks to generating innovative solutions, AI can act as a powerful tool for design engineers. As we continue to fine-tune these models and explore their capabilities, we can expect to see even more transformative changes in the field of design engineering.

However, it’s important to remember that while AI can enhance and streamline many aspects of design engineering, it doesn’t replace the need for human oversight, intuition, and expertise. The goal is to create a collaborative environment where AI and humans work together, leveraging their respective strengths to drive innovation and efficiency.

So, whether you’re a mechanical materials engineer or a scientist looking to streamline your workflows or a company seeking to stay at the forefront of technological advancements, now is the time to explore the potential of AI and LLMs in your operations. The future of product engineering is here, and it’s powered by AI. At AdvanSES we have already started allocating resources to this emerging field.

Source:
(1) Application of Artificial Intelligence in Material Testing – ResearchGate. https://www.researchgate.net/publication/361295451_Application_of_Artificial_Intelligence_in_Material_Testing/fulltext/637efc6d2f4bca7fd0883bd8/Application-of-Artificial-Intelligence-in-Material-Testing.pdf.
(2) Artificial intelligence (AI) in textile industry operational …. https://www.emerald.com/insight/content/doi/10.1108/RJTA-04-2021-0046/full/html.
(3) Artificial Intelligence in Materials Modeling and Design. https://link.springer.com/article/10.1007/s11831-020-09506-1.
(4) Artificial intelligence and machine learning in design of mechanical …. https://pubs.rsc.org/en/content/articlelanding/2021/mh/d0mh01451f.
(5) Evolution of artificial intelligence for application in contemporary …. https://link.springer.com/article/10.1557/s43579-023-00433-3.

Artificial Intelligence (AI) Applications in Mechanical Engineering and Materials testing

Kartik Srinivas Artificial Intelligence, fea abaqus ansys, material testing January 14, 2024January 14, 2024

Artificial Intelligence (AI) has found numerous applications in mechanical engineering and materials testing, revolutionizing the field with its ability to analyze vast amounts of data and reveal complex interrelationships. Here are some notable applications:

Machine Vision and Learning: AI, particularly machine vision and machine learning, can significantly improve the technical level of material testing¹. Machine vision inputs the characteristics of the inspected object into the computer, while machine learning enables the computer to better analyze these characteristics and make testing conclusions. This process is characterized by high accuracy and speed, and can be used in all aspects of material testing¹.
Textile Material Testing: AI techniques such as image analysis, back propagation, and neural networking can be specifically used as testing techniques in textile material testing. AI can automate processes in various circumstances.
Materials Modeling and Design: AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials. They reveal how changes in certain principal parameters affect the overall behavior of engineering materials. This can significantly help to improve the design and optimize the properties of future advanced engineering materials.
Mechanical Engineering: AI, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering. It can predict materials properties, design and development of new materials, and discover new mechanisms of material formation and degradation.

These Artificial Intelligence AI applications in mechanical engineering and materials testing not only enhance the efficiency and accuracy of the testing process but also open up new possibilities for material discovery and design. AdvanSES has decided to be on the forefront of this emerging technology and has invested resources into new developments.