Design Optimization for Advanced Manufacturing through Forward Looking Performance Simulation
Interview with Chris Robinson of ANSYS
Chris Robinson, Senior Product Manager at Ansys, has spent over 20 years advancing additive manufacturing—from satellite components to flight-qualified parts.
At CDFAM Amsterdam, his focus goes far beyond AM, as in this interview, he discusses how forward-looking, physics-based simulation can inform early-stage design across a range of manufacturing methods—not just additive.
From structural optimization and heat transfer to vibroacoustics and RF performance, he explains how embedding simulation earlier in the design process helps reduce rework, improve performance, and make complex products more manufacturable.
We also explore the role of APIs, AI agents, and the cultural shift needed to make simulation actionable for engineering teams.
Can you start by telling us what will you plan to present at CDFAM Amsterdam, and how does ‘forward-looking simulation’ change the way we approach design for advanced manufacturing?
I will be presenting some examples of how industry can use simulation in the early stages of design to drive optimization of a geometry.
This design can go far beyond just trying to find the right geometry to reduce weight and maintain stiffness of a structural bracket. There are many different simulation capabilities that can be leveraged to truly bring functional optimization into the early stages of design.
By using simulation early on in the design phase, users eliminate a lot of re-work that would traditionally be required if a design was adapted to optimization.
Since rework is often too expensive in regards to non-recurring engineering (NRE) costs, industry often settles for components that are far less than optimal. One solution is to bring that optimization forward, so it is initial work instead of re-work and that can cut out significant NRE costs.
Topology optimization for structures and heat exchangers is now fairly well established. What are some of the newer or lesser used simulation types that you think have untapped potential in design workflows?
Although topology optimization (TO) and heat exchangers are well established, there is still a lot of room to optimize components through these topic areas. One are that we have been making great progress with some users is in the area of doing lattice or topology optimization for LPBF support structures that improve the manufacturability of high value components. There is still a lot of white space, even for these more well established techniques.
However, what is really exciting when we start to look into the idea of applying other simulation methods to drive the design of Advanced Manufacturing components.
For instance, we can use a sound simulation to optimize a panel topography based on end use requirements. Users can design optimal RF components by leveraging RF simulation early on in the design phase.
These processes can be combined with traditional manufacturing techniques such as sheet metal stamping or more evolving techniques such as Additive Manufacturing to produce optimal parts.
The key to really finding the ideal manufacturing method of optimized geometries is enabled through early phase simulation of the product and the process of producing the product.
How does the integration of more complex performance simulations—like vibroacoustics, RF response, or multi-phase flow impact the tools or mindset needed for early-stage design?
Without a doubt, the mindset of early stage design has to shift from simply focusing on minimal level conformity for function that fits available form to a mindset detailed mindset from the beginning for performance optimization is a part of the early stage of design.
Many simulations require some starting geometry that must come from basic ideas and forms, so that is still important, but I think that the shift has to be to considering that as simply a way to start the optimization.
By bringing optimization into the early stages of design it expands the skillset for early stage designers to include basic CAE capabilities, but then when a component goes to the analyst team for final validation, there is already actually a lot of information available and in many cases the design changes and loops between design and analysis will be greatly reduced in the later stages of the design process.
What challenges do you see in making advanced simulation results actionable for designers and engineers, particularly when moving from idealized conditions to manufacturing-ready geometry?
The biggest challenges that I see to making advanced simulation results actionable, is to create tools that are both accurate and easy to use enough that early stage users can benefit from them.
Traditionally, tools that were used for early stage designers would be fast and easy to set up, but the quality of results was so low, that they were simply useful for getting a rough idea. Tools that were accurate enough to get actionable information were often difficult to use and often times the simulations take longer to run.
Allowing users to be able to have a process that works well for them and their companies with highly advanced solvers is something that is important.
Solutions like APIs that can be connected through either simple coding language, or components or AI agents can help users to have easy to use tools that leverage the best in class solvers.
This needs to not only focus on performance of the product, but also on the process used to create the product. If both product and process are considered from the beginning of the product development cycle, then products can be optimized.
I also think that even though there is more hype around AI than what is sustainable long term, there are amazing capabilities enabled through capturing information and using that information to train capabilities that can adapt to the needs of a specific user group, which will help to truly drive optimization from an automatic standpoint as well being able to leverage these types of agents to help interpret results with actionable options.
You’ve worked across the full AM ecosystem, from materials to software to qualification. What do you see as the biggest barriers that still exist when connecting simulation-driven designs with real-world production?
When I left the hardware and materials side of the AM ecosystem to help drive the adoption and development of simulation for the AM process, I saw that having the right information to make informed decisions was a huge barrier.
We were not able to drive qualification from data in many cases, because the industry didn’t know what data to capture or how to capture it most efficiently. This leads to enormous costs and a lot of wasted data. I am confident that with high fidelity predictive simulations for products and processes, then we still can drastically reduce the need for costly empirical data gathering.
We need to have a mindset shift of thinking about ways that we can reduce the empirical workload through gaining insights from predictive simulation.
Tools are available to reduce workload drastically, but there are still many companies that have a mindset that reducing workload is not sufficient unless it completely eliminates workload.
We have seen some companies that will leverage simulation to take 3 months of work and cut it down to 3 weeks or even 1 month to optimize processing parameters for new materials.
There is still some work that needs to be done, even outside of simulation, but users that adopt this type of mentality pay for their software within weeks and have opened up their teams and resources to amazing new opportunities through reduction of much of the time they are usually losing. They can get to market faster with better parts.
I think that of course there is still a lot of work to do on the detailed analysis side of things, but compared to 9 or 10 years ago, the capabilities today are amazing if they are used for what they excel at.
What are you looking forward to at CDFAM Amsterdam, and how does an event like this support your work at the intersection of simulation and design optimization?
I feel like simulation is one of the keys to unlocking the future potential of design optimization, which enables us to better support life improving opportunities in advanced manufacturing.
This truly requires an industry effort and it will not be achieved by any one individual, company, or group alone.
CDFAM will be an opportunity for others who also believe that advanced manufacturing can be optimized through computational capabilities.
This will be an opportunity for me to bring what Ansys has to offer at the intersection of simulation and design optimization to other current and potential partners and customers to help to build the ecosystem that will truly make a difference in the world in our generation and in generations to come.
To hear more from Chris Robinson and others driving simulation-informed design across disciplines, join us at CDFAM Amsterdam 2025. It’s where engineers, software developers, and designers come together to share real methods—not just concepts—for integrating simulation into advanced manufacturing workflows.
We recently profiled many of these efforts in our article on Simulation-Driven Design, Engineering and Architecture at CDFAM, and Chris’s presentation will extend that conversation with fresh insights and practical strategies.
Register now to connect with Chris and a community committed to making performance-driven design a reality.
