As technology advances and better automation systems are made to perform various functions, it reduces the need for human workers in manufacturing. While this technology has been developing rapidly, there are still many flaws in the system. Some people might think that either having a fully automated shop or a staff of able workers would be the optimal choice to run their manufacturing process. Another and quite possibly a better option is to have an automated system run by workers that are intertwined.
While the entire idea of Lean Manufacturing and the benefits that come with it do sound enticing, several system integrators and vendors that have tried to implement it have run into hurdles which have prevented a complete adoption of the framework. From unfinished actions following Kaizen, to constant doubts from the management team, to individual personalities, Lean Manufacturingoften leaves engineers scratching their heads.
Big Data Analytics, Artificial Intelligence and Machine Learning. Some people don’t see a difference in these three terms, and treat them as the same. One must understand that while some concepts of these technologies may overlap, they must be looked upon independently and studied according to their applications. AI suggests automatic generation of insights when applied to Big Data, offering results with little to no effort. The data analytics user experience on the other hand has a user experience that’s different altogether.
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 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.
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.