Autodesk Fusion Steers Successful SIM Racing Product Development 

Clinton Perry April 21, 2024

22 min read

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Elevate your design and manufacturing processes with Autodesk Fusion

See how this F1-inspired steering wheel designed and manufactured using Autodesk Fusion showcases the software’s breadth of capabilities. 

The secret to successfully developing world-class consumer products lies, in part, in having access to professional-grade design & manufacturing software that supports the entire product development lifecycle. From initial concept design to mechanical and electrical engineering, to product simulation and validation, all the way through to production and beyond; it is essential that modern software provides the collaborative tools needed to bring those products to market. In this article, we chart the story of a team of passionate designers and engineers who decided to put Autodesk Fusion through its paces.  

It started with a conversation between two friends one evening over a cold beer. “How cool would it be to test Fusion out by designing and making our very own steering wheel?”. This innocent question, from Autodesk technical sales specialist – and project lead – Christopher Derdak, started a project that would see an international team of specialist designers and engineers work with a global network of OEMs to design and make a complete F1-inspired SIM racing steering wheel. 

The F1-inspired steering wheel was designed and produced using Autodesk Fusion software.

Every race begins with the first step

The project began with a simple concept sketch of the shape of the product. Derdak took inspiration from his own experiences in the SIM driving community and was inspired by many of the modern F1 steering wheels. Using Fusion’s intuitive interface, Derdak quickly converted the concept into 2D wireframe profiles to represent the size and shape he had in mind. 

Simple wireframe sketches are defined to represent the shape of a grab handle in Fusion. 

Having created the initial 2D sketches, Derdak focused on producing the main component parts using the full suite of solid and surface modeling tools contained within Fusion’s Design workspace. Using intuitive free-form modeling, Derdak was able to combine prismatic shapes with organic forms, until the desired 3D aesthetic had been achieved. 

Free-form modeling is used to dynamically adjust a solid body to match the wireframe sketch.

Once the outer aesthetic components were complete, Derdak focused on the inner mechanical parts, including switch gear, shifter paddles, quick release mechanism, as well as all of the fixings that would be required to assemble the finished wheel.

Fusion’s “Design Workspace” provides intuitive tools for wireframe, solid, and surface modeling.

As you might expect, these individual component parts weren’t designed in isolation. Instead, Derdak used Fusion’s cloud-based data model to securely share his project with his extended team of designers and engineers.  

This collaborative approach was maintained throughout the life of the project and ensured the team was always working on a single, unified design – not separate, disconnected models stored in individual folders on local computers. From the very beginning, the team knew that, if the industrial designer made a change – which happened quite often in the early development stages – the changes would be instantly available for everyone else to review and consume. This allowed the team to adapt and pivot quickly and easily.

“During the final design stages, we had to do a lot of fine tuning and change positions of internal parts to make sure they fit. With Fusion we could easily share these updates within our cloud shared project with the rest of the team.”

—Christopher Derdak, Technical Specialist, Autodesk 

Mechanical meets electrical engineering

Having designed the main component parts, Derdak engaged the services of Autodesk electronics expert, Richard Hammerl to begin the process of designing the printed circuit boards (PCB), switches, and other components that would allow the steering wheel to function.  

Unlike legacy software solutions, where data is often exported from one CAD design software tool to another, Derdak simply invited Hammerl to participate in his Fusion project. This removed the need to export files and meant no loss of data due to translation issues. Instead, Hammerl simply used his unique invitation to securely access Derdak’s project; at which point the PCB design could begin.

Fusion allows the shape of a PCB to be derived from selected 3D mechanical parts, saving time and effort.

Due to the limited space in the steering wheel, Hammerl opted to design four individual circuit boards. Each of the PCBs has an individual, unusual contour. The PCB design process really begins with the selection of a 3D component from the main steering wheel assembly. In just 3 mouse clicks, Fusion derives a blank PCB board from the selected component, updates the profile to include any drill holes or cut-outs, and then transfers the shape to the 2D workspace. This task was completed in just a few minutes, something that would have taken at least two days in other, less capable systems. At this point, Hammerl turned his attention to building the circuit. This was achieved using Fusion’s cloud-based library of standard electrical parts to select and position key components on the board to form the desired circuit.

The schematic and 2D layout are linked, meaning any changes made to one, are automatically applied to the other.

In addition to creating a 2D view of the PCB – useful for showing the actual size, shape, and position of the individual element on the PCB – Fusion also provides a 2D schematic layout of the circuit. This is especially useful as it allows the engineer to focus on the connections with a circuit without being distracted by the shape of the individual parts.  

Dynamic 2D schematic views make it easy to work on the circuit.

As well as creating simple 2D views, Fusion can also create a 3D model of the PCB with a single mouse click. Crucially, as the library of component parts is both 2D and 3D, each time an encoder, switch, or LED was assembled into the schematic – or moved to a different position – all views, whether 2D or 3D PCB, will automatically update. Making a change in one view will be reflected in the others. This speeds up the development process and allows the designer to check for potential collisions or interference with other mechanical components; something that can be potentially disastrous (and expensive) if left until final part assembly.

2D PCB designs can be instantly converted into fully detailed 3D assemblies and used for physical interference checks.

The associativity within Fusion isn’t limited to the connection between 2D and 3D views of the PCB. In fact, the associativity stretches across the entire design of the full product. This proved especially useful in the early stages of the design where Derdak was experimenting with the position of fixing holes to accommodate a larger grab handle – and Hammerl had to ensure the PCB design kept in sync.  

As both industrial and electronics designers are working on the same dataset, the modified grab handle design was instantly made available for the electronics engineer to carry out additional checks with the PCB board. If interference occurs, individual components (or indeed the entire PCB profile) can be updated with minimum effort – with all 2D and 3D views being updated in minutes.  

“Due to the limited space in the steering wheel, we opted for four individual circuit boards. Each of the PCBs has an individual, unusual contour. Thanks to the option of creating a PCB in 3D from a mechanical sketch or a housing, including the drill holes and cut-outs, with three mouse clicks and then transferring it to my 2D workspace, this task was completed in just a few minutes. If I think back to my old layout tool, this would have taken at least two days.”

—Richard Hammerl, Technical Specialist, Autodesk 

Once the design of the PCBs was finalized, the datasets and associated 2D drawings were shared with German-based electronics manufacturer, Eurocircuits, who were responsible for a small production run. 

Generative design assists chassis design

To reduce the overall product development time, it is commonplace for multiple designers and engineers to work concurrently; this was certainly the case on this project. As Hammerl was busy working on the PCB designs, another team worked on other mechanical elements.  

Rene Schricker, Autodesk Technical Solutions Engineer, was tasked with creating the main structural chassis of the wheel. This critical component forms the backbone of the steering wheel and must resist the forces applied to it during use. It also needs to be designed such that it fits around the other internal component parts.  

Here, Schricker decided to employ generative design inside the Autodesk Fusion Simulation Extension to explore different design solutions. He started by defining geometry that must be preserved; fixing points where the chassis needs to attach to other components. In addition, geometry was selected that should be avoided, such as the overall 3D volume of the inside of the wheel and other internal parts. The avoidance geometry consisted of static components like the PCB and moving parts such as the internal switchgear.

The generative design process uses “avoid geometry” (shown in red) to limit the extent of the 3D design.

Next, Schricker defined the expected loads that would be experienced in service and focused on different manufacturing processes that could be employed. From the growing list of supported methods (3D printing, plus 3- and 5-axis CNC machining, and casting), Schricker chose additive manufacturing combined with stainless steel AISI 304. Once all variables were defined, Schricker used Fusion’s cloud-based computing to create multiple different designs while he continued working on other tasks.

Generative design creates multiple, viable design solutions that can be compared to pick the best one.

In less than an hour, Fusion had produced more than a dozen viable designs. Fusion can display the designs in different ways, including views that rank the different designs in terms of weight, volume, strength, and other criteria.

Design options can be ranked using different criteria, including volume, mass, strength, and more.

On this occasion, the initial list of designs was quickly reduced to a small number of preferred designs. At this point, Schricker simply chose the model that had the nicest aesthetic appearance; a few seconds later a complete 3D model was available inside Fusion. It’s worth noting that the 3D model is fully editable B-rep solid – not a triangle mesh – meaning further edits can be made (if needed) prior to assembling with the rest of the steering wheel.

The finished chassis was produced using generative design technology within Fusion.

Before manufacturing could commence, Schricker was keen to check the performance of the part. He did this using the range of finite element analysis (FEA) tools contained in the Autodesk Fusion Simulation Extension. As before, these checks could be carried out inside Fusion, avoiding the need to export any data to third-party software tools. Reassuringly, the finished design passed every structural test – meaning it was approved and ready for production.  

“Generative Design has proven to be the perfect tool for the Additive Manufacturing aspects of this project. With very little effort it created geometries that are perfectly optimized for 3D printing.”

Rene Schricker, Technical Solutions Engineer, Autodesk 
Fusion includes powerful simulation tools to validate performance and highlight potential stress hot spots.

Wrapping up the design

As the team continued to work on the internal mechanisms, Autodesk Technical Specialist, Ignacio Madina, was given the task of completing the design of the outer molding that would encase the rear of the steering wheel. Madina activated the Autodesk Fusion Design Extension to unlock some of the more advanced modeling tools in Fusion. He began by taking the initial 3D design from Derdak – which was missing this critical back molding. He then created a 3D form using a mix of prismatic and organic surfaces. Once done, various ribs, bosses, and fixings were added to enable the assembly of the molding to the rest of the wheel.

The back casing of the steering wheel includes multiple ribs and bosses for strength and assembly purposes.

Having finished the design of the molding, Madina’s attention turned to converting it into the core and cavity blocks that would be used for a low-production run via injection molding.

Fusion modeling workflows simplify the conversion of plastic product designs into tooling for mass production.

As before, the Autodesk Fusion Simulation Extension was used to check the plastic part to identify issues that can complicate the molding process, such as: 

The Fusion Simulation Extension can check parts to identify problems, such as excessively thick/thin wall sections.

In addition to these static checks, Madina carried out dynamic simulations to visualize and validate the mold-filling process. A study was carried out to determine the potential impact on mold fill-time and molding success rate and compare the results if using a 2-point or 3-point injection solution.  

Within seconds, Fusion provided the visual and numerical information needed to decide which solution to use. In this case, it was determined that the 2-point and 3-point solutions were similar in terms of fill time, but the 2-point option produced more desirable weld-line predictions.

Virtual simulations were used to compare 2-point and 3-point injection options.

“The simulation tools inside Fusion have been a life saver on this project. The manufacturing team had a single time slot when they could machine the molds and I needed to be able to check the moldability of the part and design the core and cavity blocks in just a few days. Thankfully, the simulation proved the design was good and the molds were finished in time for production to begin.”

Ignacio Madina, Technical Specialist, Autodesk 
Molding simulations can help identify air traps, sink marks, weld lines, and other faults.

Shifting gears into manufacturing

The remaining component parts were designed using a combination of approaches. In some cases, standard libraries of parts were imported from cloud-based libraries (for example screws, fixings, etc). Whilst other, non-standard parts were modeled inside Fusion. In total, the finished design consisted of 66 individual component parts (the PCBs were considered as single pieces). It was now time for production to begin.

By using cloud-based collaboration the team was able to complete the steering wheel design in just a few weeks.

For production to happen, the team used Fusion to generate the usual collection of 2D drawings, renderings, and animations and used them to support internal and external communications. This included general assembly drawings, annotated sub-assemblies, detailed component drawings, and bills of materials. These were all stored centrally in Fusion and securely shared with colleagues, partners, and suppliers using Fusion’s “share link” option. The recipients of these links could instantly access the data they needed to begin planning production. 

Fusion automates the production of 2D drawings, including General Assemblies and Bills-of-Materials.

Additive manufacturing makes the impossible possible

Remember that crazy generative designed chassis that we covered earlier? Let’s talk about how it was made using additive manufacturing. This task was given to Thomas Stock, a Manufacturing Research Engineer based in the Autodesk Technology Center in Birmingham, UK.  

Stock decided to use an MPRINT+ SLM machine as supplied by One Click Metal, but this presented an immediate challenge. The component itself was so large that it was unclear if it would fit inside the build volume of the machine. To find out, Stock activated the Autodesk Fusion Manufacturing Extension and used the specialist tools that it contains to orient and prepare the model for production before exporting manufacturing data to allow production to start. 

Stock started by using the automatic part orientation capabilities in Fusion to calculate how to best fit the 3D part into the machine. Once the orientation was calculated, Stock noticed that there was less than 1mm of clearance around the 3D part – that is one tight fit!  

Automatic part orientation combines with associative support structures to prepare the part for 3D printing.

Stock then used Fusion to add associative support structures to the part to ensure it maintained its shape during the printing process (this is essential when printing parts with overhanging geometry). In minutes, Stock finished preparing the part for 3D printing and exported the necessary slicing data to the One Click Metal machine. The print itself was left to run over the next couple of days – completing 20 hours later.  

The main chassis was produced using an MPRINT+ SLS 3D printing machine from One Click Metal.

Once finished, Stock used One Click Metal’s MPUREpro unpacking station to sieve and recycle the surplus powder before removing the finished part from the machine. To complete the process, the part was cut from the build plate, support structures removed, and the part’s surface bead blasted. 

The finished chassis part was de-powdered using the MPUREpro machine from One Click Metal.

“This is definitely the most challenging part I have ever printed, and I was certain that it wouldn’t fit inside our machine. However, the orientation tools in Fusion did a fantastic job in fitting the part into the build volume – just. I am really happy with the end result.”

Thomas Stock, Manufacturing Research Engineer, Autodesk 
The finished generative design, 3D printed chassis part after post-processing.

Subtractive machining brings precision where it’s needed

Most of the other component parts were manufactured either at Autodesk’s Technology Center in the UK or outsourced to a small network of suppliers. Let’s consider some of the parts made “in-house,” starting with the quick-release mechanism seen on the back of the wheel.  

The quick-release mechanism contains multiple components made using CNC machining.

The production of this part was assigned to Manufacturing Engineer, and Turn-Mill expert, Christopher Cooper. Starting with the 3D dataset produced by Derdak, Cooper spent time considering the best way to manufacture the individual components that make up the quick-release mechanism. He knew that CAM programming would be challenging, so he activated the Autodesk Fusion Manufacturing Extension to unlock advanced CAM tools inside Fusion. 

As with most CAM programming projects, Cooper had a good idea of how we wanted to machine the parts. Having picked the first component, he defined the machining setup inside Fusion and selected the appropriate tools from his cloud-based tooling library. Cutting speeds and feeds were automatically loaded from the library based on the stock material being machined.  

A machining setup is defined containing datums, stock material, and safety clearance heights.

Over half a day, Cooper then created a suite of high-quality toolpaths that would be used to produce multiple, identical components from a cylindrical bar of stock. These included: 

Before actual machining took place, Cooper decided to use the simulation capabilities in Fusion to validate his toolpaths using a virtual representation of the stock material. This allowed him to check the quality of the toolpaths and identify any problems or mistakes in a virtual world, long before any actual cutting takes place – helping to avoid costly mistakes or machine collisions happening in the real world.

Simulating machining toolpaths inside Fusion can identify mistakes early and avoid costly machine crashes.

Once the toolpaths were checked, Cooper exported NC machining code to run on a DN Solutions (formerly Doosan), Lynx 2100LSY turning center, fitted with twin spindles, a single turret, and a Fanuc NC controller.

CAM programming on the shop floor gives machine operators greater control, flexibility, and independence.

“The quick release mechanism contains some simple parts in terms of geometry, but there’s still a lot of CAM programming needed to get the desired accuracy and surface finish. Strategies like the automatic deburr mean the parts come off the machine looking great and not needing any extra hand finishing which saves me a lot of time and effort.”

—Christopher Cooper, Manufacturing Engineer, Autodesk  

Mold production with CNC machining

Turning our attention to the manufacturing of the plastic molding discussed previously. Once the core and cavity blocks had been designed, the job of producing them was passed to Josh Reader, a Manufacturing Engineer who, like Christopher Cooper, is based in the Autodesk Technology Center in Birmingham, UK. 

Reader began by interrogating the core and cavity blocks to identify issues that could complicate manufacturing. Challenging features such as deep ribs or small internal fillet radii were identified as these could be difficult/impossible to produce with CNC milling. In addition, Reader also used undercut shading to identify features or surfaces that would need (or benefit) from using 5-axis machining.  

  Like Cooper before him, Reader recognized the need for more advanced CAM programming tools and so quickly activated the Autodesk Fusion Manufacturing Extension. Once done he defined his machine setup (including defining the stock material, selecting cutting tools, and defining the workpiece coordinate system). Reader also identified the suitable CNC machine to use, on this occasion a DMG-Mori DMU 60 Evo 5-axis milling center.

Fusion adaptive clearing is an effective and tool-friendly strategy for initial rough machining.

CAM programming the core block utilized many of the 3- and 5-axis toolpaths that Fusion offers, with roughing and finishing strategies being used, including: 

Once these toolpaths were created, Reader used Machine Simulation to check for problems such as collisions or near-misses involving the cutting tool, workpiece, stock, and machine. Fusion was also used to check the programs to ensure they remained within the axis stroke of the machine, avoiding the risk of axis over-travel. After all simulations and analysis were complete, Reader was confident all toolpaths were safe and ready for production.  

Machine simulation displays a virtual piece of stock and shows the progressive removal of material.

Whereas Christopher Cooper did the CAM programming and machine operation, Reader worked in partnership with a dedicated machine operator; in this case Fernanda Medina Aguirre, a fellow Manufacturing Specialist at Autodesk. To help communicate exactly how the parts were to be machined, Reader produced detailed setup sheets containing information about the cutting tools that would be used, the exact position of machining datums, as well as the various strategies being used, and the feeds and speeds that were employed.

CAM programming is often completed by manufacturing engineers who send programs to machine operators.

2D documents were combined with “shared views” that showed images and details about the 3D models and toolpaths; these were shared securely with Aguirre who worked with Reader to set up fixtures on the machine, load the stock, and define the G54 and G55 machining datums.

5-axis swarf machining was used to finish the side wall of the core block.

“Machining the core and cavity mold blocks was surprisingly easy. It involved a combination of 3- and 5-axis machining, with some of the new Geodesic and Swarf Finishing toolpaths that have been added recently. The results are impressive. The machine motion was buttery smooth which gave a great surface finish.”

Josh Reader, Manufacturing Engineer, Autodesk 

Reader and Aguirre worked together to program both the core and cavity mold blocks. The cavity mold did not contain any slots or challenging features and so could be produced with CNC machining alone – no need for EDM. 

Adding the finishing touches with EDM

At a very early stage, Reader realized that the core mold block would need additional processing. It contained features, like deep slots and sharp internal corners, that were going to be impossible or impractical to produce via CNC milling. The decision was made to leave these features un-machined and produce them using electrical discharge machining (EDM).  

Fusion’s solid modeling tools were used to create 3D models that represent the forms that were to be “burned” into the tool via EDM.

Deep ribs and sharp internal corners were produced using electrodes and electrical discharge machining (EDM).

The electrode models (shown above in red) were securely shared with a local supplier who was tasked with manufacturing the electrodes and using them to “burn” the features into the previously CNC-machined core mold block. 

Multiple copper and graphite electrodes were produced to “burn” detailed features into the core block via EDM.

The supplier produced a family of electrodes in graphite and copper, with each one consisting of key elements: 

The die sinking process was then used to “burn” the electrodes into the core block, forming the required features.  

Electrical discharge machining (ED) was used to “burn” multiple ribs and pockets into the core block.
The complete core bock was produced using a combination of CNC machining and EDM  

Leaning on the supply chain

While many of the component parts were produced by the Autodesk team “in-house”, capacity issues meant some of these parts were outsourced to approved suppliers. We would like to thank the following organizations for their support. 

Bringing it all together

The breadth of Fusion tools that the team leveraged, from design to manufacturing to data management, culminated in a successful project. “At the start of this project, the team wanted to test Fusion to see just how well it supported the end-to-end development of a real-world consumer product,” says Christoper Derdak, Autodesk Technical Specialist and project lead. “I was pleasantly surprised at just how well Fusion was able to meet the design and manufacturing needs but what impressed me the most was how the collaborative tools in Fusion allowed the team to work together. I cannot wait for us to showcase the finished steering wheel at the Hannover Messe show in April 2024 and look forward to seeing how our current and future customers react.” 

Check out these resources to learn more: 

Learn how you can make amazing consumer products with Autodesk Fusion 
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Optimizely
We use Optimizely to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Optimizely Privacy Policy
Amplitude
We use Amplitude to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Amplitude Privacy Policy
Snowplow
We use Snowplow to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Snowplow Privacy Policy
UserVoice
We use UserVoice to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. UserVoice Privacy Policy
Clearbit
Clearbit allows real-time data enrichment to provide a personalized and relevant experience to our customers. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID.Clearbit Privacy Policy
YouTube
YouTube is a video sharing platform which allows users to view and share embedded videos on our websites. YouTube provides viewership metrics on video performance. YouTube Privacy Policy

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Adobe Analytics
We use Adobe Analytics to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Adobe Analytics Privacy Policy
Google Analytics (Web Analytics)
We use Google Analytics (Web Analytics) to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Web Analytics) Privacy Policy
AdWords
We use AdWords to deploy digital advertising on sites supported by AdWords. Ads are based on both AdWords data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AdWords has collected from you. We use the data that we provide to AdWords to better customize your digital advertising experience and present you with more relevant ads. AdWords Privacy Policy
Marketo
We use Marketo to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. We may combine this data with data collected from other sources to offer you improved sales or customer service experiences, as well as more relevant content based on advanced analytics processing. Marketo Privacy Policy
Doubleclick
We use Doubleclick to deploy digital advertising on sites supported by Doubleclick. Ads are based on both Doubleclick data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Doubleclick has collected from you. We use the data that we provide to Doubleclick to better customize your digital advertising experience and present you with more relevant ads. Doubleclick Privacy Policy
HubSpot
We use HubSpot to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. HubSpot Privacy Policy
Twitter
We use Twitter to deploy digital advertising on sites supported by Twitter. Ads are based on both Twitter data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Twitter has collected from you. We use the data that we provide to Twitter to better customize your digital advertising experience and present you with more relevant ads. Twitter Privacy Policy
Facebook
We use Facebook to deploy digital advertising on sites supported by Facebook. Ads are based on both Facebook data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Facebook has collected from you. We use the data that we provide to Facebook to better customize your digital advertising experience and present you with more relevant ads. Facebook Privacy Policy
LinkedIn
We use LinkedIn to deploy digital advertising on sites supported by LinkedIn. Ads are based on both LinkedIn data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that LinkedIn has collected from you. We use the data that we provide to LinkedIn to better customize your digital advertising experience and present you with more relevant ads. LinkedIn Privacy Policy
Yahoo! Japan
We use Yahoo! Japan to deploy digital advertising on sites supported by Yahoo! Japan. Ads are based on both Yahoo! Japan data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Yahoo! Japan has collected from you. We use the data that we provide to Yahoo! Japan to better customize your digital advertising experience and present you with more relevant ads. Yahoo! Japan Privacy Policy
Naver
We use Naver to deploy digital advertising on sites supported by Naver. Ads are based on both Naver data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Naver has collected from you. We use the data that we provide to Naver to better customize your digital advertising experience and present you with more relevant ads. Naver Privacy Policy
Quantcast
We use Quantcast to deploy digital advertising on sites supported by Quantcast. Ads are based on both Quantcast data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Quantcast has collected from you. We use the data that we provide to Quantcast to better customize your digital advertising experience and present you with more relevant ads. Quantcast Privacy Policy
Call Tracking
We use Call Tracking to provide customized phone numbers for our campaigns. This gives you faster access to our agents and helps us more accurately evaluate our performance. We may collect data about your behavior on our sites based on the phone number provided. Call Tracking Privacy Policy
Wunderkind
We use Wunderkind to deploy digital advertising on sites supported by Wunderkind. Ads are based on both Wunderkind data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Wunderkind has collected from you. We use the data that we provide to Wunderkind to better customize your digital advertising experience and present you with more relevant ads. Wunderkind Privacy Policy
ADC Media
We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
AgrantSEM
We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
Bidtellect
We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
Bing
We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
G2Crowd
We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
NMPI Display
We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
VK
We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
Adobe Target
We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
Google Analytics (Advertising)
We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
Trendkite
We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
Hotjar
We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
6 Sense
We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
Terminus
We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
StackAdapt
We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
The Trade Desk
We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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