Some of the most debated yet still least known issues within manufacturing today are Industry 4.0, IIoT, and digitization.
With a lot of uncertainty already around Industry 4.0, the main innovations driving Industry 4.0 and real-world implementations will be discussed today.
Industry 4.0 signals a revolution in the conventional world of development. Industry 4.0, also referred to as the Fourth Industrial Revolution, involves three technical developments pushing this change: communication, knowledge, and flexible automation.
Industry 4.0 merges IT (Information Technology) and OT (Operational Technology) to build a cyber-physical environment.
This integration has been made possible by the advent of digital solutions and sophisticated technology related to Industry 4.0.0. They include:
- Industrial Internet of Things
- Big Data
- Cloud computing
- Additive manufacturing (AM)
- Advanced robotics
- Augmented and virtual reality (AR/VR)
These technologies help accelerate the digital transformation of manufacturing by incorporating historically fragmented systems and processes around the value and supply chain through integrated computing systems.
Embracing Industry 4.0, modern production, and the interconnectivity that comes with it offers enterprises various advantages, including increased mobility, stability, and accessibility.
Industrial Internet of Things
The Internet of Things is at the center of Industry 4.0 (IoT).
Placed IoT refers to a network of digitally linked physical objects, enabling the collaboration and sharing of data through the Internet. From smartphones and kitchen equipment to vehicles and even homes, these intelligent machines may be anything.
For example, it is possible to use IIoT to avoid inventory from being overstocked or understocked.
The use of shelf-fitted sensors and measuring systems to relay inventory information to your warehouse management system is one way to do this. Putting such a system in place helps warehouse managers to track inventory levels, thereby having access and control of the inventory in real-time.
Big Data and Analytics
Big Data refers to the broad and complicated data sets that IoT devices produce. This knowledge comes in numerous formats and protocols from the various cloud and business software, websites, computers, sensors, cameras, and much more.
There are several different forms of data to remember in the industrial sector, including data from production machines equipped with sensors and databases from ERP, CRM, and MES networks.
With the interpretation of results.
Where it comes to information, it is important to use data analytics to turn data into information that can offer actionable insights.
Data analytics processes can be aided by deep learning algorithms and data visualization. Machine learning methods, generally speaking, apply powerful computing algorithms to process large data sets, while data visualization tools enable manufacturers to understand the tale the data tells more easily.
Ultimately, enterprises can also discover new ways to refine the systems that have the biggest effect on yield by taking previously separate data sets, compiling and evaluating them.
Producers have been capturing and preserving data for decades to optimize operations.
With the introduction of IoT and Industry 4.0, however, the fact is that data is produced at a staggering pace and high volumes, rendering manual handling impossible. This provides a need for an architecture that can more easily store and handle this information.
Cloud computing is where this comes in.
Cloud computing is a network for remote servers for customers to store and process large volumes of data. It encourages companies to use computational services without the on-site construction of a computing system.
The term cloud storage refers to information stored in the “cloud” accessible over the Internet remotely. Cloud computing is not a solution of its own but allows other solutions to be applied, which once demanded heavy computing power.
Cloud computing’s potential to offer flexible computing services and storage capacity helps enterprises, through the use of big data analytics, to gather and implement business intelligence, allowing them to simplify and streamline development and business processes.
Although robotics has been used for decades in engineering, Industry 4.0 has brought this technology new life.
A new breed of autonomous robots is emerging with recent developments in technology, capable of performing complex and delicate tasks. They will identify, analyze and respond to the input they obtain from the world and collaborate and learn from humans, guided by cutting-edge software and sensors.
Collaborative robots (‘cobots’), built to operate safely with humans, liberating workers from tedious and hazardous activities, are one field of robotics gaining considerable momentum.
The main technology pushing Industry 4.0. is additive manufacturing, or 3D printing, alongside robots and intelligent systems. Additive manufacturing works by making components layer by layer using a 3D printer using digital 3D models.
3D printing is evolving as a valuable new production tool within the framework of Industry 4.0. Once just a quick prototyping technology, AM today provides a wide spectrum of manufacturing possibilities in nearly all industries, from tooling to mass customization.
It allows parts to be stored in virtual inventories as design files to be produced on-demand and closer to the point of need, a model known as distributed development.
Such a decentralized development strategy may minimize transport distances and thus expenses and optimize inventory control by maintaining computer files rather than physical components.
For improving the efficiency and repair of manufacturing devices, the idea of a digital twin holds great promise. Gartner, a multinational consulting company, expects that 50 percent of major manufacturing organizations will use digital twins to map and manage their properties and processes by 2021.
A digital twin is a digital version of a real-world product, computer, process, or device that, by real-time simulation, helps businesses to interpret better, evaluate and refine their processes.
Although it is possible to confuse digital twins with modeling used in computing, there is even more to this theory.
A digital twin performs an online simulation based on data received from sensors attached to a computer or other unit, unlike engineering simulations.
A digital twin can continually capture this data. Simultaneously, an IIoT computer transfers data nearly in real-time, preserving the fidelity with the original over the life of the product or machine.
This helps the digital twin to anticipate future problems such that it is possible to take preemptive steps. For example, a digital twin may be used by an operator to determine whether a component is malfunctioning or to estimate a product’s lifespan. This constant simulation aims to enhance model designs as well as to ensure uptime for equipment.
This use of digital twins in challenging aerospace, heavy machinery, and automotive applications has long been an important instrument. The principle of digital twinning is now being extended through other fields through developments in computer science, machine learning, and sensors.
The manufacturing industry is only starting to explore the advantages of Augmented Reality (AR) technologies, considering their uptake in consumer applications. And still, from assisting with fabrication procedures to managing production facilities, there is a tremendous untapped opportunity for the technology.
Through superimposing visual images or data onto a tangible entity, augmented reality fills the divide between the digital and physical realms. The technology uses AR-capable equipment for this, such as smartphones, tablets, and smart glasses.
For example, let’s take a medical example: a surgeon uses AR glasses during a surgical procedure. The glasses could overlay patient details from MRI and CT scans of the patient, such as muscles, large blood vessels, and ducts, and highlight them in color. This allows the surgeon to find the best route to the intrusive area, minimizing the chance of complications and optimizing the procedure.
AR may allow staff to speed up the assembly process in the sense of manufacturing and facilitate decision-making. For example, AR glasses may be used to project data on the actual part, such as layouts, configuration instructions, potential fault locations, or a serial number of parts, enabling simpler and simpler work procedures.
Riding the Wave of Digital Manufacturing
Now is an interesting time for the automotive industry with new, digital technology emerging. The wave of emerging technology opens up possibilities for firms to take action towards greater resilience, efficiency, and sustainability. Industry 4.0 also makes it possible for people and computers to work together in new ways, enabling companies to obtain deeper insights, reduce the likelihood of mistakes, and make smarter choices.
Industry 4.0 is eventually expected to take hold in the industrial ecosystem. Yet manufacturers can remain on the leading edge of this modern digital age only by recognizing and harnessing the innovations that propel Industry 4.0.