Topics discussed include the challenges inherent to a project-based industry like construction and friction points created by a lack of industrialization. Additionally, the execs wonder if we should be paying more attention to the business model of construction itself, rather than any technical limitations, if we want to push the industry to new levels of innovation—and what role AI might have to play.

The participants for this discussion were:

Anand Mecheri, Cofounder & CEO at Invicara, a provider of digital twins for the built environment.

Yves Frinault, Cofounder & CEO at Fieldwire, a provider of field management solutions for construction teams.

Jesse Devitte, Cofounder & General Partner at Building Ventures, a venture capital firm focused on the built environment.

Thiago da Costa, Cofounder & CEO of Toric, a data analytics workspace for construction.

Viraj Voditel, Founder & CEO of Techture, a provider of cloud-based construction management and project management solutions.

Tyler Barnes, President at Tech Soft 3D, a 3D engineering SDK company.

Ron Fritz: Let’s take a look at the AEC space. Where have things changed over the years, and where do things still need to change? What, if anything, do you think is holding the industry back?

Yves Frinault: I think the business model of construction needs to evolve. There is very little incentive from a business model perspective for all the different people on a construction project to collaborate. Sure, everybody could show up 5 months ahead of the project and start working together and start solving problems, but that doesn’t happen. So, I would say the problem that is still holding construction back a little bit is the business model and the incentive between companies. That aspect has changed very, very little over the years.

Thiago da Costa: I think our industry has also been slow to educate the customer about the importance of a SaaS [software as a service] subscription model. When people first learned that they couldn’t own software anymore and that they’d have to pay a monthly fee, there were a lot of complaints. But as a vendor, how do you continually improve the software and make it better if someone pays once and then holds on to that license for the next 10 years? I think that the old business model prevented innovation. We have to find the right balance between doing business profitably and developing the tools that the industry needs because there’s a dire need for technology in the AEC space to continually evolve and improve.

Tyler Barnes: I think that the business model conversation is really interesting. Another thing that surprises me about the AEC industry—probably because I come from a mechanical background—is how much inefficiency there is, particularly in the design-to-build phase. Buildings are conceptually designed manually, then they’re redesigned in some modeler. Data is entered manually; it’s printed manually. It’s very different than the manufacturing space, where you use one application to do conceptual design, and then bring that design into a modeler and build off that design so that there’s no rework. When you think about efficiency frameworks like Six Sigma or lean manufacturing, it feels like that hasn’t quite reached AEC yet.

Viraj Voditel: I agree with what Tyler was just mentioning. We’ve seen that over and over again in this industry, especially when it comes to connected workflows—there are clear disconnects in this process, even in pretty standard use cases. So, it’s surprising that things still aren’t really happening as they should be in the industry.

Jesse Devitte: AEC is a project-based business, not a product-based business—and that’s historically been what has held it back from scaling. It fortunately is becoming more of a product business as construction becomes more industrialized. The entire way the industry works—the duplication and the reentry, from architects and engineers to the builders to the owner operators—is the reason why we mysteriously fail on things that should be so straightforward and logical, like handovers. But there is good news on all these fronts. It’s all evolving.

Ron Fritz: I was expecting a lot of conversation about technical limitations—which are certainly still there—but it seems like market dynamics and business models are very much on peoples’ minds. Anyone care to expand on any of the above—either to agree or disagree?

Anand Mecheri: I was particularly intrigued by Jesse’s comment about handover. It’s hard for anybody to really understand why it’s failing because every party is actually interested in solving that problem. The client wants a good handover. The contractor wants a good handover because it reduces risk. Even though we talk about the whole lifecycle of an asset, there are so many silos at any given stage of that lifecycle and so many different stakeholders—from the project manager, the contractors and subcontractors, the client, the operator—that it quickly becomes mind-bogglingly complex. Every stage along this lifecycle has its own set of problems that we need to find a way to solve because right now the industry is struggling.

Jesse Devitte: For years and years, it seemed like the only entities in the AEC space that cared about having visibility from the beginning of a project all the way through were Disney and the casinos. But now you’re starting to get companies like, say, Meta, who are having a data center built that they plan to use for a long time, and they’re more hands-on. The more you have these enlightened owners demanding—even requiring—visibility and a smooth, connected process, the better. And hopefully, that spills over into owners in other sectors.

Thiago da Costa: Just to add to that, I think that while owners are pushing for things to be better, with more predictability and more transparency, I would have expected by now that owners would be demanding it. I think that’s one reason construction companies have been slow to adopt technology: because no one has been requiring them to. I think if owners were to say that they have to provide these certain things or operate in a certain way, you’d see better adoption, which—by the way—is good for the construction people. Because as you adopt technology, you reduce risk, you increase productivity—you start reaping the benefits of all the things that technology can do. I think that shift from asking for things to be done in a better way to demanding it would just change the game.

Yves Frinault: I agree with all of the above. I think we’ve seen very different owner types, and that results in fundamentally different outcomes technology-wise. For example, companies like healthcare firms really care about getting a specific type of hospital. Companies like Meta know what they want in a data center. Those are the ones that push collaboration to the max and the technology tools and everything that comes with it. But at the end of the day, we’re a late-adopting industry when it comes to technology. I mean, we were like 20 years behind the U.S. Army on adoption of mobile technology.

Viraj Voditel: Adding to what Yves just mentioned about the construction industry being one of the later adopters of most of the tech out there, especially if you look at the 3D side of things: If we look at, say, the gaming industry, they’ve been able to access and interact and view 3D models in a fairly seamless manner for a long time now. The construction industry is still a long way away from being able to interact with 3D models in the way the gaming industry does. That’s because there are still a lot of clunky construction tools that require minutes and minutes of loading time for models. We still need 64 gigs of RAM to be able to open a standard-size model, not even an obscenely large model. There are a lot of struggles around some of these large dataset workflows that have been solved in other industries that regularly deal with large datasets, so it’s surprising that we’re still dealing with this in 2023. It definitely seems like a problem that should have been solved by now, but somehow it isn’t.

Ron Fritz: So, where do you think we’ll be 5 years from now? What sorts of things do you think we’ll see in the construction space? And will AI play a role in any of that?

Yves Frinault: In the near horizon, I think we’ll have figured out the loop of data capture and progress capture on job sites so that there is more robotic automation. I think that’s the next 5 years for construction.

Viraj Voditel: I think we will see some fundamental challenges in the industry being solved by tools like AI. For example, this industry has some of the more difficult challenges around interoperability between different file formats. Being able to convert a particular file format or being able to extract information in automated and intelligent ways would be very beneficial. I think some of those practical applications of AI that solve everyday problems like that could quite possibly happen in the next couple of years.

Anand Mecheri: We’ve reached a point where customers realize that their data is valuable and they can do useful things with it, but they don’t know how to scale up in an efficient manner to take advantage of it. My personal prediction is that AI will play a role in helping these companies to build solutions on a platform where all they have to do is define their requirements, and the platform will provide automation assistance that helps create the applications that will start solving their problems. That’s something that we should be able to actually bet on happening.

Thiago da Costa: I agree. I think the most likely thing to happen is that we will see an attempt at reducing the amount of time that it takes to do things so that companies can become more profitable. If you can paint a wall with a robot, why wouldn’t you paint a wall with a robot? If you can easily do valuable things with your data, why wouldn’t you do that? And so on. I think that attempts at reducing the amount of time it takes to do things will come from multiple angles, but I think what will really change the game for all of these companies is how they work together in collaboration with the owners and the transparency they have with the owners. That will change how they can adopt technology, which goes back to the business model thoughts we had earlier.

Jesse Devitte: I think the industrialization of construction will pick up the pace. This is an industry that evolves—it doesn’t change overnight necessarily, but the pace is picking up. There’s no question that the productization of the process is happening, and I think AI will only help that. I think augmenting the design and engineering process is a really natural way for it to start out.

My hope is that we do reach an inflection point in this industrialization of construction so that more broadly, a fuller range of owners can be involved earlier in the process—and leveraging technology—so that at the end of the day, the outcomes are better. A better built world is what it’s really all about, and I think we’re on the path in that direction. There’s a lot of opportunity ahead.

The post Executive Perspective—The Challenges and Opportunities Facing the AEC Space appeared first on Engineering.com.

The Third Avenue Bridge in Minneapolis is an iconic early 1900s road crossing over the Mississippi River. It is one of 24 bridges of prominent historic and architectural significance that the Minnesota Department of Transportation (MnDOT) has selected for long-term preservation.

At 102 years old, the bridge was showing signs of concrete distress, concrete cracking, and corrosion damage. As such, in 2019 a restoration project was initiated to repair the arches and re-deck the bridge to extend its design life by 50 years while also improving its safety and accessibility features and enhancing its historic and visual features.

The project team used Bridge Information Modeling (BrIM) to ensure successful restoration. BrIM is an application of building information modeling (BIM) used for the design, construction and maintenance of bridges that allows users to interrogate the design of a bridge in 3D to help better understand how different elements integrate. It is particularly useful for translating complex 2D designs into intelligible 3D imagery.

In the case of Third Avenue Bridge, the original construction plans no longer reflected reality. BrIM was instrumental in bringing disparate datasets together that, once combined, filled project data gaps and presented a new perspective on the project’s construction requirements.

One major constraint of the project execution was that almost all the project’s construction works needed to be implemented from the existing superstructure, with careful consideration given to the bridge’s structural integrity and the impact of fluctuating loads as concrete sections were removed and repaired.

The movement and location of construction vehicles, as well as materials arriving and being removed from the site, needed to be carefully sequenced to maintain the bridge’s structural integrity. Using BrIM, the project’s engineers simulated various restoration scenarios to resolve conflicts and arrive at the most effective schedule. Being able to track arch movement in relation to the weather and load on the bridge meant the project team was able to adapt the construction method and remain one step ahead on safety.

Given the project’s complexity, visuals were essential to aid those not familiar with the project’s details. A 3D BrIM model enhanced visualization, which improved step-by-step comprehension of the restoration as the work moved between spans.

The use of BrIM created a single source of truth across the project for the construction’s stakeholders. With stakeholders working from one centralized project source, collaboration and communication were more effective and led to better project outcomes. As a result, there were few instances of rework or schedule delays due to unmitigated risk, which was an important key performance indicator for the asset owner.

BrIM is now widely used by COWI on projects across Europe and India, and has proven to be highly effective at enhancing communication and collaboration and improving project outcomes, with similar results replicated at the Fargo Morehead Bridge in North Dakota and the Houston Ship Channel Bridge in Texas. With hundreds of complex capital infrastructure projects underway in North America each year, the opportunity to benefit from BrIM is huge.

The post Bridging the Mississippi with Bridge Information Modeling appeared first on Engineering.com.

In an increasingly digital world, engineers must embrace digital tools. This four-part video series explores what that means in the context of a highly-anticipated technology that can’t be developed the old-fashioned way: autonomous vehicles.

Watch all four episodes now, available exclusively on engineering.com.

Episode 1: Welcome to the Digital Age of Engineering
If you’re looking to jumpstart digital transformation, you need a boost from MBSE.

Episode 2: My Other Car is a Digital Twin
See how digital twins will unlock the next era of engineering.

Episode 3: The Future of Mobility is Online
The IoT is the crucial link between physical and digital twins.

Episode 4: The Digital Path to a Greener Planet
When time, performance, and cost are optimized, there’s still one parameter left to balance: sustainability.

The post Engineering in the Digital Age appeared first on Engineering.com.

In an increasingly digital world, engineers must embrace digital tools. This series explores what that means in the context of a highly-anticipated technology that can’t be developed the old-fashioned way: autonomous vehicles.

In the final episode of this four-part series, we revisit our entire approach to designing and building a self-driving car—all in the name of sustainability. 

Watch other episodes of Engineering in the Digital Age: 

Episode 1: If you’re looking to jumpstart digital transformation, you need a boost from MBSE.

Episode 2: Digital twins will unlock the next era of engineering.

Episode 3: The IoT is the crucial link between physical and digital twins.

The post Ep.4: The Digital Path to a Greener Planet appeared first on Engineering.com.

In an increasingly digital world, engineers must embrace digital tools. This series explores what that means in the context of a highly-anticipated technology that can’t be developed the old-fashioned way: autonomous vehicles.

In Episode 3 of this four-part series, we take a look a the sensory system of an autonomous vehicle and how it’s enabled by the Internet of Things, the backbone every smart and connected product.

Watch other episodes of Engineering in the Digital Age: 

Episode 1: If you’re looking to jumpstart digital transformation, you need a boost from MBSE.

Episode 2: Digital twins will unlock the next era of engineering.

Episode 4: When time, performance, and cost are optimized, there’s still one parameter left to balance: sustainability.

The post Ep.3: The Future of Mobility is Online appeared first on Engineering.com.

In an increasingly digital world, engineers must embrace digital tools. This series explores what that means in the context of a highly-anticipated technology that can’t be developed the old-fashioned way: autonomous vehicles. 

In Episode 2 of this four-part series, we’ll see how the digital twin isn’t just a neat trick but an indispensable tool for developing autonomous vehicles. 

Watch other episodes of Engineering in the Digital Age: 

Episode 1: If you’re looking to jumpstart digital transformation, you need a boost from MBSE.

Episode 3: The IoT is the crucial link between physical and digital twins.

Episode 4: When time, performance, and cost are optimized, there’s still one parameter left to balance: sustainability.

The post Ep.2: My Other Car is a Digital Twin appeared first on Engineering.com.

In an increasingly digital world, engineers must embrace digital tools. This series explores what that means in the context of a highly-anticipated technology that can’t be developed the old-fashioned way: autonomous vehicles.

In Episode 1 of this four-part series, we explore how model-based systems engineering (MBSE) is the first step to getting self-driving cars on the road. 

Watch other episodes of Engineering in the Digital Age: 

Episode 2: Digital twins will unlock the next era of engineering.

Episode 3: The IoT is the crucial link between physical and digital twins.

Episode 4: When time, performance, and cost are optimized, there’s still one parameter left to balance: sustainability.

The post Ep.1: Welcome to the Digital Age of Engineering appeared first on Engineering.com.

The post Test Submit appeared first on Engineering.com.

Written by: Tosh Tambe, VP of Business Transformation and SaaS Strategy, Siemens Digital Industries Software

(Image courtesy of Siemens Digital Industries Software.)

Complexity is increasing every day because customers expect more smart, connected and integrated products. As product complexity increases, so does the way in which companies design and manufacture them.

Product and production complexity is real, and is becoming an integral piece of how companies operate and collaborate between their departments as well as with customers, suppliers, partners and more.

Because of greater market volatility and the accelerating pace of change and innovation, businesses must often respond quickly to changes in their market conditions and work in real-time conditions. They need to track market data, use the information to sense changes early and take actions to respond quickly. These actions require real-time tracking and might include ramping up or down on activities, shifting resources and making bets.

The next step forward into the future and meeting these challenges is digital transformation and leveraging or utilizing the connectedness of cloud technologies to turn this complexity into a competitive advantage. No more is this realized than with small-to-medium sized businesses that must adapt quickly and have flexible solutions to meet the ever-growing needs of their customers. Large enterprises will benefit from cloud-based solutions to break down silos within the company and become more agile to reduce time-to-market and increase effectiveness.

Distribution and the As-a-Service Model

The pandemic has accelerated the realization of more remote and distributed supply chains, employees and users. Having tools cloud-ready and connected for access and flexibility of use across these distributed sets of users within a company and across a supply chain is imperative.

A SaaS solution provides accessibility, scalability and flexibility to make standardized technology available for these distributed users. The as-a-service model is the natural next step from a business world standpoint, as it provides digital threads as a service.

Connecting domain technologies more seamlessly together to enable these workflows, these digital threads are all about a high-fidelity digital representation of the workflows and processes. SaaS technology tools and cloud capabilities enable and build connectedness, providing fluidity across domains where real value in the insights is used to optimize the product and/or process.

Subscribing to that service is really subscribing to two things: more connected technologies and the community, because that connectedness naturally creates the connective tissue between the users to form a community.

(Image courtesy of Siemens Digital Industries Software.)

On-Premises vs. Cloud

The benefits of a cloud-based architecture versus an on-premises one are monumental. It is easy to state the benefits, but much harder for a company to actually go through the investment of changing the way they design and engineer their products. In an increasingly digital future, investing in cloud computing software will not just be a better option, it will be the best way to compete.

Typically, on-premises architecture features an application-centric world, rather than a data-centric world. Even with a PLM system on a server or an on-premises data center, the reality is that every individual user has application-centric access to the technology. This means, in the best-case scenario, the user has an installed application on their desktop and they fetch data from wherever the data lies, work on that data and then push that data back.

Oftentimes, however, users have an application-centric view with data islands. This unintended isolation causes massive data management headaches because it is locked into locally installed applications where it is brought in and sent out.

On the cloud architecture, users operate in a datacentric environment. The single source of truth is in centrally maintained data. Each user comes in through a browser window and operates with a set of tools approaching the data from a certain perspective.

With cloud, the connectedness is more inherent and natural than on premises, which is more about working in files. Unstructured databases, like those used in cloud computing software, are the end goal. Yet, it doesn’t stop there.

Manufacturing companies can have the data structure and files on a cloud-based system with access management, creating a controlled single source of truth type of data access without fundamentally changing the data architecture. They can have several different domains with different schemas all interacting and working together.

Overall, having the file structured data on the cloud with the right access management simplifies the problem of data management substantially.

(Image courtesy of Siemens Digital Industries Software.)

Openness and Cloud in the Ecosystem

What about sharing data back-and-forth within the supply chain? On-premises data centers might look something like the cloud, but problems arise with access from outside stakeholders because the data is isolated and behind a firewall. A company must contend with how much access to give and how it impacts the design and engineering of the product.

Companies will even design around that problem by basically stripping out all the intellectual property (IP), so the supply chain works with a shell of a design. Still, the main problem is that data needs to move from one on-premises machine to another. The different users might not be in the same trust circles, creating layer-after-layer of burdens that affect the project and turn into costly redesigns.

Through access management on the cloud, the users come and go as needed, IP is managed much better, data is not moving around anywhere and activity can be tracked.

Fluidity Across Functional Departments

The pandemic accelerated the reality of remote workers, especially in terms of evolving work/life balance and the perks that companies offer to their employees to both work more efficiently and as a recruiting tool to attract the best talent.

There is also much more contract labor than ever before, including expert gig workers. The need to support both remote employees as well as a workforce that has an increasingly greater composition of contract-based labor is extremely important.

This means a variety of teams are doing the same function or similar functions around a project presenting a challenge of working on a standard form of data. The transitions of moving from one on-premises application to another on-premises application can be burdensome.

Even if the data access issue is resolved, it still requires manipulation of that data conversion. A product design team and a tooling engineering team working together might use similar design tools, but there will be some confusion. A team of industrial engineers and simulation analysts can complicate the whole process.

(Image courtesy of Siemens Digital Industries Software.)

Building a Community of Workers

There are some communities which are natural within the process of your product lifecycle. With one single team, for example, you have a community naturally collaborating with each other using the same tools and connected technologies.

Take one step back and there are different teams, maybe different functional teams, within the same company. This too is another community, which may have challenges when it comes to collaboration.

Further back is collaboration within a supply chain. How does the entire ecosystem interact in a productive and efficient way?

The answer is enabling easier communication, collaboration and participation in these communities through more connected, collaborative technology, such as cloud.

No organization exists in isolation. They must interact as part of a wider ecosystem. They must interact with their suppliers, distributors, manufacturers and others. This is why an open ecosystem is crucial — it bridges the divides between communities and opens the ability for companies to seek expertise, recognition, help and collaboration opportunities on designs.

What this open ecosystem does is it creates a powerful industrial network effect that is only possible by embracing digital transformation. A SaaS platform and the technology of cloud-based delivery offers incredible, easy and flexible value and benefit in what would otherwise be an almost impossible method of collaboration.

(Image courtesy of Siemens Digital Industries Software.)

Defining the Future

Industries are expanding their possibilities. For instance, the automotive industry isn’t just building cars; rather, they are evolving into mobility companies. They are thinking differently by creating an agile product roadmap and securing core technologies that enable autonomous driving.

Legacy automakers are switching to more robust, flexible, open and integrated solutions. Cloud-enabled technology is proving to cut development time by almost half because their teams can collaborate more efficiently and their suppliers are part of an open ecosystem sharing a single source of truth.

Imagine accessing a secure, software solution that fosters innovation. SaaS is how today’s leading companies are building the products of tomorrow.

Visit Siemens Xcelerator as a Service page to learn more about hybrid cloud, cloud PLM, Xcelerator Cloud and other cloud services.

About the Author:

Tosh Tambe is an experienced Business Leader with a demonstrated history of working in the information technology and services industry. Skilled in leading through technological and organizational transformation with a bias for action and innovation. Experience in building Product and Go-to-Market strategy, leading R&D, Sales/Business development, and Operations teams, and forging partnerships through CXO-level engagement. Tosh has a degree from The Wharton School of Business, University of Pennsylvania.

The post Is SaaS the Future of Digitalization appeared first on Engineering.com.

Written by: Rama Annamraju, Applications Engineer, TrueInsight LLC

Each year, the Indian Institute of Bombay conducts an annual science and technology festival to provide a platform for India’s student community to develop and showcase their technical prowess. I was humbled at the chance to participate in 2009, when I was in my second year/sophomore of undergrad as a Mechanical Engineering student.

The event I took part in was called “Full Throttle: Earth Bound.” The challenge was simple: buy an off-the-shelf Internal Combustion Engine (ICE) RC car and replace the stock steering, chassis, and suspension with custom designed components. My team of five students purchased a Red Cat 3.5cc Nitro Buggy with a 2.4GHz, 2-channel remote and a 2-speed automatic transmission with centrifugal clutch (Image 1).

Image 1. Red Cat Remote Control Car.

The project seemed fun and achievable at the beginning, after all it is just 3 things to do (1) replacing the chassis, (2) changing the steering rack and (3) just swapping the suspension with some cast components. But the engineering gods weren’t going easy on us and things didn’t go as smoothly as expected. Throughout this article I want to recall all the major challenges we went through, and then delve into how I would use today’s technology to tackle these problems with more efficiently.

The 2009 Concept      

For the chassis replacement, we bought a stock aluminum plate and used a CMM (Coordinate Measuring Machine) to measure all dimensions accurately. A rudimentary drawing was generated from the data, shown below in Image 2.

Image 2. Engineering drawings created using CMM measurements.

The first challenge was the lack of exposure to CAD/CAE tools which is evident in our drawing above and the lack of Geometrical tolerancing (GD&T) in it. Recording the readings was the easy part but importing those into a CAD tool was not. We were craving for a proper tool with a flat learning curve to both understand and execute the idea in our minds. Eventually we were saved by the technicians who have some real-world experience and hence we were able to utilize their help and use the CMM data to directly feed into the CNC machine for the cutting and drilling operations. However, bringing it close to a finished part needed some cold working using a hammer and a bench vise. The finished output from the CNC machine is shown below in Image 3.

Image 3. Finished output from CNC machine.

As for the steering mechanism, we replaced the stock adjustable control rod with a homemade spoke, which connected to the servo motor. This was a simple solution, considering the complexity of the other tasks at hand. It proved to be a poor design choice when it came to the loads that the steering rack goes through while cornering. On the race day we had to keep replacing the steering rods often, luckily, they were cheap and easy to re-produce. We considered it a good design back then and in hindsight it was the most effective and cheap component of the entire project.

Redesigning the suspension proved to be the most difficult step. The stock suspension was a double wishbone system, injection molded from nylon, and which contained complex features that would be hard to replicate. It weighed in around 270 grams (9.5 Oz) for all 8 components. Alternate plastics were ruled out, as the tooling would cost as much as the car itself and 3D printing was not an option available to us back in 2009. Therefore, we decided we would use a cheap metal that can be gravity cast. We approached a local foundry to discuss a potential material that could be cast within our budget. Because of its lower density, aluminum was the lightest and most cost-effect option available.

Since there were no major casting simulation tools readily available to us, we had some limitations on how to optimize the design. We decided to switch materials from nylon to aluminum but maintained the existing design with the hope that the selection panel would accept our retrofit. We provided the foundry with the stock suspensions for them to create a mold, and then cast replicas of the suspension in aluminum, as shown in Image 4.

Image 4. Final casted part.

The resultant cast model had many defects, but luckily they were functional. One defect that had to be accounted for was shrinakge. Shrinkage effects caused some of our dimensional tolerances to be out of acceptable range. To resolve the dimensional tolerance issue, we used a file to smooth out as much of the part as possible. The other biggest concern was the weight of the components they each weighed 145 grams (5 OZ) on a average which essentially quadrupled the total weight.

Image 5. Final parts after post processing.

Race Day 2009

On the 2009 race day, a qualifier race determined the starting order for the finals. As soon as the qualifier race started, we noticed our mistake: our car was lagging due to the weight of the casted part.

To counter the weight of the casting, we added some NO₂ to the fuel with the hopes of increasing the power. However, since the problem was weight and not power, the car had low torque which made the car struggle to accelerate evenly. The race was a bust, and we were out in the qualifiers.

Tackling This Problem Today

Thirteen years later in 2022, and with the experience I have gained working as a Design Engineer, all I can think of is how much better we could have performed if we had access to the simulation tools available today. Specialized solutions such as Altair Inspire Cast, which is used to design, simulate, and optimize new/retrofit lightweight components for both performance and castability, would have dramatically accelerated our entire design process and improved our racing competitiveness.

I decided to revisit the old suspension design and experiment with various changes to see how understanding the casting process would change my design. Throughout this process, I used Inspire Cast to create a crude design of the old suspension arm (Image 6) and simulate the casting process.

Inspire Cast includes sketch and geometry modelling tools, which I used to model the part directly instead of using a third-party CAD tool. Inspire Cast also has the capability to import various third-party CAD files, if I wanted to import a CAD model directly.

Image 6. Inspire Cast modeled part ready for casting setup.

After I finished modeling the bracket, I needed to follow the Inspire Cast setup wizard to simulate the casting. Inspire Cast has an easy-to-use workflow that can be broken down into five simple steps:

1. Define the cast part.
2. Define a gate/runner system.
3. Create a virtual mold.
4. Select the casting process.
5. Run the analysis.

Image 7. Inspire Cast model during setup phase.

For the current setup, I chose the Gravity Casting option. The gravity casting parameters were defined by the filling time set at 2.5 seconds, then I hit “Run” to start the analysis, as shown in Image 8.

Image 8. Inspire Cast setup complete and analysis is ready to run.

I ran both solidification and filling analyses to compare the results. Using Inspire Cast, I can see multiple result types and insights into the casting process. Inspire Cast can generate the following results:

However, not all result types are needed to make changes to the design. From my experience in the competition, reducing post-processing steps would be my top priority. I started by looking at the temperature changes during the solidification process, since that is typically an indicator for shrinkage.

Image 9. Material temperature changes during solidification.

This plot correlates well with the total volume shrinkage plot below. Shrinkage not only requires additional post processing, but in most cases it can also affect the mass of the part.

Image 10. Total Volume Shrinkage.

Next, I wanted to look at the how the temperature dissipated during the filling phase. With this result, you can determine the temperature required for two fronts of material to fuse and study the risk of cold welding. It is important to identify cold welding, as this can cause the part to have structural weaknesses.

Image 11. Temperature distribution while filling.

Lastly, I wanted to look at which spots would be the last areas to fill, in order to predict where bubbles may form. Knowing where you may get bubbles enables you to reposition the overflows to prevent porosity. Air bubbles will impact die casting more than sand molds, as they are less susceptible to porosity thanks to the permeability of sand.

Image 12. Plot showing the last areas to fill.

While I could have completely redesigned and optimized this chassis component for performance, weight and castability using Inspire Cast, our biggest design challenge at the time was simply understanding and accounting for shrinkage defects. Inspire Cast would have provided this needed insight allowing our team more time to experiment with alternative materials and design changes that could have improved our chances to win.

Suspension Design 2.0

With this knowledge from the casting simulation, I wanted to see if I can make some subtle changes to the design and still be able to get the same results in terms of the strength and stiffness all the while reducing the weight if the components. I redesigned the suspension to add thinner walls as shown in Image 13.

Image 13. New design with thinner walls.

Altair’s Inspire Cast allows me to check the weight of the components right in modeling phase, my new components were optimized to weigh just 67 grams (2.4 Oz) each which is more than a 50 percent mass reduction. I can now export the model to Altair Inspire and analyze it for stiffness and strength. See Image 14.

Image 14. Showing the von Mises Stress in the component

From an automotive engineering perspective, reduced weight is a big advantage to reduce energy consumption. In addition to that, the ability to maintain required stiffness and strength to withstand the forces during operation is the sign of a successful redesign.

Looking back at our initial race failure, we knew that our suspension design was too heavy which led to poor control of the RC car. We also spent a lot of time postprocessing our casted suspension parts to remove defects. With the Altair simulation tools, we could have significantly lightened the suspension design (more than 50%) to give us more control and understanding potential casting defects in order to reduce our postprocessing time, would allow us to focus more on design and innovation than quality assurance.

The Future of Casting Design

The experience I gained during the competition was extremely valuable in helping me realize how to understand and solve a problem as a team.  Upon revisiting this problem years later as an engineering professional, it’s amazing just how powerful and pervasive simulation has become across the entire development cycle.  If our team would have had access to a product like Inspire Cast to explore and address the Design for Manufacture (DfM) for this casted part, I feel it would have been game-changing.

Today, I would approach this competition completely differently leveraging a simulation-driven design strategy with Altair Inspire together with Altair Inspire Cast.  This approach would incorporate and streamline loads definition, topology optimization (otherwise known as generative design) for lightweighting, structural analysis to meet performance objectives, manufacturability for a variety of different casting processes, and the validation of die designs and processes all prior to producing a physical prototype and tooling.

Perhaps I will do just that and hopefully have the opportunity to share the outcome in a follow up article!

To learn more, visit Altair.

Acknowledgments

1. I participated in the competition with a team of five.
2. The model car was manufactured by RedCat Racing.
3. The price of the RC Car in 2009 was INR: 18,000 which converts to about $360 (USD in 2009) or about $490 (USD) today.
4. The budget for the modifications was close to INR: 7,000 which converts to about $140 (USD in 2009) or about $190 (USD) today.

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