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Design Exploration of CNC Machine Components Using Generative Design

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Description

Generative design is often seen as an alien parts generator. In this session, we will share how we incorporated this design exploration method into our traditional design process, and how we applied the method on a very typical welded parts assembly of an automatic tools changer on a CNC machine tool. With no specific expectations, as we believed it was already fully optimized, we achieved almost 20% of mass reduction, positively impacting the overall machine precision. And as a side benefit—we got really good-looking parts. You'll learn which selection method we used to identify potential parts for generative design and how we defined the success criteria. We'll cover the whole process, from multiple designs exploration up to production and installation itself. We'll also discuss our generative design rollout plan.

Key Learnings

  • Learn how to implement generative design in a traditional industrial machinery business.
  • Learn about applying generative design on existing products to improve their performances.
  • Explore a strategy for generative design pilot and rollout.
  • Discover the potential benefits of an automated design exploration in your company.

Speakers

  • Vojtěch Frkal
    Vojt?ch Frkal is the technical director of TOSHULIN, a.s. He received his university education at the University of Technology in Brno. His studies were focused on the design of machine tools. He received his degree in 1995 at the Institute of Manufacturing Machines and Robotics. In his further studies he focused on the field of computer aided design in mechanical engineering. He graduated from the Institute of Solid Mechanics in 1997. He started his career in 1995 in a small engineering company. Here he worked as a designer and later as a development manager until 1999. Then he worked in the design department at the Brno branch of the Belgian company IG Watteeuw. This company deals with development and production of industrial gearboxes. Gearboxes manufactured at the Brno plant are used in rolling stock, agricultural machinery or construction machinery. Since 2000, he has been working at TOSHULIN, a.s. The company is one of the major manufacturers of machine tools in the Czech Republic. It has its own design and development department. The company's products are at a high technical level and are considered the best in the field. Vojt?ch Frkal initially worked as a designer, later held other positions in the design department. Since 2006, he has been the head of technical support for the sale of machine tools. Here he participated in communication with customers and proposed new solutions according to their requirements. He has been working as technical director since 2014. He is responsible for the development of new products, the design of the mechanical part of the machine tools, electrical HW and SW equipment, etc. He actively participates in the management of innovation and technical development. The company TOSHULIN, a.s. is a participant in important research projects in the Czech Republic and in Europe.
  • Avatar for Kamil Cejpek
    Kamil Cejpek
    Combustion engines designer by education with passion for fast sporty cars, woodworking and remote controlled helicopter models, last 20+ years I am helping customers to discover opportunities to make things better and adopt Autodesk design and manufacturing solutions across their organizations. As a technical sales specialist in Autodesk, I support internal sales teams as well as our partners in customer interactions, covering South-East Europe countries and Poland, almost anywhere between Baltic and Adriatic Seas. My main expertise lies in data and process management, though I am also passionate about new tech like generative design, design automation, VR/AR and additive manufacturing. Visionary thinking and seeing broader picture defines my personality and the way how I do things.
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Transcript

VOJTECH FRKAL: Welcome to the recording of our session called "Design Exploration of CNC Machine Components Using Generative Design." Generative design is often seen as an alien parts generator. In this session, we will tell you a story how we incorporated this design exploration method into our traditional design process, and how we applied the method on very typical welded parts. These are parts of an automatic tool changer on an CNC machine tool.

With no specific expectations as we believed it was already fully optimized, we achieved almost 20 of mass reduction positively impacting of all machine precision. And as a side benefit, we got a really good looking parts. You will learn how we identified potential parts for generative design, and how we defined the success criteria. We will cover the whole process from multiple design variants up to production and installation itself. We will also discuss some of our generative design rollout plan.

We would like to share several topics with you during this session. You will learn how we introduce the progressive method of generative design in our company. You will also hear how we implemented generative design in a traditional industrial machinery environment. We will show an example how you can apply generative design on existing products to improve their performance. We also will mention the potential benefits from introduction of tools for automated design exploration in your company.

And now, let me introduce myself. My name is Vojtech Frkal. I work as a technical director at the company Toshulin. I live in the Central Europe in the Czech Republic.

As a technical director, I am responsible for the design department, for the developing of our products for the technical development of the machine tools produced in our company. When I should comment my free time, I like to spend my free time outside in nature. And also, I like to listen on music on a vinyl LPs. And now, I will hand over to my co-speaker, Kamil.

KAMIL CEJPEK: Thank you, Vojtech. Hello, everyone. My name is Kamil Cejpek. I'm a technical sales specialist in Autodesk, helping customers to discover possibilities and capabilities of design and manufacturing solutions from Autodesk. I'm supporting and working with customers in Central and Southeast Europe, many countries between Baltic and Adriatic Sea. And Toshulin is, obviously, one of my customers I'm taking care of.

I'm based in the Czech Republic in Central Europe. And in my spare time, I am always enjoying to drive sporty-- civil sporty cars. Since my childhood, I also play with remote controlled car models. Recently started also with helicopters. And on top of that, I also own a small woodworking company, where we build beautiful custom made furniture. I'm handing over back to Vojtech.

VOJTECH FRKAL: And now, let me tell you a few words about the company where I work. As mentioned, the name of the company is Toshulin. And the company Toshulin is located in Central Europe, Czech Republic. Toshulin is a middle-sized family-owned company.

Nowadays, we are 270 people working in this factory. And we produce machine tools for our customers for all over the world. And the production of machine tools that Toshulin is based on a long-term experience. We build the machine tools more than 70 years long.

Toshulin specialized on specific type of machine tools called vertical lathes. And we build the machining centers based on vertical lathes. Nowadays, majority of machines are multi-functional. They can perform different technologies, like turning, milling, drilling, tapping, grinding, and so on.

We offer machines of different sizes and configurations. Each machine is customized. All the machines are CNC controlled. All machines can work in an automatic cycle, including change of different cutting tools.

On the picture, you can see the kinematics of a typical machine. There is always a round turning table and then two linear axes for movement of the tools. These movements can interpolate and create different shapes.

When I can tell you a few words about our customers. Our customers are companies which produce parts for different types of equipment in different fields of industry. Our most valuable customers are companies from aerospace, especially producers of jet engines.

As mentioned, the company exists more than 70 years, but still our goal is to be leaders in the field of vertical machining centers. So innovations are necessary for us. And the development in different areas accelerates nowadays so we need to follow all modern trends in the technology.

KAMIL CEJPEK: And this is basically where the story begins. The reason why we started jointly Autodesk local team and Toshulin on generative design is that Toshulin has been always an early adopter of our technologies. They were one of the first commercial users of Inventor in the past. One of the first company in the region they adopted both professional for managing design data fully integrated with their existing ERP system just to streamline engineering and production processes.

So it was just a matter of time when we introduced generative design. We started conversation together how to adopt or at least how to try this promising technology in Toshulin environment. Three key reasons why Toshulin was considering to give a try was, first, they really like to do things better, faster, and if possible, also with less resources.

When it comes to less resources, this is also related to existing lack of people in Central Europe, skilled people, designers. And one of the ideas and expectations was the generative design actually can help existing design team to be more efficient, especially during design exploration phase. And last but not least, it was sort of curiosity, if an already optimized and proven design can be optimized even more by utilizing generative design methods.

So at the very beginning of our conversation we defined the overall process. And we agreed on qualification, exploration, rationalizing, and manufacturing phases. In qualification, we selected existing critical parts. And we had multiple options. Then we defined constraints, and we also define desired outcomes.

During exploration, this was exactly when and where we used generative design. So we got multiple variants with or without restrictions in multiple iterations. What we got from exploration then in the next phase we rationalized. And basically, we translated alien parts into rational form, which was possible then to manufacture by using existing manufacturing methods. And how we qualified and what parts were selected, I'm passing to Vojtech back, and he will explain.

VOJTECH FRKAL: So let's have a look on the parts. So during our initial discussion, we considered two possible parts as candidates for optimization. The first one was the part called ram slide, which is a robust part. It makes a vertical support of the ram on the machine. This part is made from cast iron as a casting.

The other candidate was the tool magazine. It is a sub-assembly of the machine, where the different tool holders and heads are stored. Main parts of the tool magazine are made from sheet metal. And they are welded together.

Finally, we decided to go with tool magazine. There are a few reasons. The first one is that we can produce the main parts completely at our factory. We can use our common technology. We have the complete control on the production. So we are able to manufacture modified part shapes easily using existing technology and equipment.

Here, at this video, you can see the function of the tool magazine. In our case, our machine tools are working with the automatic tool exchange. This is a principle of pickup. So the ram goes to the exchange area, and the holders are unclamped and clamped automatically from the tool magazine.

On the specific machine type, the tool magazine is placed on a structural part of the machine. We call this part crossrail. The geometry of the crossrail influences directly the overall geometry behavior of the machine.

And as you can see on the picture, the tool magazine is hanging on one of the end of the crossrail. And the weight of the tool magazine causes a deformation of the crossrail. So this is a negative influence on the machine accuracy. So reducing of the weight is very positive. And just for your idea, when we talk about an accuracy of a machine tool, we speak about dimensions which are comparable or even smaller than diameter of human hair.

And let me introduce a few more specific details about the tool magazine. For the optimization, we chose two main parts of the magazine. These are the console and the disk. Mechanical rigidity of the tool magazine is very crucial for the function of the machine.

And during the design, also a non-standard situation must be considered because even if the machine runs automatically, some errors can occur. So during the design we have to consider some possible collisions and the magazine should not be destroyed during this situation. So our goal was to reduce the weight, but at the same time keep the full functionality of the tool magazine.

KAMIL CEJPEK: So once we had selected parts we wanted to optimize, we started with the exploration phase. Now, here you can see three different types of designs. The first one is human design. So this is what Toshulin designers developed over the last years. And they optimized every time it was possible or it was needed to make it better. So this is human design.

In the middle, on the other hand, you have a fully generative design outcome. So based on inputs, based on constraints, we got very organic shapes. They look amazing, but as you can imagine would be quite costly to produce it.

So the last design is something I call hu-ge design. So it's basically a combination of human and generative design. So a human designer designs this type of shapes based on generative design outcomes. Fully optimized, considering production methods, they're currently available in Toshulin.

So how we got there. First of all, we decided to use existing geometry. So we didn't start from beginning, but we took the detailed 3D models. They were already created in Autodesk Inventor. And we brought them into Fusion 360. We found this as the most efficient way. We didn't touch those models in Inventor itself, but we brought them first to Fusion 360. And then we started to modify or-- well, we basically prepared the geometry for generative design exploration and studies in Fusion environment.

It is not only about simplification. In Fusion 360, by the way, there are really, really powerful features and functions that you can literally with few clicks, you can simplify a fully detailed 3D model into basic shapes exactly what you need for generative design exploration. But it is also about defining additional geometry. Like in this case, you can see that we had to define areas where we wanted to keep free space because there are additional components, related components, or even motor inside that are.

We also define the geometry that it needs to be preserved. So basically connecting points, in this case, the place where we were connecting another components later on. And we also defined the envelope basically the basic shape where that generative design could start from.

And it was not only about considering one use case. But for example, in this study definition, we considered different operational modes. As Vojtech already mentioned, different conditions, different loads, different tools in the build could be. And also we considered failure modes. So what happens if there is an unwanted crash into the build? It should really keep that load, and it should operate as expected.

Once we did all these definitions, and we run all studies, we got a lot of results. Generative design doesn't generate only one and the only outcome. So you get a lot of options, different criterias, meeting those criterias, and then you need to decide which the outcome is the best fit for your needs. There are plenty of tools-- analytical, more or less visual you can compare different results. And you can decide very easily what outcome is a perfect starting point for your next step.

As I said before, we-- from the very beginning, we considered generative design as a really good guidance for human-based design. So we did all calculations without any restrictions in terms of production methods. With that, we got alien parts, but in the end we use them as a starting point for something what was then created by a designer.

And that was actually the next step. Because what we got from generative design, it was really, really nice and very good looking. But on the other hand, we would like to produce such parts with a traditional subtractive methods, right? So what we did was that we took the outcome from generative design study, and we used it to build a new 3D model by designer but considering that this is a welded metal plate type of design.

So the initial design was done in Fusion 360. And because we were simplifying and changing geometric comparing to generative design outcome, we always made sure that it is still meeting the requirements. So we run several finite element analysis directly in Fusion. Later on, we also double-checked with Nastran directly in Inventor.

It is very important, if you are taking this type of approach, to really verify, verify, verify you're doing those changes in multiple iterations. So every time you make a change, make sure that the results are still meeting the requirements. And this was actually the moment to really stop in the process because until now, everything was happening in digital world. But at this certain point of the project, Toshulin had to decide if they are going to produce it and make this real, or they will just keep it as a digital exercise and continue working as they used to. And handing over to Vojtech to explain how they decided.

VOJTECH FRKAL: Yes, at this moment, we had a finished proposal of the final version of modified parts. And so we prepared some material. And we presented internally this solution to the company management even to the owner. And everybody liked the idea. And it was decided to proceed with the implementation of the project to the physical realization.

So we choose one of the machines which were in the realization process. And we applied the magazine with the new design on the machine. The machine itself, this particular machine, is now running at one of the customers in the Netherlands.

And how we proceed, the initial step was to create the documentation for our colleagues in production and assembly. So we used our standard tool we are using. This is the Inventor, so we created a documentation in Inventor. And then we proceed with the physical production of the parts.

The first step in the production is the machining or preparing of the raw material. Here in this case, as mentioned, these parts are made from sheet metal. So we started to cut the parts for the sub-assemblies on the laser cutting machine. And after that, it was welded together.

And so we spoke about the advantage of this case was that we could work as we are used to. So our skilled colleagues in production, they produce the parts as they are used to. Here on these pictures, you can see some examples of several activities and steps during the production of the parts.

On the right side, there is a milling and drilling of the disk of the magazine. And in the middle some quality check on CMM. And on the right side some preparation for the last step in the production, which is painting. So here you can see this is very last activity before we start to assembly.

And you can be sure that these strange parts impressed everyone in the factory. So everybody was interested on the result, on the final look of the tool magazine. So firstly, we assembled the sub-assembly of the magazine. And then, finally, we placed the complete magazine to the machine.

And a very important step is to verify the results in the reality. So we loaded several tool holders and heads to the magazine. And we measured the deformation of the magazine.

The deformation is caused by the weight of the tools. So we used an optical device for this measurement. And the result of the measurement was very good, so the deformation was even smaller than we expected at the beginning.

So the deformation, it was one of the important parameters, but the main parameter and the main intent was to reduce the weight. So we managed to reduce the weight of the console and disk of the magazine by 100 kilogram. So it means 18% of the weight, which could be reduced. And now, I will hand over to Kamil again.

KAMIL CEJPEK: So what lessons we learned during this proof of concept? And there are three different aspects. Let's start with technical perspective. From technical perspective, Toshulin learned that the generative design really gives them great ideas.

So there were some outcomes they were quite interesting. And once we saw them, they were really obvious. But as always, when you see it it looks obvious, but without generative design, you would never find such option or such solution.

Second, ease use. This is really not a rocket science. And designers in Toshulin, they were able to adopt generative design outcomes very easily. And they were able to implement them into existing workflows very easily. And fun-- well, funny, actually, nice thing is that the generative design tools, they can be used right away today.

And third technical lesson learned was that generative design might be a very natural way of adopting Fusion 360 as a next generation design tool. From commercial perspective, Toshulin found generative design as a really innovation enabler. So it is something they're always interested in to make things differently and better. And generative design really enables them to explore more solutions, better solutions with actually not that much extra time and effort investment.

Path to sustainability. Although there are no immediate and impressive results from proof of concept, once they do a complex design optimization through whole machine or critical parts on the machine, they understand that this might lead them to cost material and emission reductions eventually.

Last but not least, market differentiator. Nowadays, not only performance and price sells today. There is really nice story. There is a very good looking parts on the machine. And those are important differentiators on the market. And Toshulin is aware of that.

From organizational perspective, well, confirmed designers are still needed. So generative design does not replace any designer. It really helps designers to be more efficient, to automate some tasks, to speed up the design exploration phase specifically. But it still requires skilled designer to make final decisions and to complete the designs based on generative design outcomes.

They also found very interesting that generative design enables people's potential. It means that the designers become much more universal while using generative design because generative design helps them to explore types of designs they're not experts in, or they have no extensive experience with. But with generative design, they can start, and they can explore those design types and components they even maybe never touched before.

Last but not least, attracting talents. Especially younger generation today when they are deciding for which company they would like to work, it is not about the typical criteria anymore. Salary, working conditions, benefits, they're still important, but they're also looking into ways how companies are doing things. What technologies they are using. And definitely something like generative design very high technology is attracting new talents. And they are eager to use such technologies in technological companies.

With that, I'm handing over back to Vojtech. And he will tell you what our future plans with generative design in Toshulin.

VOJTECH FRKAL: So thank you very much. So we think that we have achieved a very nice result here. And it motivates us to continue in adoption of the generative design technology.

So nowadays, we are at this stage that we have already produced several machines that are equipped with this new improved design of the tool magazine. At the picture, for example, you can see application on a bigger machine. This is a machine with a table diameter of 3 meters, which means 9.3 feet.

In field of machine tools, it makes sense to reduce weight of moving parts. This makes it possible to increase dynamics of the machine, reduce energy consumption, and improve accuracy of the machines. So in this concept, we returned back to the idea to optimize design of the ram slide. This is one of the future plans.

And the other candidate right now is the ballscrew support. This is the second white piece you can see on the picture. This component can be in different variants quite long and slim. So here, the potential is to increase the stiffness of the part and at the same time keep or even reduce its weight.

KAMIL CEJPEK: So as you can see, generative design was not only proof of concept thing in Toshulin, and they are continuously working and using and utilizing this very promising technology. And actually at the very beginning when we started this project, we made a rough plan, sort of roadmap how this could end up in Toshulin. In the very beginning, we agreed on proof of concept, which was successfully done, and you just seen results of it.

The next step is to introduce generative design to proper users in Toshulin and to optimize workflows, build internal expertise, especially with senior designers so they can really use generative design as a part of their daily workflows for most critical parts. In the future, there is also an idea to roll this out and to make this technology available to any designer in the team so they can benefit on generative design on any type of parts or components. And in the future, they are also considering to adopt new manufacturing methods, because as you have just seen, there is a way-- a very efficient way-- to combine traditional manufacturing methods with generative design.

But of course, if you want to enable a full generative design potential, then it is also good to consider a very modern manufacturing methods. So additive manufacturing combined additive and subtractive manufacturing methods that's something what Toshulin will consider in the future. And with that, thank you very much for watching this recording, and have a good day.

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We use OneSignal to deploy digital advertising on sites supported by OneSignal. Ads are based on both OneSignal 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 OneSignal has collected from you. We use the data that we provide to OneSignal to better customize your digital advertising experience and present you with more relevant ads. OneSignal Privacy Policy
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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