Autodesk has made the delivery of design tool software in the cloud a priority this last year, announcing several “projects” and serving them up in its Autodesk Labs online sandbox.
Project Neon, a rendering service announced last July, is among the newer offerings and cloud service just got an update. Project Neon leverages the power and compute capabilities of the cloud, freeing up the long wait times that engineering users typically endure when trying to translate 3-D models into photorealistic images. Customers no longer have to buy and maintain expensive hardware to handle their rendering needs nor do they have to wait long periods of time to generate single or multiple views of designs, often to prove out works-in-progress to clients.
In the latest update, Autodesk has upgraded the performance of Project Neon, doubling the size of each cluster (up to eight four-core machines are now employed) used to render each image. This boost results in a dramatic speed improvement, Autodesk officials say, in the ballpark of 2x for all rendering jobs.
Quality improvements is another area of focus. In this latest Project Neon release, new Texture Filtering capabilities eliminate some noise in certain materials such as fabrics and carpets in the rendered images.
At the Design News webinar on June 27, learn all about aluminum extrusion: designing the right shape so it costs the least, is simplest to manufacture, and best fits the application's structural requirements.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This radio show will show what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.