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Drafting Insights: AI-Powered Engineering & The Future of Design (Subscriber)

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Drafting Insights: AI-Powered Engineering & The Future of Design

Engineering design is evolving.
What was a long and complicated process is now being transformed by AI.

It requires multiple disciplines - 
-    Engineering
-    Project Management
-    Purchasing

New Design Process
-    Data In - AI Analysis - Design Generation - Engineer Review/Validation - Final Decision
AI can streamline design and improve accuracy.

How can AI help bring a product to market?

Requirements management defines, tracks and controls project scope and changes.

Before AI
-    Poor requirements management leads to scope creep, cost overruns and project failure.

After AI
-    Fewer iterations. More insights.

AI can use Large Language Models to auto-generate, refine and summarize requirements into clear electrical specifications, saving engineers time.

Technological developments require engineers to keep up with journals, articles, blogs, forums and social media.
The R&D Life Cycle – Idea Generation – Feasibility Analysis – Prototype Development – Testing – Commercialization 

Selecting the right components is crucial to the design process.

There may be thousands or even millions of components the engineer must consider for a specific socket in a design.

Components must function individually, work together, be available and meet cost targets.
Soon, AI can help engineers with product selection in many ways...

AI trained on datasheets can narrow down and recommend components that meet all parameters, reducing evaluation time.

AI can assist engineers with basic circuit designs, like adding capacitors and resistors.

PCB auto-routing in CAD software lets engineers quickly place components and circuits into a PCB.
However, as PCB complexity grows, auto-routing tools struggle to meet design constraints while controlling cost and manufacturability.
Next-gen AI-based routers use expert input and past experiences to simplify complex PCB layout issues, reducing layers and complexity.

Alternatives often have hidden costs, performance trade-offs, or integration issues while obsolescence disrupts projects.
For example, compatibility issues, possible supply issues or prohibitive cost.

AI in supply chain management improves forecasting with data-driven predictions and recommendations in alternatives.

As electronic products grow more complex and teams shrink, engineers must do more with less time.

A new generation of AI tools helps engineers integrate technology, select better components and improve efficiency.

Although these tools boost efficiency and accuracy, the design engineer is in full control.

The future of engineering design?
Human & AI.