Modern vehicle manufacturing is becoming increasingly data centric. A manufacturing plant can generate information from machines, sensors, cameras, PLCs, production systems, and connected equipment. The challenge is in using all of that data to generate information that can be used to make useful decisions.
This is where AI and IoT can fit together.
From Raw Data to Useful Information
Industrial IoT devices and sensors can generate information such as machine status, temperatures, vibrations, production counters, and equipment performance.
An oversimplified view of the architecture would be something like Sensors → Connectivity → Data Processing → AI/ML → Insights → Action.
The data that is mined from the factory can be used to process and analyze patterns that are not evident at first.
Depending on the use case, various aspects of the production process can be monitored and improved with computer vision, predictive maintenance, and production planning.
The quality of data is paramount to be able to obtain useful information and insights. The AI model cannot do magic with garbage data, which is why it is so important to analyze the data at hand and see if it is viable and relevant to the task.
Predictive Maintenance Use Case
One practical application of AI in manufacturing is predictive maintenance.
It is common for industrial equipment to require regular maintenance or even have 24/7 monitoring to ensure that production is not interrupted. With connected equipment, manufacturers can monitor the performance of their equipment and analyze patterns.
Irregularities in the data provided by connected sensors can be an early warning sign of an impending malfunction.
It is also important to note that predictive maintenance is not about replacing human maintenance engineers. Rather, it is about providing them with additional information that they might not have had before the predictive maintenance solution was deployed.
Computer Vision Use Case
Another compelling application of AI in manufacturing is computer vision.
Computer vision can be used to inspect manufactured parts for quality control. Cameras can capture images of the produced components, and ML models can analyze those images to detect certain visual patterns.
Depending on the use case, the computer vision application can be used to identify manufacturing defects or speed up the inspection process.
From a development perspective, computer vision applications also present additional challenges such as image resolution, data labeling, model accuracy, false positives, and constantly changing manufacturing conditions.
The Importance of IoT Architecture
AI is only as good as the data that is provided to it.
Industrial manufacturing facilities are complex systems that involve numerous PLCs, SCADA systems, MES, robots, and other equipment. It is important to consider the data pipeline for an AI application in manufacturing. For example, different pieces of equipment might use different communication protocols. The data from these sources would need to be normalized in a database before being processed by an analytics or machine learning application.
This is why building the right architecture is often more difficult than developing the AI solution itself.
Edge Computing in Industrial Environments
Edge computing is another important consideration for industrial IoT applications.
Edge devices can process data at the source, which means that manufacturers can offload some processing tasks from centralized cloud servers.
A lot of manufacturers are deploying edge computing solutions to reduce latency, bandwidth consumption, and downtime. Edge applications can be especially useful for manufacturers that need to process large amounts of data in real-time.
The right approach will always depend on a specific use case and the available infrastructure.
The Road Ahead
The future of automotive manufacturing will not be defined by a single technology. AI, IoT, robotics, industrial connectivity, edge computing, computer vision, and data analytics will all play a role in the evolution of vehicle manufacturing. The primary challenge for engineers and developers will be to combine these disciplines and build systems that use multiple technologies.
For developers, the question should not be "where can we use AI?"
the real question is "what manufacturing challenge are we trying to solve, and what data do we have available to solve it?"
This is the central challenge of connected manufacturing and why computer vision is such a compelling application.
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