Industrial Modeling in the Age of AI
Industrial modeling is the process of defining digital designs of real-world products, machines, buildings, or systems before they are manufactured or built. It is being used in industries such as manufacturing, automotive, aerospace, construction, robotics, and product design. In simple words, industrial modeling helps engineers and designers "build it digitally first" so that they can test a design, make improvements, and finalize a design without spending money building it in real materials and production. Today, AI is making a big change in industrial modeling by making it faster, smarter and more efficient.
One of the greatest improvements that AI brings is speed and automation. In the past, producing detailed 3D models has been a lot of manual work that takes a lot of time and technical expertise. Now, AI tools can help by generating shapes, offering design options and even converting sketches or images to 3D models. This assists designers to jump from idea to prototype far quicker. AI can also automatically identify any design mistakes such as weak structures, incorrect measurements or parts that will not achieve a proper fit, which saves time and prevents costly mistakes.
AI is also making a change in industrial modeling as generative design. Instead of manually designing a single solution, engineers can specify objectives such as strength, weight, cost, or materials limitations and AI systems will produce various design options that satisfy the requirements. This is particularly helpful for industries where performance is critical, such as aerospace and automotive. Many of these designs created by AI are unusual in comparison to traditional designs, but they often use fewer materials while retaining high strength, which makes them more efficient and innovative.
Another large role of AI is in the areas of simulation and testing. Industrial models are often required to be tested for stress, heat, airflow, vibration, and durability. Traditionally, the time required to run these simulations could be a long one. AI can accelerate the simulation process by predicting simulation outcomes at a faster pace as well as assisting engineers to work on the most critical improvements. This results in faster decision making and improved product development.
AI is also supporting digital twins, which are digital replicas of real machines or systems. A digital twin can be linked to sensors and data from the real world to enable companies to monitor the performance of their equipment, spot issues early on, and anticipate failures before they occur. This enhances maintenance, downtime and safety in industrial environments. With the help of AI, the digital twins become smarter since they can learn from data and make recommendations automatically.
In the manufacturing sector, AI-powered modeling is useful to improve the production planning and quality control. For example, AI can analyze a model and recommend the optimal way to manufacture it, such as optimizing for 3D printing, optimizing for waste reduction, or optimizing for assembly efficiency. It can also help to find defects by comparing real manufactured parts with the original model to ensure higher product quality.
Overall, industrial modeling as it relates to AI is becoming more powerful and accessible. AI is not replacing engineers or designers - it is helping them out with repetitive tasks, making things more accurate and providing smarter design possibilities. This results in quicker product development and better performance, reduced costs and more innovative industrial solutions. In the future, AI will transform industrial modeling yet again by making design and manufacturing more connected, intelligent and efficient than ever before.
S. M. Monowar Kayser
Lecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
