All significant optimization processes on production lines are becoming more and more inaccessible. Leveraging more intelligent automation in the workplace offers an innovative solution, but the whole process starts with data. With lots of data.
Although many technologies were used to create Forpheus, a robot with human–machine communication, data is critical in building “smarter” machines. Data collection, data–driven modeling, applying models, and finally, using the device for Machine Learning (ML) and evaluating models so that the machine can automatically regulate its behavior.
The first step is to collect data from individual machines or preferably from the entire production line. This approach generates large amounts of data in what we call big data. Thanks to today’s processing power and cloud storage, the processing of all this data can be done relatively efficiently and economically. Clean data is the basis for more efficient processing and the best results. Furthermore, displaying this collected data in an easy–to–understand way on a screen can help operators identify anomalies in the process.
Data Analysis Helps Operators
By displaying process operation data in this way, productivity gains of 20% to 30% can already be achieved. However, with the increase in data, people cannot interpret these data or understand repeated examples. Computers using powerful data analysis software are more precise and tireless than humans. With more data and more advanced or “smarter” analysis, interpretations and conclusions become more comprehensive and accurate. For example, instead of just identifying problems, the system can pinpoint the location of the situation in–line and what needs to be done to fix the problem. As a result, the operator’s task is simplified and line efficiency optimized.
The increase in the amount of data has made data management necessary. The resulting repetitive samples are then transferred back to the factory and applied in real-time by the machine.
Data use to Increase Automation
Intelligent systems can identify and flag the problem or potential problem and automatically adapt production line parts to compensate for any shortcomings during troubleshooting. Of course, all these processes are applied with safe parameters, resulting in higher production efficiency.
Let’s consider this situation at the level of a single machine. Intelligent machines with data analysis capabilities can optimize their behavior for all current conditions, as they know how to operate normally. They monitor their performance and make sure that it aligns with expected behavior. When it detects a defect or deviation from the standard pattern, the machine notifies the entire system of the problem and compensates, if possible, by correcting the way it works. From a system point of view, all line–wide deviations must be balanced to ensure consistent operation between machines.
True Smart Factory Automation
Data complexity is one element that makes the transition to the intelligent factory a considerable challenge.
Now they apply what they have learned to their systems and products to bring the benefits of intelligent automation to our customers. In addition, they know where weak links will arise by conducting astute automation experiments with many select customers.
Human – Machine Interaction
Intelligent automation built on data collection and analysis can be moved to the human and machine interaction axis.
In the light of this information, we can say that intelligent machines can also be used to provide education to people.
Adaptable to all machine applications and is ideal for the manufacturing industry. For example, intelligent robots can assess the operator’s level of expertise when communicating with robot–assisted systems or other robots, such as heavy lifting, where the robot lifts the weight. Still, fine–tuning of positioning is made by the operator. In this case, the robot uses its assessment to assist with operator training or make its tasks more accessible by offering more guidance.
Intelligent automation can make working more enjoyable and provide gains such as increased productivity moreover, not only with robots but with all machines. Moreover, by getting to know the worker on the assembly line, the robots can offer helpful tips and tricks about their tasks and establish personal contact.
We would not talk about today’s integrated and interactive machines without traditional engineering. However, to make these machines more intelligent, we need to add a touch of data science engineering.