MM: Intel’s Move Into The Autonomous Car Market; 3D Printing Tech Optimized

In this Manufacturing Minute episode, advances in 3D-printing technology and Intel’s big bet on self-driving cars.

Intel’s Fleet Of Autonomous Cars

For automakers around the world, these days it’s all about one word: automation. The development of self-driving cars and the software needed to power them has become something of a rat race in the automotive industry, with millions of dollars and man hours being invested in product design, development and testing.

And now, computer hardware company Intel is also beginning to make some strong moves into the autonomous vehicle market. In addition to recently purchasing the driverless tech company Mobileye for a tidy $15 billion, Intel also just revealed plans to develop a fully autonomous fleet of driverless vehicles. This fleet of 100 cars will serve as testers in which Intel and Mobileye will assess its all-in-one autonomous car package. This offering means that the cars will not only be able to communicate with each other, but also store data in the Cloud.

These fully autonomous SAE Level 4 vehicles are the second highest level of automation, meaning they would only require human intervention if unexpected conditions (like extreme weather) arise.

How do you think Intel’s tester fleet of self-driving cars will stack up against the competition? Tweet me your thoughts @MnetNews or comment in the section below.

Optimizing 3D Printing Materials

Advances in 3D printing technology mean that current printers can produce even small objects that could contain billions of different materials.

But researchers from the Massachusetts Institute of Technology noted that manually determining the physical properties of the vast amount of potential combinations is impossible.

So MIT's Computer Science and Artificial Intelligence Laboratory came up with a solution that could enable printer users quickly find the optimum combination for their product.

Their newly developed system catalogues the properties of a wide range of microscopic clusters in order to project how they would perform at a larger scale.

In the study, researchers randomly generated clusters combining materials in different ways, then evaluated their properties using physics simulations.

The result is assigned a point in the lab's range of properties, and an algorithm gradually fills that entire range into a cloud of potentially printable clusters.

The system means that 3D printer users could find the best materials for their projects, from identifying the maximum stiffness for a printed chair to a building functional gripper for a soft robot.

Researchers noted that it could be particularly significant for industries where mechanical properties are crucial, such as auto manufacturing or aerospace.


Could this process lead more manufacturers to embrace 3D printing? Tell us your thoughts in the comments below.

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