Modern automotive manufacturing requires harmonious coordination of machinery, tools, materials, vehicles, and production equipment. The appearance of an anomaly in one piece of equipment or a delay in material delivery can cause production downtime.
The issue is not only in collecting the data generated by connected systems. It is also in presenting this information in a way that is usable for day-to-day manufacturing.
This is where Artificial Intelligence of Things (AIoT) comes in.
AIoT in Automotive Manufacturing Explained
AIoT is a combination of the Internet of Things (IoT) and artificial intelligence (AI). IoT sensors acquire and process data from machinery, equipment, vehicles, and other connected devices. In turn, AI analyzes this information to identify patterns, determine outliers, and facilitate decision-making.
For automotive manufacturers, connected devices generate and collect vast amounts of data about production equipment and machinery. Herewith, based on the information received, AIoT can identify patterns and predict future events.
AIoT in Automotive Manufacturing: Use Cases and Examples
Automotive manufacturing is a complex process that involves many stages, from parts production to whole-car assembly. It also involves numerous tools and equipment, which requires their proper tracking and management. Moreover, during the manufacturing process, various machines and vehicles are transferred from one stage to another or from one workstation to another. These characteristics challenge the tracking of specific pieces of equipment and the collection of information about their location, state, and operating time.
Different technologies allow for the processing of this information:
• RFID (Radio-Frequency Identification): allows for storing data about assets, their inventory, and location.
• Bluetooth Low Energy (BLE): enables Bluetooth positioning.
• Ultra-Wide Band (UWB): allows for ultra-wideband indoor positioning systems.
• Real-Time Location Systems (RTLS): help locate tagged items.
For instance, applying these technologies makes it possible to get data about the location of tools in real-time and know exactly where they are needed. In addition, their use can reduce the search time for equipment at a particular workstation. At the same time, the choice of technology depends on the peculiarities of the facility’s infrastructure and the manufacturer’s requirements.
Another use case of AIoT application in automotive manufacturing is equipping machines and devices with sensors to monitor their technical state and prevent failures. Stopping the equipment requires time and increases downtime, which reduces the overall performance of the production. Preventive maintenance is not always appropriate since it is expensive and time-consuming. Thus, AIoT monitoring systems that use the data from connected devices can proactively control the state of machinery and only alert maintenance engineers if a problem is detected.
For example, if vibration of a machine is higher than usual, this does not mean that the machine is about to fail. However, in combination with data about the equipment’s operation history, this information can help find out if there are any anomalies. This is a great way to optimize maintenance and make decisions based on factual information.
Connecting Manufacturing Data to Business Software for Greater Efficiency
The described cases demonstrate that AIoT can help in solving many of the industry’s problems. However, manufacturers should understand that the effectiveness of connected devices is directly linked to the quality of data that comes from them. Data collected by sensors is of great importance since it is the basis for subsequent analysis and applying AI algorithms. Information about the technical state of manufacturing equipment will allow its further effective use and prevent failures.
However, manufacturers should not stop at collecting and analyzing this data. To do the most useful thing with it, companies should connect their operations and business processes with this information. Thus, IoT data can be connected to Enterprise Resource Planning (ERP) systems and Manufacturing Execution Systems (MES) to optimize production processes.
Such integration will allow automakers to take their operations to a new level by introducing new capabilities:
• Production analytics and monitoring;
• Equipment and tools management;
• Component tracking and visibility;
• Manufacturing resource coordination;
• Manufacturing process monitoring.
For companies that want to get started with connected devices in automotive manufacturing, OEMNex AI
is a great place to start. This solution is an AIoT reference architecture for the automotive industry that allows you to connect your equipment and sensors and start analyzing the data that comes from them. However, proper cybersecurity, data analytics, and IT infrastructure are also critical success factors for building a successful AIoT ecosystem.
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