In-depth analysis tagged CAD/Design — covering PLM history, vendor strategy, and the technical decisions reshaping engineering software.
3 articles
AI design tools are compressing engineering timelines not by replacing CAD, but by accelerating the expensive iteration loops between design, simulation, and manufacturing validation. Here's how neural surrogates, topology optimization, and AI-augmented parametric modeling combine into a compound workflow.
Text AI had transformers. Image AI had convolutional networks. Geometry AI is still building its foundational models — and the companies doing it are unlocking additive manufacturing, structural optimization, and design workflows that B-rep CAD cannot reach.
5 signals from 22 startup interviews: design intelligence is shifting toward manufacturable outcomes, vertical solutions, and production-ready workflows.
An AI copilot in engineering is an AI assistant that integrates into CAD, PLM, or ERP workflows to augment engineer productivity. Copilots enable natural language queries against product data, automated generation of design alternatives, anomaly detection in simulation results, and intelligent search across large PLM datasets. Unlike autonomous agents, copilots present suggestions that engineers approve or reject — the human remains in the decision loop.
Boundary representation (B-rep) is the dominant method for representing solid geometry in CAD kernels. A B-rep model defines a solid by its bounding surfaces — vertices, edges, and faces — with topological relationships between them. B-rep enables precise, manufacturable 3D models that can be evaluated for mass properties, interference, and manufacturing operations, making it the standard for engineering-grade CAD.
AI applied to the design phase of product development, focused on producing manufacturable designs. Design intelligence includes conceptualization, validation, optimization, and manufacturability checking.
Design methodology that integrates manufacturing constraints and capabilities into the design process, rather than treating manufacturability as a downstream check. DfM-ready designs reduce production costs, cycle time, and quality issues.
Automated Design-for-Manufacturing screening: AI-assisted analysis of a 3D design to identify features that are difficult, expensive, or impossible to manufacture with specified processes — before the design is released to production.
