Machine Learning: The Top 4 Scenarios in Manufacturing
Maintenance is no longer a guessing game; companies now know in advance when a system will fail. But machine learning is set to open up many more opportunities.
What are the benefits of machine learning?
Machine learning best demonstrates its opportunities in any situation where companies are looking to derive new knowledge from past experience. The technology enables digital systems to identify recurring patterns in data and apply the resulting findings to new data streams. In this way, trends and anomalies can be detected – in real time and within the running system. This allows manufacturing companies to use their petabytes of data in a way that benefits their business.
Intelligent monitoring prevents failures:
Sensors are constantly becoming smaller and cheaper. For many companies, this means that it is now economically viable to monitor their entire range of machinery at all times. But to ensure that the thousands of measuring devices can generate insights from the data, it must be analyzed accordingly.
This is where machine learning really comes into its own. Smart applications constantly monitor the inner workings of the machines and at the touch of a button provide a precise picture of their condition.
If values deviate from the normal state, the system warns of a possible malfunction or failure. This means that machines can be repaired before an emergency occurs. In addition, data analysis reveals how a machine should be configured – right down to the smallest components. The result is a reduction in manual reworking and an overall improvement in product quality.
Constant testing ensures quality:
Produce first, check the quality later? That approach could soon be a thing of the past thanks to machine learning solutions. Ideally, conventional testing will be almost completely replaced in the future.
Software can predict the quality of a product right from the early stages of production – with millimeter precision. This makes it possible to detect miniscule cavities and check the porosity of castings during production. However, what is even more interesting is that self-learning algorithms not only report predefined sources of error, but also detect those that were previously unknown.
Pattern recognition optimizes energy management:
The mix of fossil fuels and renewable energy is causing fluctuations in the power grid. This is forcing electricity producers and grid operators to adopt new strategies. Machine learning technologies enable electric utility companies to use historical consumption patterns to predict future demand and price fluctuations in real time.
This makes it possible to more precisely align cost prices with demand – ultimately leading to more cost-efficient operations.
Autonomous vehicles mobilize logistics:
Last but not least, machine learning paves the way for autonomous vehicles, which are set to be a crucial technology for automated logistics and intralogistics. It is only through deep learning that vehicles are able to recognize and correctly interpret their surroundings.
In the future, self-driving transport systems will assume many of the tasks of order pickers. When combined with predictive demand planning, this would enable the replenishment process to be partially automated.
Discover the biggest machine learning trends at the Digital Factory trade fair at HANNOVER MESSE 2018. Learn all about opportunities the technology offers manufacturing companies.
Article Courtesy: www.hannovermesse.de
