Ford Motor Company is exploring how large one-piece auto parts, like car spoilers, could be printed for prototyping and future production vehicles, as the first automaker to pilot the Stratasys Infinite Build 3D printer.
Capable of printing car parts of practically any shape or length, the Stratasys Infinite Build system could be a breakthrough for vehicle manufacturing, providing a more efficient and affordable way to produce tooling, prototype parts, or components at low volumes, like Ford Performance vehicles, or for personalized car parts.
“With the Infinite Build technology, we are now able to print large tools, fixtures, and components, making us more nimble in design iterations,” said Ellen Lee, Ford technical leader for additive manufacturing research. “We’re excited to have early access to Stratasys’ new technology in order to help steer the development of large scale printing for automotive applications and requirements.”
The new 3D print system is located at Ford’s Research and Innovation Center in Dearborn.
An emerging technology for manufacturing
As 3D printing becomes increasingly efficient and affordable, companies are employing this emerging technology for manufacturing applications in everything from aerospace, to education, to medicine. Wider adoption in 3D printing has been driven by recent technology advances, new areas of application and government support, according to Global Industry Analysts Inc. By 2020, the global market for 3D printing is expected to reach $9.6 billion, the organization reported.
In the future, 3D printing could have immense benefits for automotive production, including the ability to produce lighter-weight parts, which may help improve fuel efficiency. A 3D-printed spoiler, for instance, may weigh less than half of its metal-cast equivalent.
Additionally, 3D printing is a more cost-efficient way to produce parts only needed at low volumes, like prototypes and specialized parts for racecars. Further, Ford also may use the technology to make larger printed tooling and fixtures as well as personalized components for customers.
How it works
Specifications for the part are transferred from the computer-aided design program to the printer’s computer, which analyzes the design. Then, the device goes to work, printing one layer of material at a time – in this case, plastic – and then gradually stacking the layers into a finished 3D object.
When the system detects that the raw material or supply material canister is empty, a robotic arm automatically replaces it with a full canister. This allows the printer to operate for hours or days while unattended.
Benefits of 3D printing
Though 3D printing isn’t yet fast enough for high-volume production manufacturing, it is a more cost-efficient way to produce parts only needed at low volumes, like prototypes and specialized parts for racecars. In addition, when not limited by the constraints of mass production processes, components can be designed to function more efficiently.
Using traditional methods, an engineer would create a computer model of the part and wait for months for prototype tooling to be produced. With 3D printing, Ford can print the same part in days at a significantly reduced cost. For example, a prototype for a new intake manifold could be produced over a couple of days as opposed to several months, at an order of magnitude lower cost.
Project Bloodhound saved
The British project to break the world landspeed record at a site in the Northern Cape has been saved by a new backer, after it went into bankruptcy proceedings in October.
Two weeks ago, and two months after entering voluntary administration, the Bloodhound Programme Limited announced it was shutting down. This week it announced that its assets, including the Bloodhound Supersonic Car (SSC), had been acquired by an enthusiastic – and wealthy – supporter.
“We are absolutely delighted that on Monday 17th December, the business and assets were bought, allowing the Project to continue,” the team said in a statement.
“The acquisition was made by Yorkshire-based entrepreneur Ian Warhurst. Ian is a mechanical engineer by training, with a strong background in managing a highly successful business in the automotive engineering sector, so he will bring a lot of expertise to the Project.”
Warhurst and his family, says the team, have been enthusiastic Bloodhound supporters for many years, and this inspired his new involvement with the Project.
“I am delighted to have been able to safeguard the business and assets preventing the project breakup,” he said. “I know how important it is to inspire young people about science, technology, engineering and maths, and I want to ensure Bloodhound can continue doing that into the future.
“It’s clear how much this unique British project means to people and I have been overwhelmed by the messages of thanks I have received in the last few days.”
The record attempt was due to be made late next year at Hakskeen Pan in the Kalahari Desert, where retired pilot Andy Green planned to beat the 1228km/h land-speed record he set in the United States in 1997. The target is for Bloodhound to become the first car to reach 1000mph (1610km/h). A track 19km long and 500 metres wide has been prepared, with members of the local community hired to clear 16 000 tons of rock and stone to smooth the surface.
The team said in its announcement this week: “Although it has been a frustrating few months for Bloodhound, we are thrilled that Ian has saved Bloodhound SSC from closure for the country and the many supporters around the world who have been inspired by the Project. We now have a lot of planning to do for 2019 and beyond.”
Motor Racing meets Machine Learning
The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK
The car of tomorrow, most of us imagine, is being built by the great automobile manufacturers of the world. More and more, however, we are seeing information technology companies joining the race to power the autonomous vehicle future.
Last year, chip-maker Intel paid $15.3-billion to acquire Israeli company Mobileye, a leader in computer vision for autonomous driving technology. Google’s autonomous taxi division, Waymo, has been valued at $45-billion.
Now there’s a new name to add to the roster of technology giants driving the future.
Amazon Web Services, the world’s biggest cloud computing service and a subsidiary of Amazon.com, last month unveiled a scale model autonomous racing car for developers to build new artificial intelligence applications. Almost in the same breath, at its annual re:Invent conference in Las Vegas, it showcased the work being done with machine learning in Formula 1 racing.
AWS DeepRacer is a 1/18th scale fully autonomous race car, designed to incorporate the features and behaviour of a full-sized vehicle. It boasts all-wheel drive, monster truck tires, an HD video camera, and on-board computing power. In short, everything a kid would want of a self-driving toy car.
But then, it also adds everything a developer would need to make the car autonomous in ways that, for now, can only be imagined. It uses a new form of machine learning (ML), the technology that allows computer systems to improve their functions progressively as they receive feedback from their activities. ML is at the heart of artificial intelligence (AI), and will be core to autonomous, self-driving vehicles.
AWS has taken ML a step further, with an approach called reinforcement learning. This allows for quicker development of ML models and applications, and DeepRacer is designed to allow developers to experiment with and hone their skill in this area. It is built on top of another AWS platform, called Amazon SageMaker, which enables developers and data scientists to build, train, and deploy machine learning quickly and easily.
Along with DeepRacer, AWS also announced the DeepRacer League, the world’s first global autonomous racing league, open to anyone who orders the scale model from AWS.
As if to prove that DeepRacer is not just a quirky entry into the world of motor racing, AWS also showcased the work it is doing with the Formula One Group. Ross Brawn, Formula 1’s managing director of Motor Sports, joined AWS CEO Andy Jassy during the keynote address at the re:Invent conference, to demonstrate how motor racing meets machine learning.
“More than a million data points a second are transmitted between car and team during a Formula 1 race,” he said. “From this data, we can make predictions about what we expect to happen in a wheel-to-wheel situation, overtaking advantage, and pit stop advantage. ML can help us apply a proper analysis of a situation, and also bring it to fans.
“Formula 1 is a complete team contest. If you look at a video of tyre-changing in a pit stop – it takes 1.6 seconds to change four wheels and tyres – blink and you will miss it. Imagine the training that goes into it? It’s also a contest of innovative minds.”
Formula 1 racing has more than 500 million global fans and generated $1.8 billion in revenue in 2017. As a result, there are massive demands on performance, analysis and information.
During a race, up to 120 sensors on each car generate up to 3GB of data and 1 500 data points – every second. It is impossible to analyse this data on the fly without an ML platform like Amazon SageMaker. It has a further advantage: the data scientists are able to incorporate 65 years of historical race data to compare performance, make predictions, and provide insights into the teams’ and drivers’ split-second decisions and strategies.
This means Formula 1 can pinpoint how a driver is performing and whether or not drivers have pushed themselves over the limit.
“By leveraging Amazon SageMaker and AWS’s machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time,” said Pete Samara, director of innovation and digital technology at Formula 1.