Much of the innovation in manufacturing will soon become synonymous with the Fourth Industrial Revolution (4IR), and the impact that robotics, artificial intelligence and the Internet of Things will have on production processes, and possible job losses. There is no doubt that these impacts will be profound—but they need not be negative.
As regards job cuts; while there is no denying that many unskilled jobs will ultimately be at threat, in general 4IR is likely to produce more jobs than it destroys—but they will be different types of job. The trick here will be to develop clear strategies for helping existing work forces make the transition, bearing in mind that there are likely to be more than one. It will be necessary to identify those that can adapt frequently to become part of a “liquid” workforce.
Learning to work alongside, and “manage”, robots is likely to be one of the immediate challenges for workers on production lines.
But perhaps more exciting is the impact that machine learning and artificial intelligence, combined with the expanded range of capabilities that sophisticated robotics will bring, will have on the design and production of new products. The R&D teams at leading tyre manufacturers like Bridgestone are continuously working on new ideas that the emerging 4IR technologies will make possible, and there is no doubt that current innovation initiatives will gain strength.
One of the ongoing challenges in the tyre world is to design and then produce tyres better suited to driving conditions. One example would be a tyre that would enable motorists in geographical areas experiencing a wide range of climatic conditions to use the same tyre the whole year round. At present, in certain parts of Europe and the North America, it is necessary to change tyres in winter to cope with snow and icy. The quest is on for a tyre designed to cope with all weather conditions, and very likely made of a new material.
Another goal is a low rolling resistance tyre; that is, one that requires less energy to roll and thus is more fuel efficient. These are already on the market, but the pressure to reduce energy use in an effort to boost sustainability is unremitting. In similar vein, tyre companies’ R&D teams are continuously searching for new ways to recycle tyres, and to find new materials, that will reduce environmental damage.
Then, of course, there is the impact of electric and driverless vehicles, which will require totally new approaches to tyre design. For example, how will a driverless vehicle change a flat or damaged tyre? Could 3-D printing be used to effect repairs or even, in true futuristic mode, reconfigure tyre tread for a change in terrain, based on alerts from the satellite navigation system?
When it comes to tyres for electric vehicles, a whole new set of requirements, some apparently contradictory, come into play. Low rolling resistance is one, but so is high torque at low speeds, not to forget high load capability to accommodate the weight of large batteries. The lighter vehicles and virtually soundless engines will mean that reducing tyre noise in urban settings will become critical.
Another concept is “tyres as a service”. This would combine the smart car concept with an array of sensors to enable continuous monitoring of tyre condition and pressure, in conjunction with the general condition of the car. The end goal would be that motorists would never buy a tyre again, but rather subscribe to a service that fitted tyres, monitored them continuously, was on hand to make repairs as needed and fit new tyres at the correct time—all for a monthly fee.
These are just some of the innovations in the pipeline or on the drawing board, and the 4IR will contribute to making them a reality, and to inspiring more. And, to return to where we began, the other side of the coin is that these and other innovations will need to the kind of smart factories that 4IR will enable. In all cases, as noted, the key will be to prepare today’s work forces to make the transition.
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.