What is Industry 4.0 & What are its key technologies? Everything you need to know.

We are in the midst of a manufacturing evolution. It’s the latest in a history of industrial revolutions dating back over 300 years, each defined by technological advancements that have transformed how we manufacture goods.

Broadly speaking, we can define the revolutions in terms of a few key concepts and technologies: 

1. The First Industrial Revolution (1760-1840): Mechanization & Steam Power
2. The Second Industrial Revolution (1870-1914): Mass Production & Electricity 
3. The Third Industrial Revolution(1950-2000): Automation and Computerization 

And right now it’s happening again, for a fourth time. and it will be driven by data.

Also known as " industry 4.0"  & it refers to the fourth industrial revolution that’s happening all around us and includes cutting-edge technologies like additive manufacturing, artificial intelligence, augmented and virtual reality, and the Internet of Things.

Additive Manufacturing

How do you go from concept to prototype within hours instead of weeks?

Additive manufacturing (AM) is a disruptive and rapid growing technology that allows designers and engineers to create prototypes in a fraction of the time. While impacting several industries across healthcare, aerospace and robotics, it is particularly changing the game for automotive manufacturing. 

The ability to manufacture, test and customize automotive parts is giving rise to automotive innovations with the freedom of design all over the world.

It enables us to make parts that would be otherwise impossible to produce with traditional (i.e, “subtractive”) manufacturing processes, either through by part consolidation—a method famously employed by GE in the manufacturing of the LEAP engine fuel nozzles—or through new geometries, like those found in conformal cooling systems. Additive manufacturing can even lead to a new type of factory: a micro factory. Local Motors is pioneering the AM revolution in automotive, with micro factories dedicated to producing vehicles locally through additive manufacturing techniques.

Like Industry 4.0 technologies more generally, Additive manufacturing can improve processes throughout the full product life cycle. 

In the US, 80 to 90 percent of each initial prototype has been 3D printed among several major automakers. After deployment of a vehicle, this technology can proactively reduce disruptions in the supply chain by replacing faulty parts without costly wait times otherwise experienced with third party suppliers.

Moreover, customers now have the ability to make their dream cars come to life.

Although air intakes, exhaust system parts and ducting are some of the most popular components to be printed, many customs parts such as brackets, spacers and grommets are becoming more commonly desired to satisfy unique requests from customers.

Additive manufacturing gives customers and automakers the tools to enhance their individual creativity. In turn fueling the quest for automakers to out-innovate one another. With major OEM like BMW and Vollkwagen printing thousands of parts a year in their designated additive manufacturing centers, AM’s advantage creates a battlefield with unlimited possibilities for them and their competitors to discover.

Virtual and Augmented Reality

When thinking about virtual reality (VR), like most, my thoughts gravitate to gaming. however games don’t capture the full potential of this revolutionary technology. 

Additive manufacturing is not the only tool that has impacted the world of prototyping for innovators.

Similar to the way in which a video game console with motion control functions, Mercedez-Benz has implemented a “virtual assembly station” that allows designers to perform virtual assembly of parts and test out different situations that can arise with an avatar of their choosing.

VR can also prevent late detection of design errors, a major predicament in developing a new car model. The ability to simulate prototypes in terms of their volume and size and get a detailed view of how all the parts are connected together is transforming innovation before engineers can even pick up a tool to start building.

Audi is one of the major OEM that applied this tech with their VR “holodeck” which is a virtual environment that houses a 3D image of a car. The holodeck gives engineers the ability to visualize a realistic idea of a new vehicle model in the early stages of conception, in turn saving time and costs by reducing physical testing efforts during development stages of the product.

Imagine test driving a car without driving it off the lot.

The VR experience takes place in a simulated isolated reality. For car retailers this means they can reduce showroom size, in turn significantly cutting costs of rent, inventory and salaries, while also enhancing customer experience. 

Customers have the ability to change a car’s configuration and aesthetics in a matter of seconds. This flexibility is but one reason why Audi has deployed more than 1000 VR showrooms and are planning on expanding.

On the other hand, augmented reality (AR) technology combines digital creations with that from the physical world. Users are able to see existing environments overlaid by computer generated images.

Augmented Reality enables advanced driver assistance systems to help drivers stay more focused.

These days, we rely predominantly on our cell phones for GPS in the car and it’s hard not to get distracted when a message pops on the screen. With augmented reality head-up display (HUD), data such as warning signals, speed, navigation and much more can be displayed right on your windshield perceived by drivers as a part of the road. Hyundai and WayRay have released the world’s first Holographic AR Navigation System and it looks like it is straight out of a sci-fi movie.

Internet of Things

From cellphones, TVs and lamps to airplane engines and oil rig drills – the Internet of things (IoT) can connect it all. If it has an on and off switch, chances are this technology can be used to connect devices to the Internet and to each other to facilitate the sharing of data.

IoT is notably transformative for the automotive and manufacturing industry and this can be attributed to the idea of “connected vehicles” and “smart factories”. These ideas employ a network of sensors to collect data and use cloud software to transform it into valuable and actionable insights about productivity and safety.

Product development and delivery is optimized in various areas within manufacturing plants with IoT technologies.

Sensors in machines are empowered with the ability to measure and reduce excess energy consumption, in turn lowering costs and fueling environmentally sustainable operations. Operations are further improved with better asset tracking. IoT technology is able to alert manufacturers when stock is low allowing for automation in the inventory systems, as well identify potential equipment failures to decrease unexpected downtime.

With connected vehicles, safety on the road will greatly be improved by both detecting accidents and bad driving.

Your car will be able to automatically detect collisions and immediately contact emergency services. The same tech can also monitor driving habits and send recommendations to drivers so they can continuously improve on their skills.

IoT will help control traffic like never before, making morning commutes hassle free.

While preventing accidents will lead to far less congestion, IoT can also be used for swarm intelligence in traffic, allowing traffic operators to coordinate cars to reduce common chokepoints when roads are busiest during the day.

Along with efficiency of driving conditions on the road, IoT can also improve the roads themselves. Building roads that can detect maintenance needs to ensure they are not left in poor conditions for extended periods of time. 

Artificial Intelligence

The automotive industry has a history of harnessing the newest technologies to bring safe, efficient, and innovative vehicles to the market. It’s no surprise that, as a result, AI has been playing a huge role in the industry and will continue to do so for years to come. 

AI is fueling a new world of transportation where driver seats in cars remain empty.

The most talked about innovation is in autonomous vehicles – the concept of utilizing AI such that drivers are not required. Cars would be able to use AI to drive themselves thereby alongside IoT technologies, reducing accidents and traffic congestion.

The optimizations enabled by AI start right on the factory floor and extend far beyond it onto the road.

Predictive maintenance of vehicle data can alert manufacturers of potential failure points – it can be used to identify defects in parts and make adjustments quickly, with one of the biggest advantages being cost containment.

Safety on the road is of utmost importance, along with on-road vehicle failures, reckless driving being one the greatest threats and AI gives us the ability to greatly minimize this risk.

Driver monitoring allows for vehicles to determine when a driver is, for instance, falling asleep at the wheel and then assist the driver to stay awake. In case of an accident, AI can also adjust airbags to account for the driver’s specific body orientation to provide a cushiony landing.

Now that you’ve gotten a peek into the 4 technologies that are driving this modern day industrial revolution, what do you think will define the next wave of technological advancement?

The Puzzling Search for Perfect Randomness

Does objective, perfect randomness exist, or is randomness merely a product of our ignorance?

Life is unpredictable, and random things happen to us all the time. You might say the universe itself is random. Yet somehow, large numbers of random events can generate large-scale patterns that science can predict accurately. Heat diffusion and Brownian motion are just two examples.

Recently, randomness has even made the news: Apparently there’s hidden order in random surfaces, and we may be close to seeing a quantum computer generate ultimate randomness. This latter quest for perfect randomness is important because randomness brings unpredictability, and all non-quantum attempts to achieve it have the hidden flaw of being generated by algorithmic methods which can, theoretically, be deciphered. In this Insights column, we will explore how we can create randomness and defeat it in everyday activities, before soaring to philosophical heights in debating what randomness really is.

Puzzle 1: Random Combinations

Consider a simple combination bike lock like the one shown in the picture. It has three rotating discs, each of which has 10 digits embedded in numerical order. When the three discs are rotated to produce the set combination — 924 — the lock opens. When you want to lock it again, you have to scramble the digits so that they are far away from the set combination. But what does far away mean in this context? If you move each disc the maximum amount, which is five positions away, you get the number 479. But it would be easy for a tinkerer to find this position by accident, simply by moving all five discs in sync and seeing if the lock opened. Imagine that the tinkerer has enough time to try five possible combinations. In each instance, our potential thief tries out the lock after doing each of the following operations:

1. Turning a single disc a random amount.
2. Grabbing hold of any two discs and turning them some random amount in sync.
3. Grabbing hold of all three discs and turning them some random amount in sync.
4. Turning two discs different amounts.
5. Turning all three discs different amounts.

Our puzzle question is: If the code to open the lock is 924, what set of scrambled numbers is most resistant to this random tinkering, and how many such combinations are there? What’s the probability that the code will be found?

Puzzle 2: From Randomness to Order in Puzzles

I have often been struck by how similar solving any puzzle is to the process of science. We progress from randomness to order by adding pieces, and our confidence in the rightness of our solution is bolstered by every new piece that fits. In this second problem we will try to create a way to measure our progress as we go from a random disorderly state to a finished, orderly solution.

Consider solving a puzzle on a hexagonal grid like Graphene click to know more about graphene. The picture on the puzzle consists of a twisting vine. Since the pattern is repeating and self-similar, you cannot be absolutely sure that two neighboring pieces belong together even though they seem to fit visually. In fact, let’s say that for each edge of a given piece, there are three possible pieces that could fit with it. So when two pieces fit together, your confidence that their arrangement is correct can only be 33.33%. However, if you can find another piece that fits with both of your connected pieces, sharing an edge with each, your confidence that this arrangement is correct will be reinforced. Let’s try to quantify how much.

1. You find three pieces that appear to fit together without obvious misalignment in the vine pattern across the shared edges. What is the measure of your confidence that this arrangement is correct?
2. You find a central hexagonal piece surrounded by six others, and they all seem to fit with one another. What’s the measure of your confidence in the correctness of this pattern?

As your clump of pieces gets larger, your confidence in it should become more unshakable. It is reasonable to assume that three isolated clumps comprising a total of seven joined pieces are no match, in terms of the confidence they inspire, to a single surrounded hexagon as describe.

The third part of this puzzle question is open ended and is an attempt to quantify the above difference. Can you come up with a measure of the degree of completeness of a partially solved puzzle? Your method should be able to assign a number between 0 and 100 to any partially completed 10-by-10 hexagonal puzzle. The number should represent a degree of completeness that roughly correlates with the proportion of the final solution that you would expect to be correctly represented thus far.

Puzzle 3: Is Perfect Randomness Possible?

For part three of this puzzle, I give you the randomness version of the famous Bohr-Einstein debates. Everyone is welcome to join in. You can join either team E (Einstein) or team B (Bohr).

In the macro world, both teams agree that mechanisms that generate randomness are only possible because of our ignorance of the forces or algorithms that drive them. If you knew all the forces that were acting on a flipped coin or a rolled die, you could, with the requisite computing power, predict what the final outcome would be. We have been trained to believe, according to the prevailing team B view, that this is not true in the quantum world — quantum probabilities are supposed to be objective. But is such a thing even possible? Could there not be some mechanism somewhere in the ensembles of the subquantal Planckian world that decides which of two equally likely outcomes will take place, even though we may forever lack the ability to explore that level? Even if Einstein’s nightmare vision of a deity playing dice is true, there must be an algorithm in the deity’s brain that decides every choice, no matter how whimsical or devoid of apparent reason it seems to be. Again, the randomness is due to our ignorance. It is only practically unknown, not objectively random.

The standard reply to this by team B is to say that the quantum world is just too weird to apply the rules we have inferred from our experience of the macro world. But something can be weird in two ways. It could have physical impossibilities such as faster-than-light travel, for example. This kind of weirdness could exist, it just means that we need to revise our understanding of physical law in specific circumstances, just as Einstein revised Newton’s law of addition of velocities, which becomes inaccurate at very high values.

On the other hand, something could be weird because it has logical impossibilities, such as 2 + 2 equaling 5. Such a result is impossible in any conceivable universe. Team E would argue that perfect randomness and objective probabilities are logical impossibilities. We should not accept them, but instead try to find physical mechanisms that can explain the observed results, no matter what current physical laws they break.

𝚝𝚑𝚊𝚗𝚔 𝚢𝚘𝚞.