IHPC has been promoting K-AI through multi-party collaborations across departments within IHPC, across A*STAR’s Research Institutes, and external partners. While a majority are in the engineering domain like materials development, advanced manufacturing and aerospace, the K-AI collaborations include other domains such as healthcare in partnership with local clinical institutes.
In the following section, using advanced manufacturing as an example, we illustrate how K-AI can be used for (i) parts design and process optimisation; (ii) engineering process control; and (iii) defect detection post-fabrication.
- Industrial Digital Design and Additive Manufacturing Workflow
Fig 2. Example of components of AM workflow that can be modelled with a K-AI
surrogate model for faster and more cost-effective part design
In order to industrialise additive manufacturing, it is necessary to develop intelligent software tools that allow industry to exploit the high level of control that additive manufacturing processes grant over both geometry and materials while incorporating manufacturability considerations upfront during the design stage (Fig 2). By working with the A*STAR's Institute of Materials Research and Engineering (IMRE) and Advanced Remanufacturing and Technology Centre (ARTC), and other Institute of Higher Learning (IHL) partners like the National University of Singapore (NUS) and Singapore University of Technology and Design (SUTD), physics-based domain knowledge and AI methods are used to construct surrogate models of additive manufacturing processes within a typical additive manufacturing workflow. This is a proof-of-concept demonstration of the possibility for fast and cost-effective design for additive manufacturing processes such as Selective Laser Melting (SLM).
- ML-assisted Control of Shot Peening Process
Fig 3. Model-Predictive Control framework for utilising K-AI models
to enhance engineering process control
Advanced manufacturing processes such as shot peening are highly complex and can be difficult to deploy in the industry. For example, shot peening relies heavily on post processing quantification such as Almen strip testing and saturation curve development which are time consuming and costly. Real-time monitoring and control for advanced manufacturing processes such as shot peening can also reduce the costs associated with characterisation and re-work.
In our collaboration with ARTC and industry partners, a model predictive control framework (Fig 3) was developed to produce a desired output intensity. This was achieved by utilising a physics-based AI model and real-time sensor data to control process parameters optimally. This is a demonstration of the use of physics-based AI models for engineering process control, with potential for expansion to other engineering processes.
- Automated Defect Detection with X-Ray CT Images
Fig 4. Example of utilising K-AI for defect identification which is a common
inverse modelling problem for non-destructive testing (NDT)
Defects in components fabricated by additive manufacturing are a common issue, and these defects can significantly impact the components’ quality and reliability (Fig 4). X-Ray CT scans are a way to detect these defects in the components prior to further processing for reduced cost and improved efficiency. By utilising physics-based simulation models, additional simulated data and parameters can be generated, which will help in building better AI models for the inverse problem of automated defect detection from X-Ray CT images. Further domain information such as symmetry and invariances can be incorporated into such AI models to improve their generalisability.