Ever since I started exploring the foundations of Artificial Intelligence – thanks to the pandemic outbreak – I have found it super exciting to relate my past engineering experience with the AI capabilities to visualize some possible applications of AI, especially Machine Learning in the engineering design and documentation fields. A few thoughts are discussed below around this topic. Chances are that many of the applications are already in action at the moment. But hopefully you may come across something very new in this article as well.
Use of Generative Design for Developing Complex Structures
Generative Design has already exhibited its potential by solving some complex design challenges in the Aeronautics and Automobile industries. It looks highly beneficial to use this AI based design process for developing optimal solutions for highly complex structures such as offshore platforms and bridges.
Document Type Identification and Categorization
Most of you working in the engineering domain might have come across this requirement multiple times in your career. For example, document controllers perform this categorization very often for enabling proper document distribution. For some cases like brownfield engineering modification projects or proposals, the customer might have thrown a bunch of existing documents at you without proper categorization or indexing. You end up putting in a lot of manual effort for proper identification of documents before starting your actual engineering/proposal works. With the help of Machine Learning, we could automate this process and save valuable time and effort. This method would be highly advantageous for design/construction firms repeatedly offering services to Energy/Resources/Infrastructure customers.
Document Content Extraction from Vector/Hybrid/PDF Drawings
This is another headache that engineering and design professionals come across in their day to day life. While it is possible to get accurate information (for example, engineering schedules, materials take-off, Document index etc.) from intelligent design systems, it requires a lot of effort to get this done in non-intelligent design system based projects. Moreover, the quality of existing documents such as vector drawings with exploded attributes, hybrid drawings/documents which are a combination of vector and image data and PDF only documents leave the design people completely helpless in preparing accurate reports/schedules. This can be easily overcome by using Machine Learning technique that can identify information in any format of the document with same level of accuracy.
Data Input for Digital Twins from Dumb Documents
This is again related to the document content identification technique using Machine Learning as discussed above. Once we are able to identify the documents content and map interrelations between these documents using identified data, then it would be a lot easier to feed this data to Digital Twins (for example, by automatically mapping similar tags at both ends) for real-time monitoring.
Automated Pipe Routing and Plant Design Optimization
I have seen some intelligent 3D design systems in action doing automated pipe routing, but not sure up to what extent these systems offer multiple options for the designer to choose from. Using AI, it is highly probable to present the designer with many design options based on the design constraints and factors. I believe in future many plant design activities such as equipment locations, orientation, pipe routing and many other tasks would be mostly handled by AI.
Real-time Designing and Visualization for Model Reviews
Okay, your customer doesn’t want that design option you opted to exhibit. So what are the other options? Using AI, it looks highly probable to present multiple design suggestions in a real-time basis. This would also help the customer and design consultant/contractor to understand the material take-off (MTO) variations in real-time thus providing an idea the cost differences on the spot for the purpose of feasibility discussions.
Replacing Rule Based Standard Compliance Checking with AI Based Ones
How often have you seen your drawings/documents rejected by your customer for not adhering to their design/drafting standards? Quite often, I would say. The biggest challenge here is that it is really hard to develop such a rule based standard checking utility for both the customer and the design consultants. Instead, we could use Machine Learning for preparing algorithms that learn the customer standards from thousands of existing customer drawings. This method would also make it easy to implement standard revisions without much headache by employing continuous learning.
These are only glimpses of AI applications in the engineering design and documentation fields. I believe there are many more applications yet to be explored. Please expect more blog posts on various other applications in the future as and when some ideas pop up. Meanwhile, please stay in touch by subscribing the RSS / Email feed subscriptions of this blog using the right side menu widgets.