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The fundamental element to making any machine ‘smarter’ is data. Data collection, data-driven modelling, applying the models, and finally, the machine using and evaluating models to automatically adjust its own behaviour through machine learning.
The first step is collecting data, from individual machines or preferably from an entire production line. This can result in significant amounts of data – known as big data. Up to a point, analysing all this data can be handled effectively and cheaply using today’s processing power and Cloud storage. Clean data is essential to enable more efficient processing and the best results. Simply by displaying this collected information on a screen, in an easy-to understand way, can help operators identify and respond to anomalies in the process.
Data analysis helps operators
Displaying process operation data in this way can already deliver 20-30% efficiency increases. However, as the amount of data increases, humans are less able to interpret it or perceive patterns. By incorporating large data analysis software, computers offer a more accurate and tireless tool to support humans. These tools can identify irregularities in performance data and flag potential issues to the operator.
With more data and more advanced or ‘smarter’ analysis, the insights and results become more comprehensive and accurate. For example, instead of just identifying an issue, the system can locate exactly where the problem is in the line and what needs to be done to fix it. The operator’s job is made easier and line efficiency is further optimised.
As the amount of data increases, data management also becomes important. Collected data is often taken offline for advanced processing and pattern recognition. Then, the resulting patterns are transferred back to the factory to be implemented in real-time by the machine.
Using data to increase automation
We can take this automation a step further. Smart systems could identify an issue or potential issue, flag it, and then automatically adapt parts of the production line to compensate for any shortfall whilst the problem was being fixed – all within safe operating parameters. Once again, this results in even better production efficiency.
Let us consider this at the level of an individual machine. Smart machines – equipped with data analysis capabilities – can optimise their behaviour for any given situation because they ‘know’ how they are supposed to work normally. They monitor their own performance, ensuring it matches expected behaviour. If a defect or divergence from a standard pattern occurs, the machine reports the issue to the entire system and if possible, compensates for the issue by amending its operation. From a system viewpoint, any alterations must be balanced throughout the line to ensure consistent operation between machines.
Real smart factory automation
Complexity of data is one thing that makes moving to a smart factory a major challenge. We are implementing these smarter systems into our own processes, allowing us to investigate requirements and develop best practices – and there is plenty to learn. When we started looking at our own processes about two years ago, our very first data scientist spent 80% of his time just cleaning up the data.
Companies who have taken this journey can apply what they have learned to their systems and products to bring the benefits of smart automation to customers, carrying out experiments in smart automation and learning where bottlenecks occur. In the end, only by performing this research in real factories can the real value be uncovered.
Building on data collection and analysis, smart automation can be extended into the realm of human-machine interaction. Nowadays robots have the capabilities to become budding ping-pong champions – as just one example – capable of observing the motion of an opponent facing it on the other side of the table, along with cameras that watch the ball’s movement.
Analysing data from sensors, it can calculate movement very precisely and quickly, to anticipate how the opponent will hit the ball and its trajectory. How difficult or easily they return the ball gives a clue as to one way this smart machine can be used to general advantage. By being able to assess how its opponent plays, it can determine their skill level. Robots can modify their own playing level to get the best from an opponent, if playing at a slightly better level, the opponent will have a challenging game without becoming frustrated. Hence, smart machines can also be used to train people.
This training aspect can be applied to all kinds of machine applications and is ideal for the manufacturing industry. Smart robots can assess the operator’s level of expertise when interacting either with the robots themselves or with the systems being assisted by the robots – such as heavy lifting where the robot takes the weight of the object, but the operator makes fine adjustments for placement. In this case, the robot uses its appraisal of the operator’s ability to help train them or make the task easier by giving them more guidance.
Besides the rewards in improved efficiency, smart automation can make it enjoyable to work with robots and all machines. They can recognise who is at the assembly line and provide personalised interactions like giving meaningful hints and tips on how to do the job. This boosts productivity and efficiency, all through the role that data plays in the smart factory.
Without traditional engineering, there would be no integrated and interactive machines today. To make them intelligent and harness their full potential, we just need to add a touch of data science.
About the author:
Tim Foreman is the European R&D Manager at Omron, where he started back in 1993 as a Software Engineer. Tim has a PhD and MSC in Physics from Utrecht University and has held a variety of positions from Project Leader, and Group Leader to Development Manager. In 2007 he was appointed to his current position as European R&D Manager.
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