Manufacturing Driven Design
Interview with Rhushik Matroja – Cognitive Design Systems
Rhushik Matroja returns to CDFAM Amsterdam with a major update on the evolution of Cognitive Design Systems.
What began as a standalone generative design tool focused primarily on part optimisation and pricing analysis for additive manufacturing, has evolved into a concurrent engineering platform, bringing together simulation, optimization, and custom manufacturing intelligence into a unified workflow agnostic of manufacturing process.
In this interview, Rhushik explains how Cognitive Design is helping engineers simultaneously optimise for performance and manufacturability across multiple processes, from additive manufacturing and die casting, to machining and injection molding, using implicit modeling and real-time simulation data.
You’ve presented at CDFAM a couple of times before (welcome back!), can you start by giving us an update on what’s new at Cognitive Design Systems since your last presentation in Berlin?
Absolutely, and thank you — it’s great to be back at CDFAM.
Since our last presentation in Berlin, Cognitive Design Systems has undergone a significant evolution. We’ve embraced a new identity, not just a rebrand, but a transformation into a next-generation concurrent engineering platform company.
Our focus has sharpened around enabling Design Exploration through Multi-Disciplinary Optimization, where we help engineers find the right balance between performance, manufacturability, cost, and carbon footprint.
This is powered by our proprietary generative design algorithms and a deeper integration of Manufacturing-Driven Design.
What’s new is our expanded support for traditional manufacturing processes. In addition to Additive Manufacturing, we now support Die Casting, Machining, and Injection Molding, allowing designers to explore and generate manufacturable geometries from day one, tailored to their preferred production methods.
Overall, Cognitive Design is evolving from a tool into a concurrent design platform, helping teams move faster, make better decisions earlier, and bring innovation to market with less friction.
The platform integrates Manufacturing-Driven, and Simulation-Driven Design into one workflow. How does bringing these approaches together change the way engineers and designers can work?
Performance and manufacturability are two fundamental pillars of product design and both are equally critical to get right.
By combining Manufacturing-Driven Design and Simulation-Driven Design into one unified workflow, Cognitive Design ensures that engineers can explore and iterate on designs that meet both criteria simultaneously without waiting for separate downstream validation.
Our implicit modeling kernel plays a key role in this integration. It allows users to modify geometry locally and intelligently, using data fields directly from FEA or manufacturing analysis.
Whether you’re optimizing for stiffness, heat flow, or tooling constraints, these insights can be applied in real time to drive design decisions.
This approach enables a truly concurrent workflow where performance and manufacturability are not in conflict, but co-optimized.
The result: faster design cycles, fewer iterations, and high-quality parts that are ready for production from day one.
Your software enables real-time manufacturability checks across processes like AM, machining, casting, and molding. How do users customize or tweak the rulesets to reflect their own manufacturing data, constraints, or process knowledge?
Great question. Yes, customization is a core part of how Cognitive Design works.
For each manufacturing process whether it’s Additive Manufacturing, Machining, Casting, or Injection Molding, Cognitive Design provides a set of configurable design rules.
These include constraints like minimum wall thickness, draft angles, support requirements, and more. Users can easily customize these rulesets to reflect their in-house standards, machine capabilities, or supplier requirements.
Beyond static rules, the platform also allows users to import results from process simulation software (e.g., distortion, thermal gradients, residual stress) and use that data to dynamically drive geometry modifications. This is made possible by our implicit modeling engine, which can respond to field data and apply localized design changes.
We are currently collaborating with multiple process simulation software vendors to build direct bridges, making it easier for users to bring in their manufacturing intelligence and close the loop between design and production.
For someone starting a new design project, how early should your software, and these design principles be applied, and what kinds of gains can they expect compared to traditional design-then-validate workflows?
To maximize impact, Cognitive Design should be used right from the earliest stages of a new design project.
By applying performance and manufacturability constraints upfront rather than validating them after the fact engineers can avoid costly iterations and accelerate development dramatically. Our platform is built for this design-first, validation-along-the-way approach.
In fact, we’ve seen productivity gains of up to 10x compared to traditional design-then-validate workflows.
One example is a recent case study on a gearbox housing, where complex geometry was generated automatically based on multiple performance and manufacturing constraints. What would normally take weeks was achieved in just a few hours with manufacturing feasibility built in from the start.
This shift allows engineering teams to move faster, reduce late-stage rework, and bring high-quality, production-ready designs to market much earlier.
What are some of the most surprising applications or industries where you’ve seen the biggest impact from using Cognitive Design so far?
One of the most surprising and impactful applications of Cognitive Design has been in the space industry.
We worked with a major French satellite manufacturer that designs around 80 variants of the same satellite bracket every year. While the changes between variants are minor, traditionally, each one had to be redesigned manually, a time-consuming and repetitive task for the engineering team.
With Cognitive Design, we created a single parametric and reusable workflow that automatically generates all 80 bracket variants based on configurable inputs.
What used to take weeks of manual CAD work is now done in hours — with full control over both performance requirements and manufacturability constraints.
This kind of mass customization at scale showcases the real power of Cognitive Design: enabling teams to automate repetitive engineering work, reduce lead times, and focus their efforts on innovation rather than rework.
As you return to CDFAM Amsterdam, what are you looking forward to sharing, and what kinds of conversations or collaborations are you hoping to build this year?
Returning to CDFAM Amsterdam is exciting for us, it’s the perfect place to share how far Cognitive Design has come and to spark new collaborations.
This year, we’re looking forward to showcasing the latest evolution of our platform as a next-generation concurrent engineering environment, one that brings together generative design, simulation, and manufacturability into a unified, intelligent workflow.
We’ll be highlighting new capabilities like multi-process manufacturability (AM, casting, machining, molding) and our implicit modeling engine that enables real-time geometry adaptation from simulation and manufacturing data.
But beyond the tech, we’re here to have meaningful conversations with design engineers, researchers, OEMs, and software partners about how we can accelerate product development, reduce iteration loops, and rethink the role of the engineer in an AI-assisted future.
We’re especially keen to connect with teams looking to move from traditional CAD to automated, data-driven design workflows, and with anyone working on real-world challenges in space, aerospace, automotive, or high-performance components.
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If you’re leading innovation in product development or manufacturing, and exploring how AI and ML can streamline engineering workflows, reduce iteration cycles, and improve outcomes at scale, CDFAM Amsterdam offers direct access to the experts driving this transformation.
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