How can you automate mechanical design with AI?

I think this is absolutely wonderful. Imagine taking a 3D scan of an object and let AI generate the most efficient housings for that object.

Automating mechanical design with AI involves leveraging machine learning algorithms and computational techniques to streamline various aspects of the design process i.e. Generative Design, Parametric Design Optimization, Topology Optimization, Design Assistance and Recommendation Systems, Simulation and Analysis, Automatic CAD Modeling, Predictive Maintenance and Lifecycle Management.

There does have to be human intuition involved first, and even when it seems it isn’t needed, it has to be there in case it is needed. However, if a human can see a mechanical problem from an artistic view, I see no reason why AI would not be able to do the same.

AI will be able to generate CAD designs from well defined input of requirement. later AI can even split and make a BOM with real time costing .

AI could be a reliable co-pilot in the design phase, providing SOPs, optimizing designs, and identifying potential errors before manufacturing. I think there is significant potential for design engineers to think out of the box by using AI as a co-pilot.

AI can provide automated feedback on designs, analyzing elements such as composition, color scheme, typography, and more. This feedback can help designers understand strengths and weaknesses in their work. AI algorithms can predict the effectiveness of a design based on past performance data or user preferences. For example, in marketing, AI can predict how well a particular advertisement design will perform based on historical data. AI-powered tools can conduct usability testing on designs by simulating user interactions and providing insights into user experience. This helps identify areas where the design may need improvement for better usability.

AI can evaluate mechanical design in various ways, providing insights into performance, efficiency, and reliability. AI can play an important role in different aspects i.e. Performance Simulation and Analysis, Generative Design Evaluation, Generative Design Evaluation, Failure Mode Analysis, Failure Mode Analysis, Human-Machine Collaboration, and Feedback Loop for Design Improvement.

As we know, the design process is continuous and interconnected, which means that a design matures over time. Along this path, we optimize the initial design based on previous experiences. However, this process is time-consuming and expensive. Artificial intelligence can greatly assist us in expediting this journey. For instance, with abundant patterns and data, it helps us achieve our goals more efficiently, even though the algorithms developed for artificial intelligence also undergo optimization over time.

we can achieve more productivity if we use in the below given strategies 1. Design Automation 2. Generative Design 3. Predictive Analysis 4. Reduced Time to Market 5. Cost Reduction 6. Improved Performance and Reliability 7. Customization and Personalization 8. Collaborative Design 9. Sustainability

The main challenges in every AI projects are to get a well-structured detailed data with more accurate features. These are the main challenges that needs to be in consideration : 1. Reframing the required historical data as per the need of ML Engineer 2. Availability of computational resources to train the AI model 3. Creating awareness on ethical considerations to the AI team about the design data 4. Delivery the required data with more quality to the AI engineers 5.Mechanical design often involves a combination of engineering principles, material science, physics, and mathematics. Developing AI models that effectively incorporate this interdisciplinary knowledge requires collaboration between experts from different domains.

While AI offers significant benefits for automating and improving mechanical design processes, several challenges must be addressed. Some of the key challenges include Data Quality and Availability, the Interdisciplinary Nature of Mechanical Design, Complexity of Design Requirements, Explainability and Interpretability, Lack of Standardization in Design Processes, and Regulatory Compliance.

Its really very Important to start ahead towards the journey of AI/ML for each and every mechanical engineers. All we just need to do is revisit the mathematics what ever we have came across in our undergrad studies which is an important core of AI/ML and having a basic knowledge of programming skill using python. After all these pre-requisite we can have a very good start towards the intersection of AI in Mechanical design.