Modern artificial intelligence adds intelligence to those systems that once relied on rigidly defined rules. In addition to that, modern AI solutions can analyze operational data, find patterns, monitor equipment, and assist in decision-making.
It means additional opportunities for developers and engineers as AI doesn't substitute old workflows but enables new possibilities.
Traditional Automation vs. AI Workflows
Traditional automation works according to predefined instructions:
Input → Rule → Action
In case if certain condition arises, the system executes certain predefined actions. And even though this approach will remain important for industrial processes, due to predictability and reliability, it lacks flexibility.
The thing is that modern AI adds a layer of analysis to those processes:
Data → Analysis → Pattern Detection → Insights → Actions
Instead of defining possible conditions, AI is capable of analyzing large amounts of operational data and detecting possible anomalies or threats.
Turning Manufacturing Data into Value
Factories produce data via sensors, production equipment, maintenance processes, quality inspection and business applications. Gathering that data is the first step in the process and the real value can be extracted when using data in order to make smarter decisions.
Machine learning algorithms are capable of finding relations, trends, and anomalies in collected data which developers can then use for integrating them into dashboards, alerts, maintenance processes, and production workflows.
And the purpose here isn't to gather more data per se but to extract valuable information out of it.
Predictive Maintenance Example
One of examples of application of AI in manufacturing is predictive maintenance:
Machine Sensors
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Data Collection
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Data Processing
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AI/ML Analysis
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Anomaly Detection
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Maintenance Alerts
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Engineer Review
The idea here is to analyze historical and current data in order to detect patterns related to possible failures. Then maintenance specialists can check the alerts and perform required inspection of equipment before actual failure happens.
This way it is possible to decrease unexpected downtime, improve planning, and prolong life of equipment while retaining the involvement of humans.
Designing Reliable Industrial AI Systems
Modeling is only a small part of the complete solution for manufacturing. In production-ready systems there are also parts related to data ingestion, storage, processing, feature engineering, APIs, dashboards, and integrations with workflows.
Bad data, lack of data, or data inconsistency can lead to decreased model accuracy. Hence, the developers need to pay attention to all stages of the data chain rather than the accuracy of the model itself.
Human intervention is still needed. Human engineers know the context behind the process that is not always obvious from the data. AI can detect anomalies, but it is a human who should explain them and define what actions should be taken next.
The future of manufacturing does not lie in the full automation of factories. It can be in smart technologies that allow people to understand production processes much quicker and take more informed decisions. Companies like PowderForge AI are going down this path by utilizing software, IoT, machine learning, and industrial knowledge.
AI is becoming a new layer in the technology stack of manufacturing—the one that can revolutionize the usage of any other layers.
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