Industrial Robotics is a well-established field within the manufacturing sector for the past thirty years, employed for a variety of tasks like stacking, sorting, casting, welding, etc. Robots are involved within industries as they are able to perform hazardous, repetitive tasks more accurately and economically than humans. But still, in reality robots have a long way to go before they can be assigned intelligent tasks that require reasoning.
Robots have served as the backbone of the manufacturing industry for decades, replacing humans in repetitive, laborious and time-consuming tasks. Advancements in engineering introduced robots to Artificial Intelligence (AI) and soon, the idea of collaborative robots took over. The scope of having a machine understand and work with you was promising, triggering developers to vigorously work on the idea. Simultaneous advancements in technology and boom in processing capabilities, turned the entire idea into practicality.
Robotic assembly applications have ballooned in recent years, both in terms of numbers and complexity. Industrial robots today are supplemented by an array of technologies that help them respond better to unpredictable situations, while increasing their range of applicability. Smart Camera Machine Vision is one of these technologies, which, when combined with Industrial Robots holds the prospects of doing wonders.
With the massive inflow of information into our lives, and the speed with which technology is advancing, we sometimes tend to blur the lines between fundamental concepts. Robotics and Artificial intelligence are two concepts that are often mixed, and thought of as being part of each other, while in reality they serve different purposes.
Customer satisfaction has become the top-most priority for businesses involved in sales, especially with the increased involvement of social media and e-commerce services. Customers are always looking for personalized options, greater price options and easy delivery methods. Therefore, the ability of a business to meet these stringent requirements depends on how capable their warehouses are.
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.
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.
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.