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Case Study for Improving the PCB Print Process Using Factory Data

Case Study for Improving the PCB Print Process Using Factory Data
The analytics of big data could help us to understand that process better. We can buy better equipment and select the parameters more carefully.
Analysis Lab


Authored By:

Martin Franke, Siemens AG, Corporate Technology, Research in Energy and Electronics, and
Izik Avidan, Director of Customer Success, Valor-Mentor Graphics


The competitive pressures that electronics companies experience today are exceptional, even for an industry accustomed to a relentlessly high rate of innovation. Manufacturers are challenged to keep up with customization (lower volume and higher mix) and globalization. Material shortages and longer lead times means it is harder to maintain inventory. Compliance is also a challenge as more requirements are coming from customers, as well as new demands for example with electronic vehicles. New manufacturing best practices are crucial to meet these challenges.

Data collected from shop-floor drives the MES operation and the material flow. However, the value to this data does not stop there. Digitalization of the operation enables analysis and understanding of what is happening in the factory, why it is happening, and how to improve it. Although the amount of data generated by manufacturing operations is increasing exponentially, only a small portion of it actually gets collected, and an even smaller portion is analyzed in the electronics industry. In the use case presented here, we will demonstrate how manufacturers can benefit from collecting this data and applying analytics.

The biggest challenge with collecting data is turning big data into smart data that provides insight or foresight, can be understood as the point of consumption, and is immediately actionable on the shop-floor. There are three areas in which big data can be useful today in production to take immediate advantage from the amount of data.

The first is to improve our understanding of the process, so that we understand better the physical effects of what we are doing on the process. For example, we know that there might be an influence of the temperature at the reflow soldering in a certain extent, but we do not know if the temperature varies about three to four Kelvin in the reflow soldering process. We do not know whether this already has an impact on the AOI (automated optical inspection) quality or on the soldering quality. The analytics of big data could help us to understand that process better. If we understand our process better, we can buy better equipment and select the parameters more carefully. This could generate ROI in the long-term.

The second area is to improve quality. For example, we could better understand the impact of the incoming quality of our materials, which can reduce our scrap rate on a longer run. In this paper, we will present an example of how to use the data to improve quality in the printing process.

The third area is the most profitable: using the data to improve throughput. Once we have the data in-hand and are able to read the relevant information out of the data, we can judge from the overall quality on the line that the quality in all the single processes is so good, we can reduce the test level without jeopardizing overall quality.


This experiment is one of many where we can demonstrate real-world ROI for the data, we are gathering off the shop-floor. By using the data in a systematic way through a connected factory network and analyzing with the business intelligence tools available, manufacturers can take advantage of their data to improve their product and processes today.

The analysis also shows the potential of automated data extraction and preparation, thus giving an process engineer more time caring about evaluation of the data, than preparing it for the evaluation.

Initially Published in the SMTA Proceedings


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