As technology evolves at an increasingly rapid pace, it poses challenges in the adaptation of systems to incorporate the newest developments. With automation, however, these features can be put to their best use in industrial systems. Automated systems can take a range of forms, through simple conveyor systems to extensive production lines, and can include safety checks, the collection of data and the improving of efficiency through detecting damaged items for example.
The simplest form of automation is a conveyor system. Here, the motor driving the conveyor is automatically controlled through a start-stop circuit. The start-stop circuit is directed by the main controller of the system, usually a programmable logic controller (PLC), which is in turn connected to a set of sensors placed along the conveyor system. These sensors provide signals to the main controller when the conveyor becomes full. The main controller can then issue the start-stop circuit to stop, turning the conveyor off. With an automated conveyor system, therefore employees no longer need to be aware of when to stop the conveyor, and can focus their attention on, for example, the packaging of items.
Further elements of a conveyor line can be automated with additional equipment and programming, for more complex needs. Variable frequency drives (VFDs), for example, can elevate the safety and quality of production on the conveyor system. This might take the form of variation in conveyor movement speed, the detection of damaged products, and the collection of data to maintain long-term workflow efficiency. Robots can also be introduced to handle materials and palletize.
The industrial internet of things (IoT) is the basis of automated manufacturing, and can elevate the long-term efficiency of systems through the collection and storage of data. This data, which might include information on system and shipment flow, can then be used to improve processes into the future. Three notable trends in industrial automation have been emerging in recent years, and are making great strides to increase the efficiency of automated systems. These take the form of serial communication protocols, machine learning methods and robotic technologies, and are introduced here.
IO-Link (IOL) is the newest serial communication protocol used by PLCs in automation. IEC 61131, the recognised communication protocol, allows data on device identification, processes, faults and digital signals, to be transferred rapidly. IOL devices require a 3-wire cable, including wires for power, for IOL data transfer and a neutral. No further custom cables are needed, so installation is simple and they require very little maintenance. Minimal additional setup is also needed in the PLC programming suite, as the IOL uses bi-directional, point-to-point communication.
2. Machine Learning
Machine learning involves providing systems with real-time data which, when processed, they can adapt to. Such information might allow for the increasing efficiency of inventory management, warehousing cost reduction and forecasts for supply and production. As the machine learning’s artificial intelligence continues to provide data to the machine or system, they begin to learn on their own. The greater the system’s ability to recognise and analyse this data, the more efficient the processes become.
There are three predominant types of machine learning methods: supervised machine learning algorithms; unsupervised machine learning algorithms; and reinforcement machine learning algorithms.
Supervised machine learning algorithms involve the comparison of received data against a pre-configured set of training data, and the system is then modified accordingly. Unsupervised machine learning algorithms, conversely, do not work with a training dataset. Here the new information is fed to the AI, which forms conclusions by analysing this data. This method is particularly valuable for identifying high and low values, and so can be beneficial in cost reduction and forecasting processes. Semi-supervised machine learning is a middle ground between supervised and unsupervised machine learning. Here the AI is provided with a limited training dataset, and can reduce the workload and resources needed to compile this information. Relying on even a small amount of data can be extremely beneficial in the training of an AI, more rapidly increasing its analytic capabilities.
Reinforcement machine learning algorithms work slightly differently. Here the AI interacts with an external environment, trialling the submission of data and recording errors. The machine then learns the optimum behaviours for its environment and its performance is maximised.
3. Collaborative Robots (Cobots)
Collaborative robots, cobots, are new on the scene of robotics automation. Their purpose is to work collaboratively with humans, the usual potential for robots to cause injuries to humans having been eliminated. They are made to measure, tailored specifically to the environment they are installed in, and the particular purpose they serve. Cobots, however, work with a reduced workload in comparison to traditional robots, and move more slowly, leading to the requirement for increased overheads. They therefore work best in smaller scale contexts, and in the completion of slow-paced tasks.
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