Advancing the capabilities of collaborative and industrial robots to make them smarter is something carried out through Machine Learning. Without an array of sensors or neural network systems, robots would be dull and blind to say the least. Their restriction to performing one task at a time would severely limit their productivity potential. This is where vision sensors and machine learning comes in, allowing robots to achieve much more than they could independently.
There is a lot of buzz in the industrial world today claiming that we’ve entered into a new era of industrial revolution, the fourth to be exact. The primary motivators behind these discussions has been the increased involvement of internet within the industry, but before we can truly declare a paradigm shift, we must understand each individual revolution.
- The First Industrial Revolution is related to the 18th century mechanized manufacturing that swept Europe, starting from Britain.
- The Second began when electricity started taking over mechanical processes and dominated mass production
- The third was highlighted by the use of electronics, robotics and IT to increase productivity in industrial and commercial centers.
- Now, while the fourth hasn’t completely taken over, it is operating in a mixed state. It involves the use of Data, AI and Predictive Analysis to boost efficiency of existing processes, and reduce dependency on manual labor. Smart Manufacturing enters at this very stage, and puts machines into the business of real-time decision making.
Data Tells Us What to Do
Smart Manufacturing relies heavily on automated processes. It involves the use of data to control processes, and carrying out activities in advance depending on the trend. So, while the smartest person in the room would still be a human, the machine would definitely assist in maximizing profits.
For instance, if we talk about aircraft engines. Conventionally, the approach followed for their maintenance would be:
- Analyze the frequency of failure and schedule activities in advance
- Wait for it to fail before carrying out maintenance activities
- Fix it intermittently
Now, the landscape has changed completely. Sensors provide us with accurate data, which can then be analyzed to predict when maintenance would be most economical.
The Industrial Internet of Things
When IoT technology is applied to Industrial processes, it simply becomes Industrial Internet of Things. IIoT integrates major technological advancements, such as Big Data and Machine Learning, and makes use of them to increase productivity and effectiveness. One major idea that has greatly helped IIoT to make headway in the industrial world is the ability of smart machines to capture, analyze and communicate data.
Furthermore, the decreasing costs of technology is allowing more companies to shift from conventional workflows to modern ones, which are more efficient, reliable and connected.
One last thing; it isn’t absolutely necessary to operate a Smart Manufacturing workflow with an internet connection. Smart Manufacturing is about ensuring the data collected from your assets is used so that its impacts spread over the plant floor, so as long as the effect is rippled, the need for internet isn’t mandatory.
The process of finding and identifying qualified system integrators for your automation requires a strong understanding of system integration and paying close attention to what you’re looking for. Your choice of system integrators will only be as good as the candidates you’ve vetted. Therefore, having a broad initial search is essential to finding the right candidate.
System Integrators can essentially be divided into three types:
- Ones that prefer building new systems, delivering them and moving onto the next project, offering limited time post-installation support.
- Ones that build systems and support them for a considerable period of time.
- Ones that specialize in troubleshooting and tailoring of existing systems.
Process control is designed to keep variables within specific boundaries so optimum productivity can be achieved. The primary purpose of process control is to ensure that a process runs at the desired operating conditions, whilst meeting its constraints such as those of safety, environment, and reliability. Process control strategies can be organized into a hierarchy, allowing operators to differentiate vital features from the optional ones.
The use of industrial robots has brought a number of benefits to the workplace, but it has also increased the inherent risk posed to the workers on the plant. Thus, it is the responsibility of the manufacturer to ensure a safe working environment is maintained at all times.
Safety is often viewed as an extra responsibility, something that comes with greater paperwork and overhead. The truth, however, is quite different. Having a safe working environment can actually have benefits such as assurance of regulatory compliance and increased productivity from the factory floor.
For decades machine safety systems in industrial complexes have been associated with individual components such as safety interlocks, electromechanical relays, switches, fencing, enclosures, and so on. But with each passing year, this approach seems to be expiring and lagging with the requirements of today.
Machine safety components are tools that can be used in a certain manner to ensure the safety of a machine. The end-goal of machine safety component usage has shifted from installation of safety components to the successful accomplishment of a goal-set and a strategy. This effectively means that a shift needs to occur from the traditional on/off, go/no/no-go paradigm towards a more functional approach that ensures the workability of all safety-related components in a coherent manner. This system-based approach is now the consensus of several safety experts due to the rapidly changing market demands and evolving technologies.
As technology becomes more prevalent, machines are now being used to build other machines. Most of the robots produced are shipped to various factories where they play a key role in the manufacturing of cars, laptops, and other equipment. It has been reported by Loup Ventures that as more people are swayed towards gadgets, the market for industrial robots is bound to grow over 175 percent over the next decade.
But the dynamic is going to change as well. The driver of this growth won’t consist primarily of industrial arms joining car parts as they have been for decades. Instead, a new generation of robots is taking over that is smarter, more compact, and much more collaborative than the traditional industrial robot. These collaborative robots will account for a large percentage of robots sold in future decades throughout industries. To compare, collaborative robots today only account for 3 percent of industrial robots.
Previous deburring methods required a lot of time and effort to occur. An operator would unload cut parts from a plasma cutting machine, reload a sheet, and then manually grind the burrs and slagging off any edges. Once the operator finished grinding, the ground parts are retrieved by a material handler and carried away. These steps are repeated over and over, with the next batch of cut parts being unloaded, and going through the hand-grinding process. While the operation may sound coordinated to someone who runs a low-volume establishment, the truth is this kind of work is difficult and prone to issues on a wide scale.
The Factory of the Future, better known as Smart Factory, is a paradise of efficiency where words like defect, downtime, and delays exist only within historical facility logs. The facility is powered by a web of interconnected devices operating together in harmony for the satisfaction of clients and customers within time frames, and at a manageable cost.
Such a factory represents the epitome of technological development, illustrating a perfect mix between high-tech tools and skilled workers that complement each other. And while this may still be a fantastic dream, its much closer than one might think.
Many of the technologies that are changing the fabric of the society are also entering other sectors such as material handling. Warehouses and distribution centers are now faced with concepts such as Internet of Things, Artificial Intelligence, and Big Data. These concepts can greatly improve the efficiency of the establishment, but they can possess a steep learning curve.
While still new to the industrial world, these concepts are already finding use within industries that will serve as proving grounds for new material handling technologies. The future of warehouse management is dependent on a handful of key technologies that include Big Data and the Internet of Things.