Thales Alenia Space

In today's fast-paced manufacturing industry, the ability to gather, analyze and act upon real-time data is critical to optimize production and improve efficiencies. With the emergence of Industrial IoT (IIoT), manufacturing companies are adopting smart technologies to connect and automate production processes, improve operational efficiencies, and enhance product quality. In this article, we will explore an IIoT implementation on a Surface Mount Technology (SMT) line that enables the monitoring of machines and work order tracking.

Introduction

SMT technology has revolutionized the way printed circuit boards (PCBs) are assembled by automating the process of mounting electronic components on the board. The SMT line is an assembly line that is comprised of several machines such as screen printers, pick-and-place machines, reflow ovens, and inspection machines, to name a few. To optimize the production of the SMT line, it is essential to monitor the machines' status and track the work orders' progress. This is where IIoT implementation comes in.

IIoT Implementation on SMT Line

To implement IIoT on an SMT line, the first step is to connect the machines to the network. This can be done by direct connections or by retrofitting and installing sensors on the machines, which can collect data such as machine temperature, vibration, and energy consumption. The data is then transmitted to an IOT platform server that can store and process the data. By collecting real-time data, manufacturers can monitor the machines and ensure that they are operating within good parameters. Furthermore, by using predictive analytics, manufacturers can detect anomalies in the data and predict when a machine is likely to fail. This enables manufacturers to take preventive action to avoid downtime and prevent production loss.

“Manufacturers can use IIoT to reduce downtime, improve quality, and increase efficiency”

Once the data is collected, it needs to be analyzed to extract meaningful insights. Several data analysis techniques can be used in a big data environment.

These include:

1. Predictive Analytics: 

Predictive analytics is a technique that uses historical data to predict future events or trends. In the context of electronic manufacturing, predictive analytics can be used to predict equipment failure, estimate production output, and estimate order dispatch.

2. Machine Learning: 

Machine learning is a type of artificial intelligence that allows machines to learn from data and improve their performance. In the context of electronic manufacturing, machine learning can be used to identify patterns in data, detect anomalies, and optimize production processes.

3. Data Mining:

Data mining is the process of extracting useful information from large datasets. In the context of electronic manufacturing, data mining can be used to identify correlations between variables, predict equipment failure, and optimize production processes.

4. Process Mining: 

Process mining is a technique that allows manufacturers to visualize and analyze their production processes. In the context of electronic manufacturing, process mining can be used to identify bottlenecks in the process, reduce cycle time, and improve quality.

To monitor the line on the SMT line in an ESD-safe environment like in Thales Alenia Space production lines, QRcode tags can be used to identify each PCB and the work order that it is associated with; for less stringent ESD requirements, RFID tags offer excellent automation capabilities. As the PCB moves through the SMT line, the reader can detect the tag and log its location and status. This enables manufacturers to track the progress of each work order and identify bottlenecks in the process. By using machine learning algorithms, manufacturers can predict the estimated completion time for each work order and adjust the production schedule accordingly.

Benefits of IIoT Implementation on SMT Line

There are several benefits of implementing IIoT on an SMT line, including:

1. Real-Time Monitoring: with IIoT, manufacturers can monitor the machines' status in real time, enabling them to detect issues before they result in production loss.

2. Predictive Maintenance: by using predictive analytics, manufacturers can predict when a machine is likely to fail and take preventive action, avoiding unplanned downtime.

3. Work Order Tracking: by tracking the progress of each work order, manufacturers can identify bottlenecks in the process, adjust the production schedule and implement a warning and notification system.

4. Improved Quality: by monitoring the machines' performance and detecting anomalies, manufacturers can ensure that the PCBs are assembled to a high standard.

5. Increased Efficiency: by optimizing the production process, manufacturers can increase efficiency, reducing production time and costs.

Conclusion

In conclusion, an IIoT implementation on an SMT line can significantly improve manufacturing processes' efficiency and performance. By connecting the machines to the network and tracking the work orders' progress, manufacturers can collect real-time data and use predictive analytics to detect issues and predict completion times. By using IIoT, manufacturers can reduce downtime, improve quality, and increase efficiency. The large amount of data collected allows investing in research and development with the chance of identifying unexpected patterns and developing algorithms that will lead to the prediction of failures and an improvement of the global process.

As such, it is a valuable investment for any manufacturing company looking to stay ahead of the competition.