How to harmonize humans and new technologies in your assembly line
Despite digitalization, the human factor should not be underestimated in the production. The interaction and collaboration between technologies and people will be more critical than ever before.
A Cobot, or a collaborative robot, is a robot made to operate and perform its tasks together with, and in close proximity to, humans. This is in great contrast to many industrial robots that usually have to be separated from physical contact with humans to prevent accidents.
In the context of intelligent manufacturing, the cognitive robots can perceive information uncertainty, change scheduling management and adjust manufacturing behavior to cope independently with a complex manufacturing problem. Using distributed algorithms for reconfiguration of self-reconfigurable robots will drastically simplify the complexity of configuring robots. The Cobots can work independently and deal with changeable scheduling of a smart factory with connected assembly lines.
Machine intelligence plays an important role in supporting human-machine collaboration. This because machines will be providing assistance with every job, every role, and anything that is done on manufacturing sites where dynamic situations are present, as on advanced assembly lines. Therefore, intelligent human-machine interactions can be implemented within a complex manufacturing environment in order to increase the efficiency of flexible production.
A flexible assembly line is becoming more important in manufacturing where products today are personalized to consumer demands to a much higher degree. Cobots can assist when rebalancing an assembly line, with their ability to adjust their behavior to new situations.
Augmented reality devices for employees, such as tablets, helmets or glasses, help to increase both communication and visualization of contextualized data.
The technique allows repair personnel to see inside the machine that needs repair with the help of digital overlays, or see through walls to the cables and pipes behind in order to know where to drill or cut.
Oftentimes, machine-learning algorithms are used to analyze IoT data and then flag any anomalies or make recommendations to decision-makers. Staff walking through a plant will be able to access unique sets of metadata associated with each machine and the staff can consult an expert if they run into something they are unfamiliar with or if they require a second opinion.