From mechanical arms to automated assembly lines, industrial automation has evolved into a standalone ecosystem on its own. Though governed by a well-equipped workforce on the plant floor, most of the machinery used in industries is largely independent of external human interference. These are the machinery commonly known as collaborative robots—those that require minimum guidance from humans to perform a set of operations.
The manufacturing sector is one of those industrial genres that employ collaborative robots to ensure lesser idle time. By leveraging the techniques of batch processing, collaborative robots are able to streamline most of the industrial operations with a pinch of machine learning and data analytics. The key differentiating factor that separates collaborative robots from traditional assembly line robots is the flexibility with which these machines handle tasks on queue. Consider any large-scale manufacturing plant. Raw materials go through successive stages of machining and manufacturing before the end product comes out of the production or assembly line. Each series of collaborative robots are programmed to perform a particular task. With a bit of coding and timely scheduling of workflows, precision cutting and machining operations are carried out with ease, which would otherwise require intense manual labor.
A recent study proved that assembly line systems such as collaborative robots have increased the efficiency of workers and cut down the net production cost by two to eight Euros per unit of manufacturing. This results in a total savings of 500,000 Euros on an average in a traditional automotive production line.