Identify the engineering task
Determine what needs to be predicted, classified, optimized, automated or understood.
MATAPPLY / CASE STUDY 04
Engineering AI Workflow.
An illustrative engineering AI workflow showing how domain knowledge, engineering data and machine learning can be connected to support technical work.
THE AI QUESTION
Where can AI augment engineering intelligence?
Engineering AI is most useful when it works with engineering knowledge rather than treating the engineering problem as a generic data problem. The opportunity is to connect domain knowledge, data, algorithms and engineering decisions.
MATAPPLY APPROACH
Build the intelligence around the engineering problem.
Connect engineering data
Identify useful relationships between geometry, materials, simulation results, measurements and other engineering variables.
Apply AI appropriately
Select an appropriate modelling approach based on the engineering objective, available data and required level of confidence.
Return engineering value
Integrate the model into a workflow where its output can support engineering analysis, exploration or decision-making.
MATAPPLY PRINCIPLE
Engineering AI should augment engineering thinking.
The objective is not to replace engineering judgement. It is to make engineering knowledge more searchable, computational, predictive and useful within the decision process.
MATAPPLY AI
Connect the problem. Find the intelligence.
Explore how MATAPPLY AI approaches complex problems across Technology, Product, Market and Business.
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