AU Class
AU Class
class - AU

Lightning Strikes Twice: Revisiting Generative Design for Mass Production

共享此课程
在视频、演示文稿幻灯片和讲义中搜索关键字:

说明

Lightning Motorcycles has been breaking records in the world of e-bikes since the company entered the market. The LS-218 is pushing electric and gasoline-powered motorcycles to be the fastest production bike on the market. But pure performance is not the only goal; Lightning is focused on providing consumers with the best quality and value in every product it creates. Key to meeting these goals and staying ahead of the competition are new technology and ways of thinking. Lightning was an early adopter of generative design. Collaboratively, we produced a part-consolidation and lightweighting workflow that saved 30% of target component mass. In this class, we’ll show you how developments within generative design can be used to gain these benefits, while ensuring cost-effective manufacturing. This talk will cover workflows used within Fusion 360 software, maximizing the integrated simulation, generative design, and manufacturing tools to better understand the part and its performance.

主要学习内容

  • Learn how to apply generative design manufacturing methods to create designs suitable for mass production
  • Learn how to implement an integrated generative design and validation loop within Fusion 360
  • Learn how to apply generative design to multiple bodies within a singular assembly
  • Learn how to create a workflow to enhance the cost-effective manufacturing of generative design outcomes

讲师

  • Peter Simpson 的头像
    Peter Simpson
    Peter attended the University of Birmingham, graduating with a Masters Degree in Mechanical Engineering. He began his career with Autodesk during a summer internship and has since rejoined Autodesk as a Graduate Technical Consultant working in the Birmingham office, taking a full time role in the Process Specialist Team, and now becoming a Customer Advocacy Manager for Fusion 360 Design & Simulation. He has worked on a variety of projects, often focusing on the utilization of Generative Design within different industries, helping to drive the adoption of the platform and further develop the software. In his spare time, Peter is a keen sportsman, playing football, rugby and golf on a regular basis.
  • Nick Markovic
    I am a well-rounded individual with 8 years’ experience as a stress engineer coupled with a strong academic background. I have continuously broadened my engineering knowledge and gained key exposure in the aerospace, defence, rail, wind, oil and gas engineering markets. I am a specialist in both implicit and explicit finite element analysis techniques, with emphasis on non-linear analyses. I have recently joined Autodesk as a mechanical engineer in The Autodesk Advanced Consulting (AAC) team specializing in additive manufacturing and advanced simulations. I have joined AAC to maximize in-house knowledge of stress and structural analysis and bring key industry experience to the team. I have a strong passion for the practical application of new and emerging technologies to help advance the design and manufacturing industry.
Video Player is loading.
Current Time 0:00
Duration 0:00
Loaded: 0%
Stream Type LIVE
Remaining Time 0:00
 
1x
  • Chapters
  • descriptions off, selected
  • subtitles off, selected
      Transcript

      RICHARD HATFIELD: I'm Richard Hatfield, founder and CEO of Lightning Motorcycles. I'm a lifelong motorcycle rider. And engineering excellence has always been my passion.

      PETER SIMPSON: Hi, my name is Peter Simpson. And I work for Autodesk as a technical consultant. I work on the process specialist team as part of the Fusion 360 customer engagement organization. My main focus is generative design and finding new ways to harness the power of the software.

      Because of this, I was tasked with undertaking the main generative design for this part, and also any of the editing and processing that we needed.

      NICK MARKOVIC: Hi, my name is Nick. I am a research engineer at Autodesk Research. My main focus areas are developing new digital twin workflows and developing new manufacturing technologies. In this project, my responsibility was ensuring that the new Swing On prototype treated from the generative design technology was compliant and safe to operate by using Fusion 360 simulation product.

      This is the LS218. This is the fastest production motorcycle in the world. It's won numerous prestigious races and set a series of land speed records and it's electric. The story of Lightning really began in 1990, when I was invited to drive and participate in the development of an electric Porsche. And in the process I developed a deep understanding and expertise around electric drive systems and wanted to bring that to motorcycling.

      In 2006 I built the first lithium battery sport bike. And in the first test ride up the first hill, became convinced that electric motorcycles were the future. And this was a great opportunity to build a company.

      Recently, for the first time in human history, CO2 levels exceeded 415 parts per million in the atmosphere. The USA has the second highest CO2 emissions of all the countries in the world. Transportation is the largest source of carbon emissions in the United States.

      And worldwide, motorcycles have higher emissions than cars. And there are significantly more motorcycles in the world than cars. So we believe that building great electric motorcycles, which motivate consumers to convert to electric motorcycles, can contribute in a significant way to reduction of global warming.

      Lightning's mission is to build world-class two wheel electric transportation with superior efficiency, performance, and affordability than current gasoline alternatives. We believe two wheel electric vehicles are a great solution for daily transportation and an important solution for reducing traffic congestion and parking issues. We dream that electric motorcycles will replace gas motorcycles in all the major markets in the next 5 to 10 years.

      Our goal is to accelerate that Shift to the next generation of personal transportation by offering great products with competitive prices for riders to enjoy around the world. And we believe deeply in this mission and we build it into every bike. When people ride our bikes, whether they're world champions or first time riders, they get it.

      The electric motorcycles that have been available for consumers have not met the goals that consumers are looking for. So the range was inadequate, the charging time too long, the performance inadequate, price too high for the build quality. So as a result, electric motorcycles so far have not been able to win the hearts and minds of motorcycle riders over the gasoline alternatives.

      So we've really focused at Lightning on developing technologies to address these objections. So we build our products to create excitement and to bring new riders into electric motorcycling. Lightning seeks out competition to drive innovation in our company and then to use that innovation that we develop to create exciting products for the marketplace.

      So early on, to drive electric vehicle adoption, we set two milestones for ourselves. The first was to build electric motorcycles that could outperform the best gas motorcycles in the world. And then the second was to build a supply chain that would allow us to provide superior products at competitive pricing to our customers.

      So the first milestone was achieved at the Pikes Peak International Hill Climb where we became the first electric vehicle to race against the top gas racing motorcycles in the world and win by a decisive margin of over 20 seconds. So the key to how we've been able to achieve our second milestone, this combination of price and performance, is that our approach from the beginning has been fundamentally different.

      We began experimenting with lithium batteries for vehicles in 2006. And the process of developing competition bikes, doing EV engineering contracts, have created a deep fluency in these EV technologies and have created an understanding of how really to optimize the trade offs between power, and handling, and heat, and weight, torque, charge time, and price. And then take that and roll it all into products that have superior overall performance.

      So this has allowed us also to develop a network of relationships of engineers and other companies that also are passionate about the technology around electric vehicles. And then secondly, one of the design principles we developed through the engineering contracts is the importance of using a modular platform in EV technology so that we have the flexibility to use similar components in a widely disparate group of applications. So the same type of components can be used in an 80 volt ATV as are use in an 800 volt fuel cell hybrid bus or in an electric airplane. And this allows us to develop quickly and cost effectively.

      So there have been a whole series of challenges in building the technology in our motorcycles. So some of the challenges are building the level of performance that we need on something as small and as compact as a motorcycle. Thermal management is a critical issue, achieving this level of performance and not having temperature issues on the components.

      Weight is the enemy of performance motorcycles. So the ability to use the tools that we have from Autodesk to minimize the weight of our components, while still achieving the strength required and being able to package this in the very limited space of a motorcycle.

      When we began winning on the racetrack, people sought us out to work with us and tap into our expertise. This gave us a chance to work with electric vehicle innovators, entrepreneurs, inventors, engineers who are all leaders in their field. Over the years, we've done a series of engineering projects from electric cars, electric airplanes, electric boats, hybrid buses, ATVs, scooters, and material applications.

      And a couple examples of these were, one, a cutting edge battery technology company that contacted us to build a battery, the first battery pack capable of charging in five minutes. And to use it for a demonstration run between San Francisco and Los Angeles. And then secondly, collaborating with Autodesk in the development of their generative design software where we were able to work with them, utilize their software to design a swing arm for a bike that was both lighter and stronger. And this was featured in Autodesk rollout of the software. And we've learned a lot from each of these projects.

      So the tools that we have access to from our relationship with Autodesk have been a real key to achieving the level of performance and the maturity of products that we built. On a daily basis, we use Autodesk products like Fusion, Inventor, Alias to do the surfacing, the CFD and FDA analysis to achieve the aerodynamics, and the strength, and the light weight that we need for our products.

      So let's hear from the team from Autodesk who've been a major factor in our success.

      PETER SIMPSON: Thanks, Richard. Many of you may have seen from the title of our talk and previous AU presentations this is not the first time that we've worked with Lightning Motorcycles. We actually did a similar project back in 2018. And we are revisiting that project and looking at the very same part.

      In the original iteration, we set out to see how far we could really push the performance of this part. This was in order to allow Lightning to just continue pushing the performance of their motorcycles as a leader in the field.

      This project showed promising signs. We saved a huge amount of weight on the part. And we add it to some part consolidation efforts from their side.

      Unfortunately there were some shortcomings with this. And I'll go on to talk about them now. Due to the nature of generative design at the time, these shortcomings were somewhat unavoidable. There was a lack of manufacturing bias within the software in that we couldn't define a manufacturing method as well as we can today.

      The other issue was to do with the load case setup. It was much harder to actually translate those real world loads into the generative design software. To try and combat these shortcomings, we decided to leverage the advancements in generative design in Fusion 360 to focus on a more manufacturable outcome.

      We focused on utilizing the milling constraints with generative design to reduce the cost and complexity of the part while still allowing valid performance benefits. This would be traded off against some of the performance gains that we saw in the first iteration. But ultimately, it was in an attempt to find a commercially viable part that Lightning could sell on their bikes worldwide.

      The advancements made in the past three years in generative design have really enabled us to revisit this project. When we have such a clear trade off of manufacturability versus performance, the progression of the manufacturing controls within generative design is invaluable. It really allows us to pinpoint specific methods that we're interested in and have expertise in.

      This allows us to tailor the final outcomes , and ultimately make all outcomes relative to a chosen method. Milling, and specifically three axis milling, was deemed to be an area of expertise that we have within the company. Not only this, but we thought that with three axis milling, you can gain a complex enough geometry while still maintaining some speed and cost relativity.

      So the other key advancement that really allowed us to succeed in this project is the advancements in terms of solar sophistication and even the addition of some new solvers within generative design. These allow us better load case attributes and allow us to then translate that into better representation of the real world loading. Understandably, if we have a better representation of the real world loading, we can then be confident that our outcomes are going to be better set up to cope with that once they're produced.

      The benefits of the solver are also that we could set this up as a three part solution, keeping it as an assembly. Instead of going for the monolithic approach like we did in 2018, where we could do part consolidation alongside light weighting, we kept this as three specific parts. This allowed the manufacturing to be a bit more simple, cost effective, and time effective.

      An often overlooked advancement in the generative design world has actually been the advancements to editability of the outcomes. Because we can now take all of our outcomes out and edit them within Fusion, it makes it a lot easier to make small changes to the designs. This is really valuable in terms of manufacturability, but also when you relate it to the simulation outcomes that we're going to talk about in a little bit.

      So now it's time to actually look at the outcomes from this project. In the next section, we're going to be basically comparing the original part that Lightning are currently using, the 2018 generative design iteration, and our current generative design prototype. As you can see, the existing part is simple, but effective. It's manufactured by dye casting.

      Due to the mass production of this part, that makes it both cost and time effective. The first generative design iteration from 2018 is highly optimized. We're saving about 40% of the mass of the original part. However, it's really costly to make and almost impossible, in some regions, to really ensure a reliable, repeatable manufacturing process.

      Personally, I believe that the final iteration really bridges the gap between the two prior iterations. We're saving about 10% of the weight, yet we're still actually managing to keep manufacturability and cost and time effectiveness at the forefront. Through further development and some further manual edits, I really believe that we can better represent our trade offs and ultimately get a production part on Lightning bikes soon.

      NICK MARKOVIC: Undertaking validation studies in a virtual environment is essential in design and make workflows. Numerical validation tools are used to understand how a part or assembly performs under certain conditions. For example, simulation tools are used to calculate how loads lead to deformation or catastrophic failures. This can save time to manufacture.

      The user can experiment with virtual design variations or changing the design requirements. This can save costs by minimizing physical prototyping and physical experiments. Fusion 360 has a large simulation portfolio, and it's growing.

      All simulation studies can be run in the cloud at an affordable rate. Also, this is a great opportunity to share these results to the Lightning folks on the cloud without the need of manually sending gigabytes of result files. In this discussion, I will perform structural stress analysis using Inventor Nastran to understand if the new generative design swing arm that Peter has created is structurally sound.

      The swing arm assembly can be tricky to simulate, as there are plenty of moving and rotating parts. However, Lightning have simplified the load cases for us to perform rapid prototyping and validation. The rear wheel assembly was idealized as a rigid body element. Also we simplified the rear shock assembly by using a combination of simplified solid bodies and rigid body elements.

      Lightening has also provided three load cases. The first load case represents the vertical load acting on the swing arm during acceleration and cruise condition. The second load case represents an oblique load acting on the swing arm during the quiet cornering at an angle of 40 degrees. The final load case represents a torsion load acting on the swing arm.

      The end plates and center components were bonded using automatic contact function. And these bodies were adequately meshed using parabolic solid elements to enhance the result accuracy.

      The final element analysis works by breaking down a real life object into a large number of elements. And mathematical equations are used to predict the behavior of each element and grid points.

      The computer combines all of the visual element behaviors to predict the behavior of the global assembly. Now the simulation [INAUDIBLE] has been completed. The final step is to run a series of linear static simulations to calculate the maximum deflections and the maximum stresses that each load bears.

      Also, a factor of safety is calculated. This is denoted as FoS. Mechanical engineers commonly use this factor to assess if the stresses are above or below the material strength limits. The main objective is to ensure the factor of safety is greater or equal to unity. Once the cloud solves are finished, the results are automatically uploaded to the simulation of for post-processing.

      This graphical user interface is what we use in the simulation results environments. For multiple lood cases, the user can open different window layouts where the result comparisons are easily made. This [INAUDIBLE] shows the factor of safety distribution. In simple terms, blue collar means the region's low stress and the warmer colors indicate the part is well stressed. The red color indicates the stress is close to the design limits, and this is what we need to avoid.

      The next step is to find the critical factor of safety. And understand what is real and what is an artifact created from the simulation. You can see that load case three is the bounded load case because it has the lowest factor of safety value.

      We can also view the global deformations when the swing arm is loaded. The displacements were exaggerated to understand the global structural behavior. I always use this as a sanity check to see if the model is behaving correctly.

      This is the most exciting bit. I have summarized the key results as a radar diagram for each design variant, where the model setups are identical. This analytical shows the maximum displacements for each load case.

      Ideally, we would like the radar to be as small as possible, as this will create the stiffest swing arm and thus increasing the overall ride performance. We can see the latest generative design model is much safer than the previous generative design model. This demonstrates how much generative design technology has improved since 2018.

      Last but not least, this radar shows the minimum factual safety for each load cased. The red dotted triangle region indicates the factor of safety is less than 1. And therefore, this means the design fails to meet the criteria.

      However, the latest generative design triangle points are outside the red perimeter. And therefore this meets the design criteria. However, the 2018 version fails to meet the latest requirements for load cases one and three.

      This concludes the simulation chapter. And I'll pass it back to Peter and Richard to close the show.

      PETER SIMPSON: Thanks for that Nick. Ultimately, I think we can really take a lot from this project. And I think a lot of these can be categorized into three key takeaways. Takeaway number one is that the constant advancements within generative design mean that we can really reimagine what is possible with this software. The second takeaway is all to do with the benefits of collaboration within Fusion 360.

      Due to it being cloud based, there's no longer a need to send documents back and forth. This really allows people to work in real time collaboratively in between different teams and even between different companies. The final takeaway is to do with Fusion 360 once again.

      The ability for us to complete the whole design and make workflow within one system was really invaluable for this project. Next, I'm going to hand it over to Richard to let their next steps in order for Lightning to take this part to production.

      RICHARD HATFIELD: Thanks, Peter. Our current priority at Lightning is to scale our production to meet the demand for electric motorcycles. And our goal was to see hundreds and thousands of Lightning bikes on the street everywhere around the world. We really want to develop the products, make the products that push the transition from fossil fuels to renewable energy. And our goal at Lightning is contributing to making the world a better place. And doing that one electric motorcycle at a time.

      ______
      icon-svg-close-thick

      Cookie 首选项

      您的隐私对我们非常重要,为您提供出色的体验是我们的责任。为了帮助自定义信息和构建应用程序,我们会收集有关您如何使用此站点的数据。

      我们是否可以收集并使用您的数据?

      详细了解我们使用的第三方服务以及我们的隐私声明

      绝对必要 – 我们的网站正常运行并为您提供服务所必需的

      通过这些 Cookie,我们可以记录您的偏好或登录信息,响应您的请求或完成购物车中物品或服务的订购。

      改善您的体验 – 使我们能够为您展示与您相关的内容

      通过这些 Cookie,我们可以提供增强的功能和个性化服务。可能由我们或第三方提供商进行设置,我们会利用其服务为您提供定制的信息和体验。如果您不允许使用这些 Cookie,可能会无法使用某些或全部服务。

      定制您的广告 – 允许我们为您提供针对性的广告

      这些 Cookie 会根据您的活动和兴趣收集有关您的数据,以便向您显示相关广告并跟踪其效果。通过收集这些数据,我们可以更有针对性地向您显示与您的兴趣相关的广告。如果您不允许使用这些 Cookie,您看到的广告将缺乏针对性。

      icon-svg-close-thick

      第三方服务

      详细了解每个类别中我们所用的第三方服务,以及我们如何使用所收集的与您的网络活动相关的数据。

      icon-svg-hide-thick

      icon-svg-show-thick

      绝对必要 – 我们的网站正常运行并为您提供服务所必需的

      Qualtrics
      我们通过 Qualtrics 借助调查或联机表单获得您的反馈。您可能会被随机选定参与某项调查,或者您可以主动向我们提供反馈。填写调查之前,我们将收集数据以更好地了解您所执行的操作。这有助于我们解决您可能遇到的问题。. Qualtrics 隐私政策
      Akamai mPulse
      我们通过 Akamai mPulse 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Akamai mPulse 隐私政策
      Digital River
      我们通过 Digital River 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Digital River 隐私政策
      Dynatrace
      我们通过 Dynatrace 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Dynatrace 隐私政策
      Khoros
      我们通过 Khoros 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Khoros 隐私政策
      Launch Darkly
      我们通过 Launch Darkly 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Launch Darkly 隐私政策
      New Relic
      我们通过 New Relic 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. New Relic 隐私政策
      Salesforce Live Agent
      我们通过 Salesforce Live Agent 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Salesforce Live Agent 隐私政策
      Wistia
      我们通过 Wistia 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Wistia 隐私政策
      Tealium
      我们通过 Tealium 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Tealium 隐私政策
      Upsellit
      我们通过 Upsellit 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Upsellit 隐私政策
      CJ Affiliates
      我们通过 CJ Affiliates 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. CJ Affiliates 隐私政策
      Commission Factory
      我们通过 Commission Factory 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Commission Factory 隐私政策
      Google Analytics (Strictly Necessary)
      我们通过 Google Analytics (Strictly Necessary) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Strictly Necessary) 隐私政策
      Typepad Stats
      我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
      Geo Targetly
      我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
      SpeedCurve
      我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
      Qualified
      Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

      icon-svg-hide-thick

      icon-svg-show-thick

      改善您的体验 – 使我们能够为您展示与您相关的内容

      Google Optimize
      我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
      ClickTale
      我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
      OneSignal
      我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
      Optimizely
      我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
      Amplitude
      我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
      Snowplow
      我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
      UserVoice
      我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
      Clearbit
      Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
      YouTube
      YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

      icon-svg-hide-thick

      icon-svg-show-thick

      定制您的广告 – 允许我们为您提供针对性的广告

      Adobe Analytics
      我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
      Google Analytics (Web Analytics)
      我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
      AdWords
      我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
      Marketo
      我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
      Doubleclick
      我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
      HubSpot
      我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
      Twitter
      我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
      Facebook
      我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
      LinkedIn
      我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
      Yahoo! Japan
      我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
      Naver
      我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
      Quantcast
      我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
      Call Tracking
      我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
      Wunderkind
      我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
      ADC Media
      我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
      AgrantSEM
      我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
      Bidtellect
      我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
      Bing
      我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
      G2Crowd
      我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
      NMPI Display
      我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
      VK
      我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
      Adobe Target
      我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
      Google Analytics (Advertising)
      我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
      Trendkite
      我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
      Hotjar
      我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
      6 Sense
      我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
      Terminus
      我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
      StackAdapt
      我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
      The Trade Desk
      我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
      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

      是否确定要简化联机体验?

      我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

      个性化您的体验,选择由您来做。

      我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

      我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

      通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。